AN ABSTRACT OF THE DISSERTATION OF. Title: Utilizing Wireless-based Data Collection Units for Automated Vehicle. Abstract approved: David S.

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2 AN ABSTRACT OF THE DISSERTATION OF Amirali Saeedi for the degree of Doctor of Philosophy in Industrial Engineering presented on February 22, Title: Utilizing Wireless-based Data Collection Units for Automated Vehicle Movement Data Collection Abstract approved: David S. Kim There are many different types of automatic data collection technologies that have been used in transportation system applications such as pneumatic tubes, radar, video cameras, inductive loops detectors, wireless toll tags, and global positioning systems (GPS). Nevertheless, there are still multiple examples of important and helpful transportation system data that still require manual data collection. In this research, the automatic transportation system data collection capabilities are expanded by enhancements in the use of wireless communications technology. In recent years, smartphones and electronic peripherals with wireless communication capabilities have become very popular. Many of these electronic devices include a Bluetooth or Wi-Fi wireless radio, whose presence in a vehicle can be used as a vehicle identifier. With wireless on-board devices available now and in the future, this research explores how roadside data collection units (DCUs) communicating with on-board devices can be used for the automated data collection of important road system data such as intersection performance data.

3 To this end, two approaches for wirelessly collecting vehicle movement over a short road segment were explored. One approach utilized the collection and triangulation of wireless signal strength data, and demonstrated the capabilities and limitations of this approach. The second approach focused on developing methods for utilizing wireless signal strength data for vehicle point detection and identification. The vehicle point detection methods developed were applied to collect travel time data over signalized arterial roads, and to collect intersection delay data for a three way stop controlled intersection. The results from these case studies indicate a significant advantage in the proposed data collection system over the existing data collection approaches presented in the literature.

4 Copyright by Amirali Saeedi February 22, 2013 All Rights Reserved

5 Utilizing Wireless-based Data Collection Units for Automated Vehicle Movement Data Collection by Amirali Saeedi A DISSERTATION submitted to Oregon State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy Presented February 22, 2013 Commencement June 2013

6 Doctor of Philosophy dissertation of Amirali Saeedi presented on February 22, APPROVED: Major Professor, representing Industrial Engineering Head of the School of Mechanical, Industrial, and Manufacturing Engineering Dean of the Graduate School I understand that my dissertation will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my dissertation to any reader upon request. Amirali Saeedi, Author

7 ACKNOWLEDGEMENTS I would especially like to thank my advisors, Dr. David S. Kim and Dr. J. David Porter, for guiding me through this research with excellent patience and for the given encouragements. I would also like to thank my committee members Dr. Toni Doolen, Dr. Karen Dixon, Dr. David Hurwitz, and Dr. Scott Leavengood for their suggestions and for reviewing this work. This work has received major benefits from discussions with other graduate students in the Industrial Engineering Department at OSU, among them Dr. Sejoon Park.

8 TABLE OF CONTENTS Page 1 INTRODUCTION RESEARCH MOTIVATION RESEARCH OBJECTIVES RESEARCH CHALLENGES CONNECTION TO VEHICLE-TO-INFRASTRUCTURE RESEARCH RESEARCH CONTRIBUTION THESIS ORGANIZATION LITERATURE REVIEW TRAFFIC MEASURES OF EFFECTIVENESS INTERSECTION PERFORMANCE MEASURES AUTOMATIC DATA COLLECTION TECHNOLOGIES EXISTING TRAVEL DATA COLLECTION SYSTEMS BLUETOOTH TECHNOLOGY BLUETOOTH-BASED DATA COLLECTION SYSTEMS BLUETOOTH SIGNAL DISTANCE-SIGNAL STRENGTH RELATIONSHIP CONNECTED VEHICLE RESEARCH INITIATIVE METHODOLOGY BLUETOOTH TECHNOLOGY TRILATERATION APPROACH TO ESTIMATE VEHICLE MOVEMENT BLUETOOTH SIGNAL STRENGTH ACQUISITION ESTIMATING DISTANCE FROM RSSI PARTICLE SWARM OPTIMIZATION SIGNAL LOCALIZATION APPROACHES... 46

9 TABLE OF CONTENTS (Continued) Page TRILATERATION STUDY TO DEVELP A SIGNAL PROPAGATION MODEL FITTING THE ENVIRONMENT POWER DECAY FACTOR ACCURACY ASSESSMENT CONCLUSIONS AND LIMITATIONS VEHICLE POINT DETECTION SYSTEM VEHICULAR MOVEMENTS NEAR A DATA COLLECTION UNIT AND THE RSSI-DISTANCE RELATIONSHIP POINT DETECTION ALGORITHM VALIDATION EXPERIMENTS APPLICATION CASE STUDIES TRAVEL TIME STUDY GENERATION OF TRAVEL TIME SAMPLES USING MAC ADDRESS DATA TRAVEL TIME SAMPLE GENERATION SUPPLEMENTED WITH RSSI DATA INTERSECTION PERFORMANCE INTERSECTION TEST SETUP TEST RESULTS AND CONCLUSIONS CONCLUSIONS BIBLIOGRAPHY APPENDICES APPENDIX A. BLUETOOTH INQUIRY PROCEDURE APPENDIX B. TRILATERATION ALGORITHM CODE APPENDIX C. PARTICLE SWARM OPTIMIZATION ALGORITHM IN VISUAL BASIC

10 LIST OF FIGURES Figure Page 1.1. A Set of Three Wireless DCUs Installed at a Signalized Intersection Schematic Representation of Bluetooth DCU Detection Range Intersection of Three Circles at a Single Point Trilateration Test Experiment Setup Scanners Arrangement in an L Shape Error Comparisons for Estimated Distances Using the Developed Signal Propagation Model for Both Test Cellphones Highlighted Example of a Through Pass RSSI and Distance Sample Data Highlighted Example of a Stop Far From Stop Line RSSI and Distance Sample Data Highlighted Example of a Stop Near Stop Line RSSI and Distance Sample Data Multiple detections within the DCU's detection zone Schematic representation of multiple detections in a single trip forming a group RSSI values over time for two devices in a probe vehicle arriving and waiting at an intersection DCU installation on Camp Adair Road, Benton County Histogram of the difference between the time the probe vehicle passed the DCU on Camp Adair Road and the MAC address record with the highest RSSI DCU Location on Wallace Road, Salem, Oregon Histogram of the difference between the time the probe vehicle passed the DCU on Wallace Road and the MAC address record with the highest RSSI Southernmost DCU locations on Highway 99W, Tigard Oregon Southernmost DCU locations on Highway 99W, Tigard Oregon... 75

11 LIST OF FIGURES (Continued) Figure Page Histogram of the difference between the time the probe vehicle passed DCU 12 on Highway 99W and the MAC address record with the highest RSSI Histogram of accuracy of the time stamp before the RSSI values rapidly decrease Location of Bluetooth DCUs on Highway 99W (Tigard, OR) Test Setup Utilized in the Intersection Control Delay Experiment Test Setup Utilized in the Intersection Control Delay Experiment... 91

12 LIST OF TABLES Table Page 2.1. Selected arterial performance measures Most Common Automated Data Collection Technologies Used in Transportation Applications Bluetooth Classifications Random locations selected for Test Cell Phone Signal Propagation Model Accuracy Test Results Sample of MAC Address Data from an Installed Bluetooth DCU Cell Phone 1 Sample Probe Vehicle Test Results for the Road Segment between Johnson St and the 217 NB Ramp Average Absolute Travel Time Sample Errors in Seconds for Different Travel Time Calculation Approaches The Volume Of Travel Time Samples Generated Compared To Traffic Volume Summary Results from the Intersection Delay Study A.1. Discovery probability of Bluetooth devices in relation to inquiry time (Nicolai & Kenn, 2007)

13 DEDICATION To my parents for their unconditional love and endless patience.

14 1 INTRODUCTION There are many different types of automatic data collection technologies that have been used in transportation system applications such as pneumatic tubes, radar, video cameras, inductive loops detectors, wireless toll tags, and global positioning system (GPS). Nevertheless, there are many types of transportation system data that still require manual data collection. In this research, the capabilities of automatic transportation system data collection are expanded by enhancements in the use of wireless communications technology. The introduction to this research will begin with an overview of the motivating transportation system data collection application for which automation will be beneficial. This will be followed by a specification of the research objectives and an overview of the research approach. This introductory chapter will conclude with a discussion of the research contributions presented and the organization of this thesis. 1.1 RESEARCH MOTIVATION Intersections in urban and rural highway networks have a significant role on the operation and performance of the traffic system since the performance of an intersection controls the performance of the roads meeting at that intersection. Intersections can be bottlenecks in a road network with respect to throughput affecting the capacity of the road system and its efficiency (as measured by travel

15 2 times), which may result in an impact on traffic congestion and safety. The effect of intersections on road network performance has been studied in previous research (Ibrahim et al., 2008; Sharma & Swami, 2010). Accordingly, there has been research directed at intersection design and control to increase capacity and provide high levels of safety (Pickrell & Neumann, 2001). While the importance of intersections on the performance of the traffic system and on safety has been recognized, the acquisition of intersection performance data still relies on manual data collection methods. Although more advanced automated methods are being tested, but they are not common practice yet. There exist a number of different intersection performance measures. However, the 2010 Highway Capacity Manual (Transportation Research Board, 2010) designates average control delay as the primary performance measure for signalized intersections. Control delay is defined as the total delay a vehicle experiences due to interaction with the traffic control device at the intersection. It includes delay due to deceleration, stopping, and acceleration. There are no currently available, low-cost automated data collection methods that can be utilized to collect data to estimate control delay. In addition to intersections, there are a variety of road segments that are areas of interest for engineers where manual data collection is required. These road segments usually have some performance, functional, or geometric characteristics that require engineers to conduct on-site data collection studies to investigate the cause for existing issues, or to predict vulnerable situations (e.g., for future developments).

16 3 Generally, the criteria listed below can help to identify areas of interest for transportation engineers: - Performance criteria: Highly congested routes, intersections with excessive delays, and corridors with high accident rates are main areas of interest. For these situations, road segment data can help engineers to identify solutions to the problems. - Functional criteria: Some road segments may have been assigned a unique functionality that distinguishes them from other road segments. Airport roads, industrial roads, interstate highways, and roads located on evacuation plans are main examples of this category (Kirby & Pickett, 2001). Road segment data can be used to determine road segment performance for specific functions. - Geometric criteria: Sometimes a physical factor can change or affect (usually for a short period of time) the normal functionality of a road segment. Examples could be construction zones, accident sites, or roads adjacent to an attraction (high demand) center (U.S. Department of Transportation, 2008). 1.2 RESEARCH OBJECTIVES In recent years, smartphones and electronic peripherals with wireless communication capabilities have become very popular. Many of these electronic devices include a Bluetooth or Wi-Fi wireless radio, whose presence in a vehicle can be used as a vehicle identifier. In the future, it is envisioned that vehicles will be equipped with

17 4 on-board Dedicated Short Range Communication (DSRC) devices. With wireless onboard devices available now and in the future, this research will explore how roadside data collection units (DCUs) communicating with on-board devices can be used to address the lack of automated data collection for important road system components such as intersections. The objective of this research is to explore the capabilities of wireless radio frequency technology with respect to automated vehicle data collection. An exploration of collecting vehicle movement data utilizing triangulation of wireless signal data is conducted, and demonstrates the capabilities and limitations of this approach. The research then focuses on developing the knowledge of how wireless radio frequency technology can be utilized to provide automated vehicle point detection and identification capability. In this research, vehicle point detection refers to the determination of the time a traveling vehicle just crosses a specific line crossing a road. The wireless vehicle point detection and identification capability can then be used to collect road segment data from which performance measures such as control delay (and other measures) can be estimated. The purpose of this study is to utilize wireless technologies to collect vehicle movement data and the focus is not on obtaining performance measures. However to validate the wireless data collection system proposed in this study, the application of the proposed system in obtaining travel time data over signalized arterial roads and intersection delay were demonstrated through two case studies. Estimating these measures assumes that a

18 5 sufficient number of travelling vehicles are equipped or contain the wireless technology that can be wirelessly detected and used as a vehicle identifier. For the intersection control delay application, multiple wireless DCUs can be placed at known distances along a road both before and after an intersection, as depicted in Figure 1.1. By using multiple DCUs, data on vehicle position over time may be collected for those vehicles containing detectable wireless devices. This data can be used to compute performance measures such as travel times through areas of interest, and can be used to show how congestion builds over time in specific areas due to non-recurring events. DCU 1 DCU 2 DCU 3 Figure 1.1. A Set of Three Wireless DCUs Installed at a Signalized Intersection

19 6 Compared to other technologies that have both vehicle point detection and identification capabilities (e.g., video and GPS technology), wireless data collection units are inexpensive and may be deployed as portable or permanently installed DCUs. Permanently installed DCUs may also be connected to central traffic data management systems through an existing network infrastructure or through wireless technologies. During this study, the concept has been tested through both temporary and permanent deployment of the wireless-based data collection units. 1.3 RESEARCH CHALLENGES One characteristic of wireless data collection systems is that they typically have a large detection range. At a roadside DCU, a vehicle containing a discoverable Bluetooth device is often detected multiple times as it travels within the antenna coverage area of the DCU, as depicted in Figure 1.2. Each detection of the same device in a vehicle will have a different time stamp. When utilized for travel time data collection, this characteristic of vehicle data collected using Bluetooth technology has been widely acknowledged by several researchers (Van Boxel et al., 2011; Tsubota et al., 2011) as the main cause for travel time sample inaccuracy. Due to these spatial errors associated with data collected by Bluetooth DCUs, researchers believe that Bluetooth wireless data collection is not precise enough for travel time data collection on shorter arterial segments (Saito & Forbush, 2011; Wasson et al., 2008). The spatial errors associated with wireless data collection is the main challenge addressed in this research.

20 7 Figure 1.2. Schematic Representation of Bluetooth DCU Detection Range To develop the point detection capability with wireless DCUs, received signal strength indicator (RSSI) data is utilized. Within the Bluetooth technology communication protocol, RSSI data are available at the same time devices are detected. In other wireless platforms such as Wi-Fi, ZigBee, and DSRC, the RSSI data are also available both during the inquiry process and once a connection (i.e, pairing) of devices has been established. The key feature of RSSI data that helps develop point detection capability is the correlation of RSSI values with distance. The basic idea that is expanded upon in this research is that when a vehicle is detected multiple times as it travels through the DCU antenna coverage area, RSSI data can be

21 8 utilized to identify the detection that corresponds to when the vehicle was the closest to the DCU. 1.4 CONNECTION TO VEHICLE-TO-INFRASTRUCTURE RESEARCH The ideas proposed in this research are implemented as a wireless-based Vehicle-To- Infrastructure (V2I) data collection system that utilizes an existing infrastructure based on the Bluetooth wireless communications protocol. The focus on Bluetooth technology is due to the presence of many detectable Bluetooth devices that can currently be found in traveling vehicles. Thus, research findings can be tested and utilized on actual roads with varying traffic characteristics. Accordingly, this research can also provide more near-term data collection solutions for the types of applications discussed earlier. The research developments will also contribute to the Connected Vehicle Research initiative for V2I communications (US Department of Transportation, 2010). In the Connected Vehicle Research federal initiative, DSRC wireless technology operating in 5.9 GHz band is utilized instead of Bluetooth, which operates at 2.4 GHz. Nevertheless, the approach and methods developed in this research are applicable to other wireless technologies and in particular to DSRC. 1.5 RESEARCH CONTRIBUTION In this research, the limits of using a single wireless roadside DCU as a point detection device for vehicles containing a detectable wireless device are explored.

22 9 The spatial errors associated with wireless data collection from moving vehicles is the main challenge addressed in this research. The approach explored to address these spatial errors is the acquisition and processing of RSSI data which can be obtained at the same time device identification occurs. The resulting point detection precision realized is sufficient for a variety of road segment data collection applications. This is demonstrated using Bluetooth wireless technology. Multiple DCUs were utilized as point detection units at a three-way intersection to collect vehicle movement data. Results obtained from the wireless data collection were compared to ground truth data obtained from video recordings. Although Bluetooth technology was used as the implementation platform, the approach and principles utilized are applicable to other wireless technologies. In particular, the approach of utilizing and processing RSSI data is also applicable to DSRC wireless technology. The results and approach developed in this research, and implemented in Bluetooth, offers a current solution for a low-cost automated data collection system that is capable of providing data that is unavailable through most current automatic data collection techniques (with the exception of video image processing and GPS probe vehicle data collection neither of which is low cost). Data from multiple Bluetooth-based DCUs can collect sufficiently precise vehicle movement data over many types of road segments for a variety of purposes. With respect to intersections, there are multiple topics in practice and research that would benefit from the availability of such data. Some of these topics are reducing fuel consumption and

23 10 emissions through traffic signal strategies, active traffic management for signalized networks, assessing signal timing and control strategies, and intersection performance and travel time reliability. 1.6 THESIS ORGANIZATION In the next chapter, a summary of the literature relevant to this research is presented. Next, the methodologies investigated in this research are explained in detail, including two wireless-based traffic data collection methods. A series of test experiments are conducted to validate the methods proposed in this research and a summary of the results of these experiments is provided. Finally, a few examples of the real-world applications of the proposed techniques are presented.

24 11 2 LITERATURE REVIEW In this chapter, a review of the literature concerning modern traffic data collection technologies is provided. This review focuses on advanced, non-intrusive traffic data collection technologies that do not interrupt traffic during installation and/or operation. First, an introduction of traffic performance measures and, more specifically, measures of effectiveness applicable to intersections is presented. Next, a summary of existing automatic data collection systems is presented with more detail on travel time and other wireless data collection systems currently in practice. Finally, a summary of the connected vehicle research initiative is presented and its relevance to this study is briefly discussed. 2.1 TRAFFIC MEASURES OF EFFECTIVENESS The Federal Highway Administration (FHWA) defines a performance measure as the use of statistical evidence to determine the progress towards specific defined operational objectives. A performance measure provides a basic understanding of whether different aspects of the transportation system performance are getting better or worse (Balke et al, 2005). For example, arterial performance measures obtained over time can help transportation managers and operators to better evaluate the effectiveness of signal timing plans and other operational improvements. In the long

25 12 run, planning agencies would be able to monitor the arterial network and understand the effects of changing land uses (Bertini et al., 2001). According to Schrank et al. (2007), the public motoring cost during traffic congestion delay is worth more than a billion dollars in extra travel time, extra energy consumption and extra environmental impacts. Signalized intersections are usually designed with the primary emphasis on minimizing delay. Traffic signals, when implemented properly, can reduce delay and accidents, and improve the quality of traffic movement (Courage & Parapar, 1975). Signal timing strategies include the minimization of stops, delays, fuel consumption and air pollution emissions and the maximization of progressive movement through a system (Li et al., 2004). Improving signal timing reduces fuel consumption and emissions, hence, improving air quality. Signal retiming is a process that optimizes the operation of signalized intersections through a variety of low-cost improvements, including the development and implementation of new signal timing parameters, phasing sequences, improved control strategies and, occasionally, minor roadway improvements. Sunkari (2004) has summarized a comprehensive list of successful examples for signal retiming projects, demonstrating a significant amount of savings in delay time and fuel consumption at intersections. Researchers use a wide variety of traffic characteristics and performance measures to study traffic problems. For intersections, agencies use different performance measures to quantify the efficiency of roadway systems and evaluate the movement operations. Typical intersection data collection has been limited to

26 13 volume, occupancy, travel time, and average speed and the assessment has been conducted off-line (U.S. Department of Transportation, 2010). In other words, the researcher collected the data in the field and returned to the office for further data analysis. Therefore, there was always a time lag between when the observation was conducted and when the analysis was completed. Recently, a trend toward online data collection techniques has been raised in studies related to roadway and intersection operations performance (Balke et al, 2005; U.S. Department of Transportation, 2010; Bonneson & Abbas, 2002). By using real-time traffic data, drivers can be kept updated about the traffic conditions to make their driving safer and more efficient. Shaw (2003) has conducted an extensive survey on arterial performance measures. In his study, an overview of the most common arterial performance measures is presented. These measures are summarized in Table 2.1. Table 2.1 Selected arterial performance measures # Metric # Metric 1 Maximum Speed 10 Queue Length 2 Average Speed 11 Platoon Ratio 1 3 Speed Index 2 12 Number of Stops 1 Ratio of platoon miles traveled to vehicle miles traveled. 2 A ratio that is calculated by dividing a roadway segment s average observed travel speed by the posted speed limit for that segment.

27 14 # Metric # Metric 4 Density 13 Signal Failure 5 Travel Time 14 Duration of Congestion 6 Travel Time Variance 15 Number of Incidents 7 Average Delay 16 Incidents Duration 8 Maximum Delay 17 Nonrecurring Delay 9 Level of Service (LOS) 3 18 Emissions The traffic performance measures presented in Table 2.1 are among the most common metrics used to support decisions that may results in changes to the transportation system. Many transportation agencies have begun to introduce explicit transportation system performance measures into their policies, planning, and programming activities (Pickrell, & Neumann, 2001). Depending on the area of use, some performance measures may be more explanatory and useful than others. In addition, the metrics presented in Table 2.1 can be further customized for different areas of interest. Travel time, speed, and volume are major arterial performance measures (Wolfe et al., 2001). Travel time and volume data collection based on the reading of time-stamped media access control (MAC) addresses from Bluetooth 3 A performance measure used by traffic engineers to determine the effectiveness of elements of a transportation system

28 15 enabled devices has been in use for several years (Wasson et al., 2008). A basic setup to collect travel time data via Bluetooth includes a reader unit and an antenna. These two components collectively are referred to as the data collection unit (DCU). With DCUs placed at different locations along a road segment, computing the time-stamp differences for the same MAC address read at each DCU could generate travel time samples INTERSECTION PERFORMANCE MEASURES Engineers and researchers tend to use specific performance measures for different traffic infrastructures, as they are more descriptive to those particular systems. In this section, a series of intersection performance measures are explained. These measures are commonly used in studies focused on arterial roads and intersections. As a performance measure, delay plays a critical role when evaluating service level at signalized and un-signalized intersections. The average time that vehicles spend at an intersection is directly related to the average delay for that particular intersection. The 2010 Highway Capacity Manual (HCM) designates average control delay as the primary performance measure for signalized intersections (Transportation Research Board, 2010). Control delay is used in traffic signal timing, as well as to obtain the level of service (a common intersection measure of performance) for a particular intersection. The Highway Capacity Manual defines level of service for signalized and un-signalized intersections as a function of the

29 16 average vehicle control delay. This measure is popular since it can be presented to people without any knowledge in transportation engineering. Two major delay types are defined for intersections: stopped time delay and control delay. Stopped delay is defined as the time a vehicle is stopped at an intersection. Control delay is defined as the total delay due to the interaction of vehicles with the type of control utilized at the intersection and includes deceleration delay, stopped delay, and acceleration delay (Quiroga & Bullock, 1998). Theoretically, control delay is measured by comparison with the uncontrolled condition, i.e., the difference between the travel time that would have occurred in the absence of the intersection control and the travel time that results because of the presence of the intersection control. Existing techniques for measuring control delay are inaccurate and time-consuming. Therefore, control delay is rarely measured. Acceleration and deceleration time and distance data, or vehicle trajectories, are other important intersection data and are mainly related to signal timing, performance, and safety aspects of the intersection design. Acceleration and deceleration distances and times associated with speed change cycles (stop, start and slow down / speed up maneuvers) under normal driving conditions is essential for the analysis of determining geometric, stopped and queuing components of intersection control delay. Vehicle trajectory information is also essential for development and calibration of simulation models, the analysis of operating cost, fuel consumption and pollutant emissions. Many efforts have been summarized in the literature to model acceleration and deceleration profiles (Akcelik, & Besley, 2001; Bennett, 1994).

30 17 Unfortunately, due to the complexity of such maneuvers, the literature on vehicle trajectories has been very limited. Since direct observation and measurement of vehicle trajectories tend to be inaccurate and impractical, the developed models have been limited to historical or simulation data. In this research, the data collection efforts and proposed methodologies are mainly focused on intersection delay, vehicular speed and travel times at signalized arterial roads. Travel time and delay are intuitive performance measures, understandable for both engineers and public road users, which can provide valuable information on the quality of roadway system. 2.2 AUTOMATIC DATA COLLECTION TECHNOLOGIES To frame the relevance of the proposed research, a review of the current automated traffic data collection technologies and a review of the relevant research literature were conducted. Table 2.2 summarizes the most common automatic data collection techniques used in transportation applications.

31 18 Table 2.2. Most Common Automated Data Collection Technologies Used in Transportation Applications Technology Pros Cons Inductive Loop Mature, well understood technology Large experience base Provides basic traffic parameters (e.g., volume, presence, occupancy, speed, headway, and gap) Insensitive to inclement weather such as rain, fog, and snow Provides best accuracy for count data as compared with other commonly used techniques Common standard for obtaining accurate occupancy measurements High frequency excitation models provide classification data Installation requires pavement cut Improper installation decreases pavement life Installation and maintenance require lane closure Wire loops subject to stresses of traffic and temperature Multiple detectors usually required to monitor a location Detection accuracy may decrease when design requires detection of a large variety of vehicle classes Destroyed by utility cuts or pavement milling operations Microwave Radar Typically insensitive to inclement weather at the relatively short ranges encountered in traffic management applications - Direct measurement of speed - Multiple lane operation available Continuous Wave (CW) Doppler sensors cannot detect stopped vehicles

32 19 (Continued)! TECHNOLOGY PROS CONS Infrared (Laser Radar) Transmits multiple beams for accurate measurement of vehicle position, speed, and class Multiple-lane operation available Operation may be affected by fog when visibility is less than ~6 m (20 ft) or blowing snow is present Installation and maintenance, including periodic lens cleaning, require lane closure Ultrasonic Multiple-lane operation available Capable of over height vehicle detection Large Japanese experience base Environmental conditions such as temperature change and extreme air turbulence can affect performance Large pulse repetition periods may degrade occupancy measurement on freeways with vehicles traveling at moderate to high speeds Bluetooth Multiple-lane operation Can operate during night time and all weather conditions Non-intrusive Installation and maintenance does not need to interrupt the traffic Cannot capture the entire traffic. Can only sample the vehicles with active an Bluetooth device onboard Spatial errors

33 20 (Continued) TECHNOLOGY PROS CONS Monitors multiple lanes and multiple detection zones/lanes Easy to add and modify detection zones Rich array of data available Provides wide area detection when information gathered at one camera location can be linked to another Installation and maintenance, including periodic lens cleaning, require lane closure when camera is mounted over roadway (lane closure may not be required when camera is mounted at side of roadway) Performance affected by inclement weather such as fog, rain, and snow; vehicle shadows; vehicle projection into Video Image Processor adjacent lanes; occlusion; day-to-night transition; vehicle/road contrast; and water, salt grime, icicles, and cobwebs on camera lens Requires15-to21-m(50-to70-ft) camera mounting height (in a side-mounting configuration) for optimum presence detection and speed measurement Some models susceptible to camera motion caused by strong winds or vibration of camera mounting structure

34 21 As can be seen from the information in Table 2.2, there are major limitations with existing technologies in terms of cost, flexibility, and richness of data that the technology offers. Additionally, automatic data collection technologies, and particularly wireless-based systems, tend to generate a lot of data. However, the output data is not always accurate and useful. Therefore, to get reliable results from any automatic data collection system, it is necessary to take extra steps before and after data collection occurs to guarantee the acquisition of quality data. For the purpose of travel time data collection, it is necessary to use a series of filtering techniques to eliminate outlier input data and apply prediction and smoothing techniques to generate reliable travel time estimates. Among all the existing data collection systems, this research is primarily focused on travel time data collection systems. In the next section, an overview of the existing travel data collection systems is presented EXISTING TRAVEL DATA COLLECTION SYSTEMS Travel time data is one of the most important traffic measures of performance. A wide range of automatic travel time data collection systems have been in practice for a long time. In this section, an overview of these technologies is presented. The TransGuide traffic management center monitors traffic operations on a network of freeways and major arterial streets covering most of the metropolitan area of San Antonio, TX. One of the main components of the monitoring system is a series of sensors (mainly loop detector pairs and sonic detectors) spaced roughly 0.5 miles

35 22 apart that provide the capability to measure point speeds. Based on these point speeds, the system estimates travel times to specific landmarks and displays the estimated travel times on dynamic message signs (DMSs) located approximately every 2 to 3 miles. For each pair of adjacent detector stations, the system determines the lowest of the two detector station speeds and assigns that speed to the road segment that connects the adjacent detector stations. The system converts each partial road segment speed into an equivalent travel time and adds all of the partial segment travel times to produce an estimate of the total travel time between the DMS location and the major interchange. Besides TransGuide, there are three other major traffic data detection and archiving programs in Texas: TransVista in El Paso, TransStar in Houston and DalTrans in Dallas. The TransVista system uses a combination of point loop detectors and fifty-five cameras to monitor sections of the roadway. DMSs and lane control signs are used to disseminate information about freeway conditions to the travelers. The freeway sections visible through the cameras can be accessed on the Internet (PBS&J, 2001). The TransStar system in Houston uses Automatic Vehicle Identification (AVI) to monitor freeway traffic and disseminate information through popular media outlets such as radio, television, and DMSs. Primary information transmitted are travel time estimates and speed data. Vehicles with transponders are used as probes. AVI readers are installed along freeways to detect when the probe vehicles pass the point of

36 23 installation. The time difference between detection of a probe vehicle at successive AVI reader locations is used to arrive at travel time estimates and speed. The DalTrans/TransVision system in Dallas uses a combination of loop detectors and closed circuit cameras to provide information about incidents, lane closures, and speeds in the Dallas and Fort Worth areas. The information is provided to the public using DMSs or it can be accessed on the Internet. The Florida Department of Transportation (DOT) collects real-time data on a 40- mile segment on the I-4 corridor near Orlando using loop detectors (coupled as dualloops). A nonlinear time series model developed at the University of Central Florida was used to predict travel times. This forecasted travel time information was transmitted to the public through a website. The Wisconsin DOT provides an estimate of current travel times on the I-94, I-894, and I-43 corridors near Milwaukee using inductive loop detectors and freeway cameras. In California, a study is underway to start using remote sensor nodes to monitor traffic. Several magnetic sensors are placed in or above the road. These sensors detect presence of vehicles by measuring the change in the magnetic field produced above the sensor and transmit the data back to an access point. The system is controlled by an energy saving protocol called PEDAMACS. This protocol controls the data transfer between the access point and the sensors via radio signals in order to minimize the time the sensors are functioning. When the sensors are not needed they go into sleep mode to save energy. The access point, which can be placed adjacent to

37 24 the road, can be accessed by the transportation management centers to remotely obtain data and control the nodes. Traffic sensors are the most critical elements of an Intelligent Transportation System (ITS). Different types of traffic sensors with different qualities are available. However, existing systems are expensive and require a high level of maintenance. Neal and Yance (2005) developed the Advanced Re-locatable Traffic Sensor (ARTS) system for use in construction zones and incident management. The prototype smart sensor system developed is highly portable, and equipped with a wireless communication subsystem. The system enables real-time data acquisition, processing and communication to a Traffic Management Center (TMS) for appropriate actions. The ARTS system is built of several components that make the overall functionality of the system possible including a Doppler microwave radar, a digital compass, a GPS positioning subsystem, a satellite packet data terminal, and an on-board computer system. The unit is powered up by a solar portable system. The unit is capable of accurately measuring traffic counts, speed, volume, and headway, but is much more costly than the concept proposed in this research. A considerable amount of research has addressed the use of Bluetooth-based data collection systems on highways and arterial roads. In recent years, the use of timestamped media access control (MAC) address data acquired from Bluetooth-enabled devices to collect travel time data has received significant attention in the past few years. In the next section, the Bluetooth technology is briefly introduced and a

38 25 summary of the Bluetooth-based data collection systems is provided. The Bluetooth technology is later revisited with great detail in Chapter BLUETOOTH TECHNOLOGY Bluetooth is a short-range, wireless communications protocol operating in the licensefree 2.4 GHz frequency band. In contrast to Wi-Fi, which offers higher transfer rates and distance, Bluetooth is characterized by its low power requirements and low-cost transceiver chips. The Bluetooth technology is embedded in many devices, such as cellphones; smartphone and PDAs, laptop computers and tablets, Bluetooth handsfree sets and speakerphones (Jawanda, 2012). ABI Research, a market intelligence company specializing in global technology markets, recently reported that it expects cumulative Bluetooth device shipments to reach 20 billion by 2017 (ABI Research, 2012) BLUETOOTH-BASED DATA COLLECTION SYSTEMS The research conducted by Young (2007) is one of the first studies where Bluetooth technology was utilized for the acquisition of traffic data. In this study, MAC addresses were used as a unique identification number to tag vehicles in conjunction with a time stamp for a known location. These time-stamped data were later paired with similar data collected elsewhere. The differences in time between detections and the distance between collection locations were used to calculate a space mean speed.

39 26 Wasson et al. (2008) reports on the results of a study conducted by the Indiana Department of Transportation and Purdue University where several Bluetooth data collection units were used for several days to collect time-stamped MAC addresses along a 10-mile corridor of I-65 near Indianapolis, Indiana. The road segment where the MAC address data were collected included both signalized arterials and interstate highway segments to demonstrate the use of Bluetooth-based data to obtain travel time information. The results of the study showed that arterial data have a larger variance due to the impact of signals and the noise that is introduced when the vehicles constantly enter and leave the arterial. Tarnoff et al. (2010) compared data from GPS-equipped probe vehicles to Bluetooth-based data collected for the same routes. They concluded that the travel times calculated from the Bluetooth data are very close to freeway GPS data. However, due to low market penetration of the Bluetooth enabled devices, the study population is smaller than the data provided by third party companies for GPSequipped vehicles. Haghanhi et al. (2010), who also worked on the same corridor as Tarnoff et al. (2010), also reported the same issues when using the Bluetooth-based data collection approach. Quayle at al. (2010) studied arterial travel times in Portland, Oregon. Using several Bluetooth data collection units, data were collected for 27 days along a 2.5- mile signalized suburban arterial route, in addition to data from GPS floating car data for one day. They concluded that the travel times calculated based on Bluetoothbased data were close to the GPS floating car data (with an average error of 15.25

40 27 seconds) and that Bluetooth offers a low cost technique for collecting data that can reduce the amount of needed manual work. Tsubota et al. (2011) performed arterial traffic congestion analysis using the Bluetooth duration data. The research proposed the idea of utilizing the duration data (i.e., the time it takes Bluetooth devices to pass through the detection zone of Bluetooth scanners) to derive intersection performance measures at signalized intersections. The research team, however, has not reported any experiments to further validate and compare the results with real world data. The research also acknowledged the presence of various traffic modes and need for filtering unwanted samples from the arterial traffic. Young (2012) conducted a study on Bluetooth traffic monitoring technology for use as permanent sensors, delivering travel time data on Maryland freeways. To validate the accuracy of Bluetooth-based travel times, the data were compared to the data obtained from automatic traffic recorder systems installed on US route 29 west of Baltimore, MD, as well as the travel times obtained from the toll tag system on a section of I-80 in San Francisco, CA. The city of Houston, TX, in collaboration with the Texas Transportation Institute (TTI), conducted several projects to demonstrate the use of Bluetooth-based data collection systems to estimate urban travel times (Puckett & Vickich, 2010). The researchers concluded that Bluetooth-based traffic data collection technology is a viable option for the collection of travel time data. Based on an assumption of a single Bluetooth source per vehicle, the researchers claim to have captured 11% of the

41 28 traffic volume with their Bluetooth DCUs in Houston, Texas. The researchers also showed that their Bluetooth-based travel time estimates are comparably close to travel time estimates calculated using toll tag data. Bullock et al. (2009) extended the use of Bluetooth-based data collection technology to applications other than roadside travel time data. In their research, Bluetooth-based data were used to estimate passenger delays at queues for security areas of the Indianapolis International Airport. Short range (Class II) Bluetooth modules were placed inside enclosures on both the upstream and downstream sides of a security checkpoint. The selection of Class II transceivers was due to a desire to minimize the variations in travel times detected due the close proximity of the two detection units inside of the airport terminal. The researchers concluded that the number of Bluetooth sources recorded corresponded to a range from 5% to 6.8% of passengers, assuming one Bluetooth-enabled device per passenger only. The research has captured the changes in travel times passing through the security to be correlated with the number of passengers screened at the checkpoint. However, ground truth travel time data for passenger transit times through security were not available for comparison. Day et al. (2009) investigated the potential of using Bluetooth-based data to estimate delays at construction work zones in real-time. Their study was conducted over a period of twelve weeks on a rural interstate work zone in Northwest Indiana. The researchers showed that by presenting the motorists with the Bluetooth-based travel time data for both the main segment and the temporary detour, they were able

42 29 to show that more motorists utilized the detour route than when no travel time data were provided. Day et al. (2010) utilized travel times collected using Bluetooth technology to measure the effectiveness of signal timing. Barcelo et al. (2010) utilized the travel time data to predict short-term travel times using Kalman filtering. No papers utilizing multiple Bluetooth-based DCUs in the same area and triangulation to estimate vehicle location have been found BLUETOOTH SIGNAL DISTANCE-SIGNAL STRENGTH RELATIONSHIP A number of researchers have examined the use of Received Signal Strength Indicator (RSSI) data from Bluetooth devices as a means to provide location information in indoor and outdoor environments. These studies mainly try to accurately locate a signal source by processing the signal strength data. At the time this research was conducted, no other study could be found on utilizing signal strength data to enhance traffic data collection. In general, researchers have followed two different approaches to obtain distance measures from RSSI readings. The first approach uses empirical data to map RSSI values to specific locations in the environment under study. An example of this approach is the work performed by Feldmann et al. (2003) where a regression model was deployed to estimate the distance for the observed RSSI values. The second approach utilizes radio frequency propagation models as a basis for distance estimation. Kotanen et al. (2003) and Fink et al. (2009) are examples of such work.

43 30 Ahmed et al. (2008) reported on a prototypical proof-of-concept implementation of a Bluetooth and wireless mesh networks platform for traffic network monitoring. In this study, Bluetooth detection data are used to obtain travel time and speed samples. To improve the accuracy of the results, the system takes advantage of RSSI data collected during the inquiry process. The basic idea behind this system is that Bluetooth devices will generate stronger signal strength when they are closer to a receiver. The study did not specifically mention any details about the procedures and routines used to obtain RSSI data or how the RSSI data were processed. According to their test results, in some cases the system failed to correctly predict the location of the vehicle when it passed a detection unit. The noisy nature of the RSSI values and the simplicity of the time stamp selection algorithm used could have affected the results. 2.4 CONNECTED VEHICLE RESEARCH INITIATIVE The U.S. Department of Transportation (USDOT) initiated the Intelligent Transportation Systems (ITS) program to solve transportations issues related to safety, mobility, and environment friendliness, by integration of intelligent vehicles and intelligent infrastructure. The goal of the Connected Vehicle Research initiative (previously known as the IntelliDrive program) is to provide connectivity between vehicles, infrastructure and passenger wireless devices to ensure safety, mobility and environmental benefits (U.S. Department of Transportation, 2010). The wireless technology designed for this type of automotive connectivity purposes is known as Dedicated

44 31 Short Range Communications (DSRC). DSRC operates on the 5.9 GHz frequency band, and has been assigned by the USDOT for the use of automotive safety applications (U.S. Department of Transportation, 2010). DSRC is a secure and reliable form of communication making it the focus of many vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) applications. In the ITS strategic plan, the USDOT is committed to use wireless technologies such as DSRC for active safety in both V2V and V2I applications (Amanna, 2009). The ITS program has identified the following areas to focus research activities of the connected vehicle research (Row et al., 2008): Technology scanning and research to identify and study a wide range of potential technology solutions. Research, demonstration, and evaluation of technology-enabled safety applications. Establishment of test beds to support operational tests and demonstrations for public and private sector use. Development of architecture and standards to provide an open platform for wireless communications between vehicles and roadside infrastructure. Study of non-technical issues such as privacy, liability, and application of regulation Research on ancillary benefits to mobility and the environment.

45 32 In general, the Connected Vehicle Research program is mainly focusing on crash prevention and efficiency improvement through the integration of intelligent vehicles and intelligent infrastructure. The ultimate goal of the project is to provide an infrastructure where vehicles can identify threats and hazards on the roadway and communicate this information over wireless networks to alert and warn other drivers. Over the last five years, various states have been participating in this program and among those California, Michigan, and Arizona have initiated major projects (U.S. Department of Transportation, 2010). In response to the USDOT ITS program initiation, several protocols and programs for a portable DSRC system have been proposed. Maitipe and Hayee (2010) have presented a study to develop, implement and demonstrate a DSRC-based V2I information relay system for improving traffic efficiency and safety in the work-zone related congestion buildup on US roadways. Their study included two sets of road side units (RSU) and on-board units (OBU). The RSU devices are portable systems that can be installed temporarily near work zones. The RSU unit plays the role of a central dispatcher for a series of OBUs in the range. When a vehicle equipped with an OBU approaches the work zone, traffic data will be transmitted to the RSU and after data analysis by RSU, information will be broadcasted to all OBU devices in range that have not reached the work zone. Jang et al. (2010) have proposed a smart roadside system providing driver assistance and traffic safety warnings. Wang (2009) has presented an Intellidrive application for signalized intersection safety, where signals can dynamically respond to hazardous conditions to avoid red-light running

46 33 related collision. The proposed collision avoidance system is based on a model for red-light running prediction with Intellidrive V2I communications that is specially designed for all-red extension for IntelliDrive-enabled vehicles to improve red-light running prediction. The great advantage of these DSRC-based systems is the ability of two-way communication between OBUs within one-kilometer range and RSUs. RSUs transmit data for all OBUs in the proximity. However, the major drawback of this system is how to retrofit the OBUs to all existing users. Thus, only vehicles equipped with OBUs (test units) can benefit from this system at this time, which is a very small portion of the traffic.

47 34 3 METHODOLOGY In this chapter the approach and methods utilized to collect vehicle movement data over time with wireless data collection units are presented. The methods presented in this chapter can be implemented using a wide range of wireless networking protocols including Wi-Fi, DSRC, Bluetooth, and ZigBee. Bluetooth was selected as the implementation platform due to the current use of Bluetooth devices in vehicles today, thus allowing real-world testing to evaluate the methods developed. A detailed introduction of the Bluetooth technology is provided in section 3.1. Two different approaches were explored that differ with respect to the nature of the vehicle movement data sought, and the usefulness of the results obtained. The first approach is wireless signal trilateration. The objective of this approach is to use wireless vehicle identification and signal strength data to dynamically collect vehicle position data within the discovery range of the data collection units. The objective of this approach is to collect data that can be used to identify a vehicle s trajectory over time, and hence could also be used to extract several intersection performance measures such as intersection control delay and acceleration/deceleration times. The results obtained did not provide the accuracy and consistency needed to identify a vehicle s trajectory over time. However, the presentation of the methodology identifies current data collection limits with the wireless technology utilized. The second approach seeks to utilize the data collection units as point detection systems. Wireless vehicle identification and signal strength data are used to estimate

48 35 when a vehicle has passed an imaginary line extended from the data collection unit across the road. By utilizing a series of data collection units that are separated by known distances along a road segment, the objective is to accurately estimate travel times between any pair of units. This information can be used to estimate other useful performance measures such as average control delay and the average time spent at an intersection. A significant challenge in this approach is to address the large coverage area of the data collection units, and the resultant potential spatial errors when utilizing the data collection units as point detection units. This chapter begins with a presentation of needed background information. First an overview of Bluetooth technology is presented which also includes a description of the Bluetooth scanning or inquiry procedure. Relevant signal propagation concepts are then introduced. The application of these concepts to obtain vehicle movement data and accurate travel time data is investigated using two proposed approaches. In the first approach, trilateration concepts are used to collect vehicle location data. In the second approach, signal strength data are utilized to estimate the time that vehicles pass a Bluetooth-based data collection unit. The data collection approaches are explained in detail in separate sections, and further validation tests are presented. 3.1 BLUETOOTH TECHNOLOGY The Bluetooth Special Interest Group (SIG) first published the Bluetooth wireless communications protocol in By 2010, over 13,000 companies had joined the SIG including almost every major commercial electronics manufacturer. The

49 36 Bluetooth protocol includes specifications for the frequency (spectrum) utilized, interference handling, range, and power consumption of the technology. The SIG created three Bluetooth classes that differ with respect to the distance that communications between devices was intended to work reliably (see Table 3.1). For this research a Class I Bluetooth module was used to achieve longer ranges and more reliability. Table 3.1 Bluetooth Classifications Bluetooth Classification Effective Range Class I 300ft Class II 33ft Class III 3ft In this study, Bluetooth technology has been used to build a data collection unit (DCU). The DCU is a device that identifies Bluetooth-enabled devices within its discovery range by continuously performing a Bluetooth inquiry process that seeks to identify other Bluetooth devices with communications range. Since each Bluetooth device has a unique MAC address, the DCU can distinguish between different Bluetooth devices, and therefore different vehicles that house these devices as they travel. The inquiry process is a complex procedure that is explained in detail in the Bluetooth specification documents published by Bluetooth Special Interest Group. A brief summary of this procedure comes next.

50 37 The Bluetooth protocol implements a master-slave structure (similar to a serverclient structure in a LAN network). When a master device wants to initiate a connection, it first performs a process that searches for other discoverable master and slave devices in range. In the Bluetooth specification this scanning procedure is referred to as the inquiry process. In this research the data collection unit operates as a master device and is programmed to continually repeat the inquiry process, while also never establishing a connection with other slave devices. The Bluetooth inquiry process follows a series of steps defined by the protocol in which a master device attempts to detect other devices responding to an inquiry message. The specific mechanism by which Bluetooth devices identify and establish communication with multiple Bluetooth devices is called frequency hopping and is probabilistic in nature. Therefore, the inquiry process may not detect all Bluetooth devices within communication range of the master device. To increase the chances of detecting all possible Bluetooth devices nearby, the inquiry process can be repeated multiple times. For more details on the Bluetooth inquiry process see Appendix. 3.2 TRILATERATION APPROACH TO ESTIMATE VEHICLE MOVEMENT The first approach investigated in this research was to use a trilateration technique to estimate vehicle movement through an intersection covered by at least three DCUs. From a mathematical perspective, if the distance of an object to at least three different locations is a known, then there is a unique location in three-dimensional space where the object must reside. Trilateration is the process of solving for this location (the

51 38 technique is also used in GPS to determine location). By using multiple DCUs and trilateration, the objective is to collect vehicle position data over time through the DCU coverage area for those vehicles containing active Bluetooth devices. Since the trilateration approach is based on distances to known locations, it is necessary to estimate the distance between data collection units and the vehicle (with active Bluetooth device on board) from signal strength data. The details of Bluetooth signal strength acquisition are presented in section Next, the conversion of signal strength data into distance estimates using a signal propagation model is presented. The accuracy potential of the trilateration approach is demonstrated through a field experiment. Prior to the accuracy assessment the environmental power decay factor of the signal propagation model was estimated from signal strength data collected from Bluetooth devices at specific locations relative to three DCUs. The accuracy of the trilateration approach was then evaluated using new random Bluetooth device locations BLUETOOTH SIGNAL STRENGTH ACQUISITION In the literature of the Bluetooth data collection systems, two types of possible methods for acquiring the Bluetooth received signal strength information (Received Signal Strength Indication or RSSI) have been proposed [Bluetooth Special Interest Group, 2011; Bluetooth SIG, 2004]: the connection-based method and the inquirybased method. In the connection-based method, a communication connection between the DCU and a mobile Bluetooth device must be established before signal strength

52 39 measurements can be obtained. In a peer-to-peer connection mode, signal strength is much easier to obtain than extracting inquiry based signal strength. That is because all Bluetooth software (or more accurately, Bluetooth drivers) is supplied with a large library of ready-to-use functions that can be called within a Bluetooth connection. Among these ready-to-use functions, there is a function that returns the signal strength for the active connection. The problems with connection based signal strength are response time, and the requirement for authorization to establish a connection before obtaining signal strength information. In addition, on many Bluetooth devices the transmission power adjustment, which is used to adjust power usage based on signal strength values, eliminates the correlation between the signal strength measurement and the distance between a mobile Bluetooth device and the DCU. The inquiry-based method retrieves the RSSI measure from the inquiry response without establishing any connection to the mobile Bluetooth device. The RSSI can be obtained during the Bluetooth inquiry procedure at the same time that the MAC address is read and thus does not add any additional processing and/or delays when reading MAC addresses (Almaula & Cheng, 2006). Therefore, the inquiry-based method is used to obtain RSSI data for distance estimation process in this study ESTIMATING DISTANCE FROM RSSI The basis for almost all signal localization methods is the Received Signal Strength Indication (RSSI). RSSI number is a unit of measurement that represents the radio

53 40 power level received at a destination. RSSI is usually presented in units of energy, which can be both absolute (mw) and relative (dbm) representations. Absolute power of a signal is measured in wattage (W). The Bel or Decibel system can only describe relative power. For example, a gain of 3 db means the signal is 2 times as strong as it was before, but the db scale does not define the amount of power transmitted at source. The dbm system instead, indicates the power relative to 1 milliwatt of power. This conversion can be done using x = 10 log ଵ (P) where x is power in dbm and P is power in milliwatt. In order to use signal strength for localization, RSSI values must be converted to accurate distance estimates. In the literature, two main approaches have been used to obtain distance measures from RSSI values. The first approach uses an empirical method to map RSSI values to specific locations in the environment under study. A dense population of locations ensures a rich sampling of the RSSI throughout the environment (Awad et al., 2007; Almaula & Cheng, 2006; Feldmann et al., 2003). This method requires a location-rssi map preparation stage, and a developed map cannot be applied to a different location. Additionally, this method is only applicable to the devices and location used to develop map of RSSI values. Therefore, this method is not a good candidate for the purpose of this project. The second approach utilizes a radio signal propagation model. There is an extensive body of literature devoted to understanding and modeling radio signal propagation (Kotanen et al., 2003; Fink et al., 2009; Thapa & Case, 2003). An overview of the signal propagation model used for this research is provided in this section. In this approach power loss throughout the environment is computed as a function of the

54 41 distance between antennas and fit to the entire environment by a power decay factor so that the amount of loss (in milliwatt) can be measured (Fink et al., 2009). = ( ఒ ) ଶ Equation 3.1 ସగ 10 log ଵ P 10 log ଵ P ௧ = 20 log ଵ ൬ λ 4π ൰ + 10n log 1 ଵ d Where P(t) is the signal strength received at a distance d and P(t) is the signal strength transmitted presented in mw. λ is the wavelength. The factor n represents the path loss exponent (a.k.a. environment power decay factor) and is affected by the external factors like multi-path fading, absorption, air temperature, etc. The propagation model presented in Equation 3.1 can be generally used for any signal. A log10 was applied to both sides of the model presented in Equation 3.1 to work with dbm. (see Equation 3.2 for the results). The signal propagation model used for this study (presented in Equation 3.2) is based on the work completed by Morrow (2002). This model was utilized to translate RSSI levels into distance estimates. In this model power loss throughout the environment is computed as a function of the distance between antennas of two communicating devices and is adjusted for a particular environment by an environment power decay factor: Equation 3.2 Where PL is the received power level relative to the transmitted power (RSSI, explained in units of dbm), λ is the Bluetooth wavelength in meters, n is the environment power decay factor, and d is the distance at the receivers location (for

55 42 Bluetooth, λ = meters is used). Numerous environmental factors can be introduced into a signal propagation model such as Doppler effect, multi-path, fading, etc. These factors can quickly increase the complexity of the model and hence, reduce its usability. By considering λ = and solving equation 3.2 for d, the signal propagation model can be formatted as Equation 3.3: షరబ.మఱళ d = 10 భబ Equation 3.3 In addition to the theoretical model presented in Equation 3.3, several other empirical models were tested. Empirical models were examined to check if other nonlinear models offered a better fit than the signal propagation model. An example of one empirical model is presented in Equation 3.4: d = A PL Equation 3.4 Where A and B are parameters of the empirical model. The parameters for different signal propagation models were fit to data generated by obtaining signal strength readings from Bluetooth devices placed at known distances to multiple DCUs. Details of this data acquisition are presented in section The Parani UD-100 Bluetooth modules utilized to generate this data report RSSI levels in units of dbm. Since the power level transmitted by most of commercial Bluetooth-enabled devices (such as cellphones, headsets, PDAs, etc.) is about 0 dbm (1 milliwatt), the RSSI level received at the scanner device can be used as PL. By knowing the received RSSI level and having a proper environment power decay factor, one can use the propagation model presented in Equation 3.3 to calculate the distance estimate.

56 43 The value for the power decay factor was determined empirically from generated data. A meta-heuristic optimization algorithm called particle swarm optimization was utilized to compute the value of this parameter. This method was applied to data generated by placing Bluetooth devices at random positions with known distances to fixed DCU locations, and recording the RSSI levels received from the Bluetooth devices (18 samples for each of the two test cell phone). The particle swarm optimization in this study tries to find the best value for the environment power decay factor (n) so that when the sample pairs of distance-rssi are inserted into the propagation model, the total squared distance error is minimized. An overview of meta-heuristic algorithms and specifically the particle swarm optimization method is presented next PARTICLE SWARM OPTIMIZATION Particle swarm optimization is a general class of metaheurstic algorithms. A metaheuristic refers to a computational method that attempts to optimize a problem iteratively by following a general search strategy. Metaheuristic algorithms are heuristics in that in most cases optimality is not guaranteed, but they have been successfully applied to many real combinatorial and non-linear optimization problems. Metaheuristics algorithms can often generate very good solutions to difficult optimization problems in a reasonable amount of time. Particle swarm optimization (PSO) was first introduced by Kennedy and Eberhart and was first intended for simulating social behavior (Kennedy & Eberhart, 1995).

57 44 Kennedy and Eberhart s work was originally influenced by earlier research completed by Heppner and Grenander about bird flocks searching for corn (Heppner & Grenander, 1990). In PSO a number of entities, which are referred to as particles, are initialized in the solution space of a problem or function. Each particle will give an objective function value (Poli et al., 2007). In each iteration, every particle moves through the search space by combining some aspect of the history of its own current and best locations with that of other particles, with some random perturbations. The next iteration takes place after all particles have been moved. Eventually the swarm (all of the particles) as a whole, like a flock of birds collectively foraging for food, is likely to move close to an optimal value. A wide variety of PSO algorithms have been proposed, and the specific PSO algorithm implemented is presented next. The particle swarm optimization starts with a random value for the environment power decay factor and then begins an iterative process to improve this value. For this study, the algorithm initialized 100 random values for the environment power decay factor (n). Each of these values, which can be a candidate solution for n, is called a particle. It is important to mention that the algorithm has no knowledge of the underlying propagation model, which helps to build the objective function for this study. Thus it has no way of knowing if any of the candidate solutions are near to or far away from a near-optimum solution. The algorithm simply uses the objective function to evaluate its candidate solutions, and operates upon the resultant fitness values that is the difference between the estimated distance using an RSSI sample and

58 45 a random value for n in the propagation model and the actual distance for the corresponding RSSI. The algorithm maintains the sum of squares for all distance errors and tries to minimize this value by improving the particle s locations. The algorithm keeps track of the best value for each particle (local optimum) and for the entire particles population (global optimum) to move toward the best fitness value. The steps of a basic particle swarm optimization algorithm that were followed to estimate the environment power decay factor n in the propagation model: 1. Start with an initial set of particles, typically randomly distributed throughout the design space. In this study particles are random values for the environment power decay factor n in the signal propagation model. For this study, a number of 100 particles were initialized with random starting values. The uniform random number generation method in visual basic was utilized to initialize the particles. 2. Calculate a velocity vector for each particle in the swarm. The velocity and position update step (step 3) are responsible for the optimization ability of the algorithm. The velocity of each particle is updated using the following equation: v (t + 1) = wv (t) + c ଵ r ଵ [x (t) x (t)] + c ଶ r ଶ [g(t) x (t)] i is the index of the particle. v (t) is the velocity of particle i at time t. x (t) is the position of the particle i at time t. The parameters w, c ଵ, and c ଶ are usersupplied coefficients ( 0 w < 1.2; 0 c ଵ 2; 0 c ଶ 2 ). x (t) is the

59 46 individual best candidate solution for particle i at time t and g(t) is the swarm s global best candidate solution at time t. These parameters were also initialized randomly. For each set of candidate solutions (a.k.a. particles) fitness evaluation is conducted by supplying the candidate solution to the signal propagation model. Pairs of collected Distance-RSSI data are provided to the algorithm for the fitness evaluation process. For each iteration, the estimated distance is compared to the actual distance to compute the fitness value. 3. Update the position of each particle, using its previous position and the updated velocity vector to reduce the total fitness values. Once the velocity for each particle is calculated, each particle s position is updated by applying the new velocity to the particle s previous position: x (t + 1) = x (t) + v (t + 1) 4. Go to Step 2 and repeat until total fitness values converge to zero. The algorithm presented in this section was implemented using Microsoft Visual Basic. The algorithm was replicated a few times with particle swarm sizes of 100 and Each replication of the algorithm requires several minutes to complete and R^2 values of higher than 0.90 were achieved SIGNAL LOCALIZATION APPROACHES Triangulating an object's position is a method of determining the location of the object, based on its distance relative to other known locations or signal sources. In

60 47 general, there are two triangulation approaches. In the first approach that is usually referred to as triangulation, knowing the coordinates (x,y) of the three stations and the distances (r) from the stations to the location of the object, it is easy to find the circle equations that represent all potential points for the location of the object relative to those three reference points. To find a single point that represents the actual location of the object, one needs to solve the three simultaneous circle equations (see Figure 3.1). However, it is very difficult to solve these equations, since they are nonlinear. Therefore, there is a need for a simpler method. If one pair of equations for the circles are taken and subtracted one from the other, the result will be the equation of the line passing through the two points of intersection of the two circles (see Equation 3.5). ଶ ቊ Circle a: (x x ୟ) ଶ + (y y ୟ ) ଶ = r ୟ Equation 3.5 Circle b: (x x ୠ ) ଶ + (y y ୠ ) ଶ = r ୠ ଶ ଶ Circle b Circle a: 2x(x ୟ x ୠ ) ൫x ୟ ଶ x ୠ ଶ ൯ + 2y(y ୟ y ୠ ) ൫y ୟ ଶ y ୠ ଶ ൯ = r ୠ ଶ r ୟ The big advantage being that the result is therefore a linear equation, which is much easier to deal with. Next, by subtracting any other two of the three circle equations, a second line equation would be obtained. The intersection of these two lines is the location of the object. Both line equations will pass through two points of intersection between each pair of circles, and one of these intersection points is the point that the object is located at.

61 48 Figure 3.1. Intersection of Three Circles at a Single Point The triangulation method explained so far is very simple and works as long as these three circles really intersect at a single point. However, that is not always the case. In signal propagation, the distance estimates always have some error, which prevents the three circles from intersecting at a single point. Thus, there would be a need for a different algorithm to handle such errors and still be able to estimate the location of the object. To mitigate the errors resulting from the propagation model, a trilateration process can be utilized. In geometry, trilateration is an iterative process to determine absolute or relative location of an unknown point by measurement of distances from a number of known points, using the geometry of circles and/or spheres. Trilateration is extensively used in commercial navigation applications, including Global Positioning Systems (GPS).

62 49 In the case of a two-dimensional problem, when it is known that a point is located on at least three curves such as the boundaries of three circles then the circle centers and the three radii provide sufficient information to narrow the possible locations down to one location. However if these three circles do not intersect on a single point, an iterative process can be used to relatively scale the circles' radii to force these three circles to intersect at one point. This extra step is needed when errors resulting from the inaccuracy of the propagation model are present. Both algorithms explained in this section are implemented using Python language and are presented in the Appendix section B TRILATERATION STUDY TO DEVELP A SIGNAL PROPAGATION MODEL For this experiment three Bluetooth scanner devices were used. Bluetooth scanners were attached to 2.4 GHz omnidirectional antennas to match the setup configuration implemented for a research project conducted for Oregon Department of Transportation. A large parking lot on Oregon State University campus was selected to conduct this experiment. Figure 3.2 illustrates the test site for this experiment. The antennas were placed in an L shape configuration, 10 meters separated from each other (see Figure 3.3 for setup arrangement).

63 50 Figure 3.2. Trilateration Test Experiment Setup Figure 3.3. Scanners Arrangement in an L Shape

64 51 For this experiment a stratified random sampling approach was selected to evenly distribute sample locations across the discovery range of the units. The discovery range (which represents an imaginary intersection with its four approaches) was divided into 9 different areas. Defined areas (numbered from 1 to 9 in Figure 3.3) were sorted in a random order and for each area two random samples were collected. Pairs of locations (x, y) were generated randomly for each defined area. To facilitate the test process, Table 3.2 was developed. For each row in this table, two experimental Bluetooth-enabled cell phone devices were placed at the location noted on column three. Next, signal strength levels from all three Bluetooth scanners were measured and recorded (i.e. RSSI 1, RSSI 2, and RSSI 3). The signal propagation model was calibrated based on the collected data. Table 3.2. Random locations selected for Test Cell Phone 1 # Region Location ( x, y ) RSSI 1 RSSI 2 RSSI , , , , , , , ,

65 , , , , , , , , , , FITTING THE ENVIRONMENT POWER DECAY FACTOR Using the data collected during the study presented in section 3.2.5, and by utilizing a particle swarm optimization, a number of mathematical models were developed to describe the signal propagation phenomena (See Table 3.2 for the data). As it was explained earlier, these models include the theoretical signal propagation model based on Morrow s work (2002) and a few empirical models. For each mode, an R-square value was calculated based on the data used to fit model s parameters. Higher R- square values indicate that the model (and its parameters) do a better job in converting RSSIs into distance estimations. Among these models however, the model based on the theoretical power loss model generated the highest level of accuracy.

66 53 The propagation model equation with its calibrated parameters is presented in Equation 3.6 that has a value of 1.68 for n. RSSI = 16.8 log ଵ (d) Equation 3.6 As a part of the model development process, the distance estimates for each sample location based on the recorded RSSI levels were compared to the actual distance from the sample location to the DCUs. Figure 3.4 illustrates actual and estimated distances versus recorded RSSIs. In the figure, actual distance samples for each random location is marked using dots for each test cell phone Estimated Distance Error Error (distance) Estimated Distance Error RSSI- Figure 3.4 Error Comparisons for Estimated Distances Using the Developed Signal Propagation Model for Both Test Cellphones

67 ACCURACY ASSESSMENT Next a second field test was conducted to evaluate the accuracy of the developed signal propagation model. This experiment was completed at the same test site with similar setup configuration. An assessment of the propagation model (Equation 3.6) accuracy was conducted using a number of location-rssi pairs. The propagation model developed in previous step was used to estimate the location of the Bluetooth device merely based on the RSSI levels recorded on three DCUs. In this step the RSSI values recorded from three DCUs were translated into three distance estimates. Finally, the iterative trilateration algorithm was utilized to obtain relative location estimates for each sample reading. The location estimations were compared against actual locations and the distance between these two points were used as the error value. The results are summarized in Table 3.3. The first two columns show the location of the test cell phone relative to the antenna 1 (see Figure 3.3). The next three columns show the distance estimates from each antenna using the recorded RSSI. Columns six and seven show the estimated location using the iterative trilateration technique. The last column represents the Euclidean distance between the actual location and the estimated location that serves as the error.

68 55 Table 3.3 Signal Propagation Model Accuracy Test Results Actual Location Linear Estimated Distances (m) Predicted Location (x,y) Distance Ave STD CONCLUSIONS AND LIMITATIONS Although the estimated locations obtained through the trilateration method are close to actual locations on average, the test results are not satisfactory on an individual case basis. In those cases with large errors, the predicted locations are a few meters off from the actual location of the test cell phones. There are several sources of inaccuracy in the environment that makes the trilateration approach inappropriate for the outdoor application proposed in this research. The signal strength indicator itself is a measure that has some noise in its

69 56 nature. Moreover, physical phenomena such as the Doppler effect, multi-path, and fading are other factors that can introduce error to the signal strength. Additionally, the dynamic movement of physical objects on the road (i.e. vehicles, bicyclists, and pedestrians) is another factor that makes the outdoor trilateration based on the signal strength challenging. In the rest of this research another approach based on the wireless received signal strength is pursued. The new approach is less sensitive to the natural noise and fluctuation within the RSSI levels. Furthermore, the new approach utilizes RSSI data from a group of detections for the same device to obtain location data, and does not compare detections from different devices. 3.3 VEHICLE POINT DETECTION SYSTEM In this section a different approach for collecting vehicle movement data is presented. The objective is to explore and test how the DCUs that collect data wirelessly can be utilized as point detection units. A point detection unit identifies the time that a vehicle has just passed an imaginary line on the road that originates from the data collection unit. By utilizing multiple DCUs that function as point detection units a vehicles trajectory through a road segment can be approximated. The objective of this method is less ambitious with respect to the vehicle movement data level of detail sought in the Trilateration approach. To achieve this goal, time stamped wireless data obtained by a DCU from passing vehicles, which includes RSSI levels, is utilized to estimate when a vehicle was the closest to the data collection unit. When multiple

70 57 DCUs are utilized on a road segment this information can be used to derive several performance measures such as travel times and intersection delay. The remainder of this section starts with a presentation of the background theory supporting how RSSI data can be used to estimate when vehicles just pass a DCU. This theory is implemented in a RSSI-based point detection algorithm that is described next. Finally, a validation test experiment and results for the proposed algorithm are presented VEHICULAR MOVEMENTS NEAR A DATA COLLECTION UNIT AND THE RSSI-DISTANCE RELATIONSHIP Vehicle trajectories as a vehicle travels by a DCU can take many different forms. Vehicles may pass the DCU at a fairly constant speed, or could be accelerating or decelerating. Vehicles may also stop near the DCU before passing it. If a vehicle contains a detectable wireless device, the DCU will be detecting the vehicle multiple times as it travels in the coverage area of the DCU. In this section theoretical signal propagation models are utilized to understand how the signal strength of the DCU detections will vary over time for different vehicle trajectories. In this research it is assumed that a DCU is located at the stop line located on a signalized intersection. The vehicle trajectories analyzed include through passes and stops due to a red light. The stops may occur relatively close or far from the stop line. For these three cases, numerical examples of RSSI levels as a function of distance and time (i.e., vehicle trajectory) are generated and plotted (see Figures 3.5, 3.6, and

71 58 3.7). The predicted RSSI levels as a function of distance and time are obtained from the model presented in section (Equation 3.6) The plots show the time on the X axis (in seconds) as the vehicle enters and leaves the intersection, the predicted RSSI level on the left Y axis (in dbm), and the distance from the perpendicular line on the road, extended from the DCU on the right Y axis (in meters). It is assumed that the design vehicle has a constant speed of 35 MPH when approaching the stop line, and it takes about 10 seconds to stop or return to the constant speed if a stop occurs. For example, assume that a vehicle at time t is 90 meters away from the perpendicular line extended on the road from the DCU. Assuming that the lateral distance from the vehicle s lane to the DCU is about 8 meters (26 feet is the width of two standard lanes), this will result in a direct distance of 8 ଶ + 90 ଶ = meters from the DCU. Using the propagation model introduced in Equation 3.6, the expected RSSI level at this distance is -67dbm. The output of the propagation model in Equation 3.6 is a positive number since the propagation model represents the RSSI level change as the signal travels over a given length (path loss). An average cell phone transmits a power level of 1 milliwatt (0 dbm). Therefore, the RSSI level at a distance of meters should be -67 dbm. To consider the impact of environmental noise on the RSSI levels recorded, and the vehicle s movement effect on the RSSI levels, a random value from a normal distribution was also added to RSSI values predicted by equation 3.6. These plots (Figures 3.5, 3.6, and 3.7) show a sample of RSSI behavior as a vehicle enters and leaves a signalized intersection.

72 RSSI Distance Figure 3.5. Highlighted Example of a Through Pass RSSI and Distance Sample Data RSSI Distance Figure 3.6. Highlighted Example of a Stop Far From Stop Line RSSI and Distance Sample Data

73 RSSI Distance Figure 3.7. Highlighted Example of a Stop Near Stop Line RSSI and Distance Sample Data Figure 3.5 shows the scenario when a vehicle arrives at the intersection during a green light. In this example, there is no congestion at the intersection. Therefore, the vehicle travels through the intersection with a constant speed. As the vehicle enters the discovery range of the DCU located at the intersection, the RSSI levels start to increase until a maximum point and then decrease, until the vehicle leaves the intersection. Theoretically, the maximum point is recorded when the vehicle was the closest to the DCU. Therefore, the time-stamped data record with the highest RSSI value is the best estimate of the time that vehicle has passed the DCU. Figure 3.6 illustrates the second scenario in which the vehicle stops due to the red light. In this case the vehicle stops far from the stop line because a queue has formed in front of the stop line. During the time that vehicle has stopped, it is unlikely to see large differences in RSSI levels since the vehicle s distance to the DCU remains constant. Once the vehicle starts to move again, the RSSI level increases, as the

74 61 vehicle gets closer to the stop line and then decreases as the vehicle continues beyond the stop line. Again, the time-stamped data record with the maximum RSSI level best estimates the time the vehicle passed the stop line. Figure 3.7 shows the third scenario in which the vehicle also stops due to the red light. In this scenario however, the vehicle stops very close to the stop line. In this case, it is not easy to use the RSSI data to predict when vehicle has passed the stop line since it is less likely that the time-stamped data record with the maximum RSSI value is the best estimate of the time that vehicle has passed the stop line. This can be attributed to the effect of noise on the RSSI levels, which are often greater than the predicted difference in RSSI levels when a vehicle stops close to the DCU and when it is adjacent to the DCU. This complexity is usually magnified in real-world scenarios in which the noise levels of the RSSI data can be significantly higher due to other factors such as interference, and the presence of other vehicles POINT DETECTION ALGORITHM In this section, a point detection algorithm using time-stamped RSSI data associated with a traveling vehicle is presented. The algorithm presented here utilizes the distance-rssi relationship introduced in section and generates an estimate for the time that a vehicle has just passed an imaginary line on the road that originates from the data collection unit. A sample of data collected from one DCU is shown in Table 3.4. These data represent a sample of MAC addresses collected over a sevenminute period from one of the installed DCUs. Truncated MAC addresses are shown

75 62 in the first column and the timestamps are shown in the second column. For the purposes of this research, the combination of a truncated MAC address and its corresponding timestamp (i.e., date and time) are referred to as detection. The three columns on the left in Table 3.4 show the MAC addresses in the sequence that they were detected by the DCU. The three columns on the right in Table 1 show the same data sorted by MAC address. Nine different MAC addresses were detected over the seven-minute period. A single MAC address may be detected multiple times as it passes the detection zone of a DCU. The antenna attached to the DCU utilized to collect the sample data in Table 3.4 covers a large area of the road (approximately 1,200 feet of the road, 600 feet before and after the DCU). Since a vehicle traveling on this road at a particular speed will be in the length of road covered by the DCU's antenna for couple of seconds, multiple detections of MAC addresses should occur (see Figure 3.8). For the sample data presented in Table 3.4, the speed limit was 45 MPH, which requires an average vehicle 18 seconds to travel the length of the antenna s discovery range. The combined effects of the probabilistic nature of the Bluetooth inquiry procedure, the variations in radio frequency signal strength of the radios of Bluetooth enabled devices, and unknown and uncontrollable factors affecting radio frequency communications (e.g., interference, multi-path) explain why active Bluetooth devices in different passing vehicles moving at the same speed may be detected a different number of times. To facilitate the description of issues related to locating vehicle's location based on MAC address data, the concept of a group of MAC addresses will be defined and

76 63 illustrated. A group will be defined as a collection of MAC address detections for the same MAC address that represent one trip (in a single direction) of the corresponding Bluetooth-enabled device through the length of road covered by the DCU's antenna along the road that it is monitoring. For example, the trip illustrated in Figure 3.8 shows a group size of 3. Figure 3.8. Multiple detections within the DCU's detection zone

77 64 Table 3.4 Sample of MAC Address Data from an Installed Bluetooth DCU MAC Addresses in Sequence MAC Add. Date Time 2:83:AC:3 6/26/ :00:09 0:D1:BA:6 6/26/ :00:13 0:D1:BA:6 6/26/ :00:21 5:F9:FB:8 6/26/ :00:53 5:F9:FB:8 6/26/ :00:57 A:5E:22:8 6/26/ :00:57 5:F9:FB:8 6/26/ :01:02 5:F9:FB:8 6/26/ :01:06 5:F9:FB:8 6/26/ :01:10 5:F9:FB:8 6/26/ :01:14 5:F9:FB:8 6/26/ :01:19 A:CC:9B:5 6/26/ :01:27 A:CC:9B:5 6/26/ :01:31 A:CC:9B:5 6/26/ :01:47 A:CC:9B:5 6/26/ :01:51 A:5E:22:8 6/26/ :01:59 A:5E:22:8 6/26/ :02:15 C:2B:BD:0 6/26/ :03:06 9:C0:9B:2 6/26/ :03:10 C:2B:BD:0 6/26/ :03:10 9:C0:9B:2 6/26/ :03:15 C:2B:BD:0 6/26/ :03:15 C:21:14:6 6/26/ :04:33 C:21:14:6 6/26/ :04:37 C:21:14:6 6/26/ :04:41 8:6F:2D:F 6/26/ :05:32 A:5E:22:8 6/26/ :06:08 A:5E:22:8 6/26/ :07:02 MAC Addresses Sorted MAC Add. Date Time 0:D1:BA:6 6/26/ :00:13 0:D1:BA:6 6/26/ :00:21 2:83:AC:3 6/26/ :00:09 5:F9:FB:8 6/26/ :00:53 5:F9:FB:8 6/26/ :00:57 5:F9:FB:8 6/26/ :01:02 5:F9:FB:8 6/26/ :01:06 5:F9:FB:8 6/26/ :01:10 5:F9:FB:8 6/26/ :01:14 5:F9:FB:8 6/26/ :01:19 8:6F:2D:F 6/26/ :05:32 9:C0:9B:2 6/26/ :03:10 9:C0:9B:2 6/26/ :03:15 A:5E:22:8 6/26/ :00:57 A:5E:22:8 6/26/ :01:59 A:5E:22:8 6/26/ :02:15 A:5E:22:8 6/26/ :06:08 A:5E:22:8 6/26/ :07:02 A:CC:9B:5 6/26/ :01:27 A:CC:9B:5 6/26/ :01:31 A:CC:9B:5 6/26/ :01:47 A:CC:9B:5 6/26/ :01:51 C:21:14:6 6/26/ :04:33 C:21:14:6 6/26/ :04:37 C:21:14:6 6/26/ :04:41 C:2B:BD:0 6/26/ :03:06 C:2B:BD:0 6/26/ :03:10 C:2B:BD:0 6/26/ :03:15 In the data shown in Table 3.4, the smallest time interval observed between detections with the same MAC address is four seconds because the DCUs were programmed to repeat the initiation of an inquiry procedure that is four seconds in length to cover the entire Bluetooth frequency spectrum and hence, detect all Bluetooth-enabled devices nearby. Table 3.4 also shows different MAC addresses

78 65 that have the same timestamps (e.g., 9:C0:9B:2 and C:2B:BD:0). These MAC addresses were detected in the same inquiry period, and represent either multiple devices in the same vehicle or multiple vehicles with active devices passing the reader at about the same time. For the purpose of identifying those MAC address detections that correspond to when the vehicle was the closest to the imaginary line originating at a DCU, Figure 3.9 shows an example in which a vehicle was detected three times passing a DCU (shown as location numbers 1, 2, and 3). In this example, at the timestamp of the MAC address detected at location number 2 the vehicle was closest to the DCU. For vehicles that arrive at an intersection and have to wait for the traffic signal to change, their number of MAC address detections (or group size) can grow rapidly. This can result in large errors when calculating traffic performance measures, such as travel time samples, when an inappropriate timestamp is chosen. Figure 3.9 Schematic representation of multiple detections in a single trip forming a group.

79 66 As explained before, the method developed in this research to select a single MAC address detection is based on the RSSI. The RSSI is known to be correlated with distance, where a larger RSSI value indicates that the DCU and Bluetooth device are closer together than a lower RSSI value (Awad et al., 2007; Kotanen et al., 2003; Pei et al., 2010). Although RSSI is correlated with distance, it is also affected by the movement of the Bluetooth mobile device. In theory moving toward and away from the DCU can generate Doppler effect, which in some cases can reduce the signal strength level received at the DCU. Multi-path is another propagation phenomenon that results when radio signals reaching the receiving antenna come from two or more paths. This phenomenon can have both constructive and destructive effects on the signal strength. Modeling the effect of these phenomena is complex and beyond the scope of this research, however it is accounted for indirectly by adding the noise adjustment to the data in Figures 3.5, 3.6, and 3.7. For example, in Figure 3.9 a device that is stationary at location 2 will often generate a MAC address detection with a higher RSSI than when the device is at location x, but in movement. For DCUs located at signalized intersections, this implies that using the MAC address detection with the highest RSSI is often not the detection that represents the time the vehicle was closest to the DCU. For a DCU located at a signalized intersection, the method used to identify the MAC address detection representing the time when the vehicle is closest to the DCU is based on the rate of change in RSSI values between consecutive MAC address

80 67 detections within a group. In this approach, the MAC address detection selected is that detection after which the subsequent RSSI values decrease at the highest rate. This rate of change can be obtained using following equation 3.7: ୳ ୬୲ ୴୧୭୳ୱ ୳ ୬୲ ୧୫ ୱ୲ୟ୫୮ ୴୧୭୳ୱ ୧୫ ୱ୲ୟ୫୮ Equation 3.7 In those cases where this decreasing rate of change is not observed, the last MAC address detection in a group is saved. Often, the MAC address detection selected also has the largest RSSI value. Intuitively, the method described attempts to detect the time that a vehicle has just started to leave the intersection after passing the DCU. Figure 3.10 shows a real example of RSSI data over time for multiple reads of the same Bluetooth-enabled devices during a single probe vehicle trip past a DCU. In this example, a probe vehicle carrying two Bluetooth-enabled cell phone devices approached an operating DCU at the signalized intersection of SW Durham Rd and Highway 99W. The probe vehicle stopped for a while at this intersection and then left the discovery area of the DCU. The dashed line represents the manually recorded time that corresponds to when the probe vehicle just passed the imaginary line perpendicular to the road, extending from the DCU. In Figure 3.10, the most accurate timestamps are identified by the larger squares for both cell phones. These timestamps represent the time the probe vehicle started to leave the intersection and are characterized by a rapid decrease in the RSSI levels after these points.

81 68 Figure RSSI values over time for two devices in a probe vehicle arriving and waiting at an intersection VALIDATION EXPERIMENTS A series of experiments were conducted to test the accuracy of the proposed point detection system in three different environments. The purpose of these experiments was to assess differences between the time stamp of the automatically selected record (utilizing the point detection algorithm presented in section 3.3.2), and the time the vehicle crosses an imaginary line extending from the DCU antenna across and perpendicular to the road. A schematic of the general physical experimental setup is presented in Figure 3.9. A DCU and antenna are installed at some location adjacent to the roadway being monitored (point A in Figure 3.9). The distance d, from point A in

82 69 Figure 3.9 and the roadway will vary depending on the available mounting structures at the location where the DCU is placed. In these experiments, a probe vehicle containing active Bluetooth devices with known MAC addresses travels past a DCU multiple times. At the same time there is a person operating a laptop computer that is communicating with the DCU. The communication of the DCU and laptop computer permits synchronization of the DCU time and laptop time. Software is provided to help the laptop operator record the exact time that a vehicle passes the DCU (crosses line AB in Figure 3.9). When the front of the vehicle just passes line AB in Figure 3.9, the operator will press the Enter key on the laptop keyboard. When this key is pressed a time stamp will be automatically generated and stored in a file. If feasible in a particular test environment the vehicle will be driven past the DCU at a fixed speed. Different speeds can be examined to see if speed has an effect on the differences between the timestamp of the selected record, and the time the vehicle passes the DCU. The DCU will be scanning continuously during the experiment. Knowing the time that the vehicle passed the DCU and assuming a constant travel speed, the time that the vehicle was a specific distance from the DCU can be computed. For example, the time that the vehicle is at points 1, 2, and 3 in Figure 3.9 can be computed from the time the vehicle passed the DCU (point x). The distance that each point is from point x (d1, d2, d3) is known. By matching the MAC address timestamps to various locations, it is possible to map RSSI values to different vehicle locations.

83 70 The first test environment used for validation was a low traffic, free flowing rural road near Corvallis, Oregon (Camp Adair Road adjacent to EE Wilson Wildlife Area Figure 3.11). Due to low traffic volumes it was possible to drive at various constant speeds past the DCU. The probe vehicle traveled passed the DCU 30 times (15 times traveling east and 15 times traveling west) for each speed tested. The speeds tested were 25, 35, and 45 mph. The DCU was located 36 feet from the road and the antenna was mounted on a tripod at a height of 70 inches. Two different cell phones with Bluetooth communications active were used in the tests. The responses computed from the recorded data were the time differences between the MAC address records with the highest RSSI reading and the times the vehicle crossed the line drawn from the DCU perpendicular to the road. A histogram of all the test results is shown in Figure Most time differences are less than two seconds, with a maximum difference of 7.5 seconds. The average time difference was 0.23 seconds, with a standard deviation of 1.37 seconds. Negative time differences occur when the time stamp of the highest RSSI record occurs after the vehicle passes the DCU.

84 71 Figure DCU installation on Camp Adair Road, Benton County Figure Histogram of the difference between the time the probe vehicle passed the DCU on Camp Adair Road and the MAC address record with the highest RSSI

85 72 The second test environment was Wallace Road, which is located on the west side of the city of Salem, Oregon. This road is also a free-flowing road with no signals located near the DCU, but a single signal between the DCU locations. The Oregon Department of Transportation (ODOT) Intelligent Transportation Systems (ITS) Unit operates a test site on this road located at latitude-longitude of N and W (see Figure 3.13). Wallace Road runs north and south with two lanes in both directions, and the speed limit is 45 MPH. Forty total probe vehicle runs (20 traveling northbound and 20 traveling southbound) were made past the DCU, with two different cell phones with active Bluetooth communications present in the vehicle. The responses computed from the recorded data were the time differences between the MAC address records with the highest RSSI reading and the times the vehicle crossed the line drawn from the DCU perpendicular to the road. A histogram of all the test results is shown in Figure Most time differences are less than two seconds, with a maximum difference of five seconds. The average time difference was 0.23 seconds (the same as for Camp Adair Road) with a standard deviation of 1.24 seconds. Negative time differences occur when the time stamp of the highest RSSI record occurs after the vehicle passes the DCU.

86 73 Figure DCU Location on Wallace Road, Salem, Oregon Figure Histogram of the difference between the time the probe vehicle passed the DCU on Wallace Road and the MAC address record with the highest RSSI

87 74 The third test environment was along Highway 99W in Tigard, Oregon. Five DCUs have been installed at five different intersections (see Figures 3.15 and 3.16). Figure Southernmost DCU locations on Highway 99W, Tigard Oregon

88 75 Figure Southernmost DCU locations on Highway 99W, Tigard Oregon The Highway 99W tests were conducted at DCU 12 which is located at a signalized intersection. Forty total probe vehicle runs (20 traveling northbound and 20 traveling southbound) were made past the DCU, with two different cell phones with active Bluetooth communications present in the vehicle. Two responses were recorded and analyzed. The first response computed from the recorded data were the time differences between the MAC address records with the highest RSSI reading and the times the vehicle crossed the line drawn from the DCU perpendicular to the road. This is the same response as used in the prior test environments, which were both free-flowing

89 76 roads. A histogram of all the test results is shown in Figure Most time differences are less than two seconds, with a maximum difference of 43 seconds. The average time difference was 3.1 seconds with a standard deviation of 9.9 seconds. The results were similar to the other test environments except for five test runs where the response was greater than 11 seconds (42, 41, 18, 12, and 12 seconds). With these five responses removed, the average, maximum, and standard deviation of the response are seconds, 3.3 seconds, and 1.3 seconds respectively. In those instances where large time differences were observed, the probe vehicle was stopped by the signal and stationary. The stationary position of the probe vehicle was one or two vehicle lengths before the line drawn from DCU 12 perpendicular to the road. When stationary, yet close to the DCU, higher RSSI readings were obtained than when the probe vehicle was moving across the line drawn from the DCU. In this case, the MAC address record with the highest RSSI reading occurred when the probe vehicle was close in distance to the line drawn from the DCU, but still relatively far with respect to time since the vehicle was waiting for a green light to proceed. As seen in Figure 3.12, all of the relatively large time differences are positive differences, which occur when the MAC address with the highest RSSI reading occurs before the probe vehicle passes the line drawn from the DCU. Negative time differences occur when the time stamp of the highest RSSI record occurs after the vehicle passes the DCU. In such cases as just described the accuracy of the travel time samples generated will be reduced. The time interval between when the highest RSSI reading was recorded and when the vehicle passed the DCU will be added to

90 77 the travel time along the road section that occurs after the signal. Similarly, this time difference will not be included in the travel time for the road section occurring before the signal. Figure Histogram of the difference between the time the probe vehicle passed DCU 12 on Highway 99W and the MAC address record with the highest RSSI The second response recorded and analyzed is based on a method to eliminate these occasional large errors caused when a vehicle stops just before passing the DCU. In this method the single record selected from a group of MAC addresses is the record after which the subsequent RSSI values decrease at the highest rate. In those

91 78 cases where this decrease is not observed the last record in a group is saved. Since the highest RSSI record can often be when a vehicle stops close to, but before passing the DCU, the RSSI values should also decrease rapidly once a vehicle passes the DCU after the vehicle has a green signal. Figure 3.10 showed the time stamps and associated RSSI for two Bluetooth devices (two cell phones) when a vehicle stops and waits close to the DCU as it travels through the intersection. The large circles represent the time stamps associated with the highest RSSI. Since the vehicle stops near the DCU the time stamps which are associated with the highest RSSI are considerably earlier than the time when the vehicle passes the DCU. The responses computed using this method were compared to the times the probe vehicle crossed the line drawn from the DCU perpendicular to the road. A histogram of all the test results is shown in Figure Most time differences are less than two seconds, with a maximum difference of 21 seconds. The average time difference was 2.2 seconds with a standard deviation of 4.1 seconds. The performance is more accurate and consistent than saving the record with the highest RSSI value.

92 79 Figure Histogram of accuracy of the time stamp before the RSSI values rapidly decrease

93 80 4 APPLICATION CASE STUDIES The Bluetooth-based vehicle point detection system introduced in this research can be employed to collect vehicle movement data in different areas of the transportation system. The vehicle movement data collected can then be utilized to estimate traffic performance measures for analysis and planning studies. In this section, two particular applications of the Bluetooth-based vehicle point detection system are presented. In these applications, vehicle movement data are utilized to derive two commonly used traffic performance measures: intersection delay and travel time. The most common current data collection methods employed to collect data to estimate these performance measures are expensive and, in some cases, inaccurate (Bonneson, & Abbas, 2002; Middleton et al., 2009; Balke et al., 2005) and labor intense. In contrast, the data collection method developed in this research provides a low-cost solution to estimate these performance measures with high levels of accuracy. 4.1 TRAVEL TIME STUDY The use of time-stamped media access control (MAC) address data acquired from Bluetooth-enabled devices to collect travel time data has received significant attention in recent years. However, past research has mainly focused on the application of Bluetooth technology to obtain travel time data on free flowing roads. A smaller amount of research has addressed the use of Bluetooth-based data collection systems

94 81 on arterial roads and, in particular, for the collection of travel times between signalized intersections where the travel time accuracy has been questionable. The point detection system presented in Chapter 3 was utilized to develop a methodology to collect accurate travel time data between signalized intersections using a Bluetooth-based data collection system. The proposed RSSI-based travel time data collection method can be implemented using any wireless technology that provides a unique identification number to distinguish between different mobile devices and an associated signal strength measurement during the wireless communication process. The results of this research have been attested with a Bluetooth-based data collection system that consists of five DCUs permanently installed at consecutive signalized intersections along an urban, high-volume signalized arterial (i.e., Highway 99W) running north-south in Tigard, OR. This system was introduced and used earlier in Chapter 3 to perform a timestamp selection accuracy study. These DCUs are located at intersections because of the availability of power and access to a communications network through signal control cabinets. Figure 4.1 shows the five consecutive signalized intersections (approximately one mile apart from each other) where DCUs are installed, i.e., Durham Rd, McDonald St, Johnson St, 217 NB ramp, and 64th Ave.

95 82 Figure 4.1. Location of Bluetooth DCUs on Highway 99W (Tigard, OR) GENERATION OF TRAVEL TIME SAMPLES USING MAC ADDRESS DATA To begin the discussion of travel time sample generation, the specific road segment of interest must be defined. In this research, the road segments for which travel time samples are desired start at an imaginary line extending from a roadside DCU across and perpendicular to the road, and end at a similar line drawn from another DCU at a different location along the same road. Travel time samples are computed as the time difference between detections of the same MAC address at each DCU. This research focuses on the case when these roadside DCUs are installed at signalized intersections

96 83 located on arterial roads. Ground truth travel times for a specific vehicle will be defined as the time difference between when the vehicle crosses the previously defined imaginary lines (determined and recorded by an observer inside the probe vehicle). All travel time sample accuracy results are relative to the ground truth travel times TRAVEL TIME SAMPLE GENERATION SUPPLEMENTED WITH RSSI DATA Based on the definition of a road segment introduced earlier, the most accurate travel time samples generated from MAC address data will utilize those MAC address detections that correspond to when the vehicle was the closest to the imaginary line originating at a DCU. As explained in section 3.3.2, the method developed in this research to select a single MAC address detection from the group is based on the RSSI. The method used to identify the MAC address detection representing the time when the vehicle is closest to the DCU is based on the rate of change in RSSI values between consecutive MAC address detections within a group. In this approach, the MAC address detection selected is that detection after which the subsequent RSSI values decrease at the highest rate. In those cases where this decreasing rate of change is not observed, the last MAC address detection in a group is saved. Often, the MAC address detection selected also has the largest RSSI value. Intuitively, the method

97 84 described attempts to detect the time that a vehicle has just started to leave the intersection after passing the DCU. An experiment was conducted on Highway 99W in Tigard, Oregon, to assess differences between travel times calculated using time-stamped MAC addresses associated with the first detection, last detection, and average of the first and last detections in a group, and travel times calculated with time-stamped MAC address detections selected utilizing RSSI data presented in section The experimental data were collected from a probe vehicle containing two active Bluetooth devices (cell phones 1 and 2) with known MAC addresses that traveled past the five DCUs installed on Highway 99W multiple times. A laptop computer was used in the experiment to facilitate the collection of manual travel times. An observer pressed the Enter key on the laptop when the vehicle passed the DCUs to instruct a software application to automatically generate and store a timestamp in a file. The DCUs scanned continuously during the experiment. A total of 20 probe vehicle runs (10 traveling northbound and 10 traveling southbound) were made past all five DCUs. Adjacent signalized intersections on Highway 99W were paired to form a total of four road segments with an average length of one mile. For each probe vehicle run on each defined segment, four different travel time samples were generated utilizing the various methods for selecting MAC address detections form a group. Next, the calculated travel times were compared against the "ground truth" travel times collected. The absolute difference between the calculated travel time samples and "ground truth" travel times were recorded as the error for each travel time calculation

98 85 method. Table 4.1 summarizes the errors for the calculated travel time samples using different timestamp selection approaches for cell phone 1, for the road segment between Johnson St and the 217 NB Ramp. Table 4.1 Cell Phone 1 Sample Probe Vehicle Test Results for the Road Segment between Johnson St and the 217 NB Ramp Non RSSI-based "Ground Absolute Error Relative to RSSI Methods Truth" "Ground Truth" Travel Times Metho Firs (F+L)/ Travel (F+L)/ RSSI Run Last d First Last t 2 Times 2 Method 1 1:28 1:35 1:31 1:25 1:24 0:04 0:11 0:07 0:01 2 2:25 1:48 2:06 1:49 1:49 0:36 0:01 0:17 0:00 3 2:12 2:12 2:12 2:03 2:03 0:09 0:09 0:09 0:00 4 2:49 1:36 2:12 1:39 1:35 1:14 0:01 0:37 0:04 5 1:51 3:01 2:26 2:51 2:53 1:02 0:08 0:27 0:02 6 2:38 1:34 2:06 1:37 1:33 1:05 0:01 0:33 0:04 7 1:41 2:59 2:20 2:29 2:29 0:48 0:30 0:09 0:00 8 2:58 2:45 2:51 2:39 2:40 0:18 0:05 0:11 0:01 9 1:58 2:56 2:27 2:47 2:46 0:48 0:10 0:19 0: :41 2:02 2:52 1:56 1:54 1:47 0:08 0:58 0: :51 1:43 1:47 1:42 1:40 0:11 0:03 0:07 0: :42 1:40 1:41 1:34 1:34 0:08 0:06 0:07 0: :46 2:33 2:39 2:41 2:40 0:06 0:07 0:01 0: :02 1:40 1:51 1:38 1:36 0:26 0:04 0:15 0: :03 2:03 2:03 1:56 1:53 0:10 0:10 0:10 0: :34 2:29 2:31 2:26 2:25 0:09 0:04 0:06 0: :44 1:48 1:46 1:39 1:38 0:06 0:10 0:08 0: :46 2:48 2:47 2:43 2:42 0:04 0:06 0:05 0: :55 2:05 2:00 1:53 1:54 0:01 0:11 0:06 0: :58 3:04 3:01 3:03 3:03 0:05 0:01 0:02 0:00 AVG (sec): STDV (sec):

99 86 The test results presented in Table 4.1 clearly show that the travel time samples obtained with the RSSI-based method are significantly more accurate and precise than those obtained with any other method. For this example road segment, the average travel times generated using the RSSI-based method had an average error of 1.35 seconds compared to the "ground truth" travel times recorded. On the other hand, the travel times generated using first-to-first MAC address timestamps (i.e., the most common approach cited in the literature to obtain travel time samples) had an average error of seconds, which is significantly higher than the calculated error for the proposed method. Table 4.2 summarizes the test results for all four road segments. Table 4.2 Average Absolute Travel Time Sample Errors in Seconds for Different Travel Time Calculation Approaches Segment Durham Rd McDonald St McDonald St Johnson St Johnson St 217 NB ramp 217 NB ramp 64th Ave Average Cell Phone First-to-First Last-to-Last Average-to- Average RSSI Method (13.12%) 8.90 (5.97%) (7.68%) 2.65 (1.78%) (8.76%) 4.75 (3.19%) 7.70 (5.17%) 3.15 (2.11%) (6.29%) 6.10 (4.49%) 5.90 (4.34%) 3.05 (2.24%) (4.49%) 5.37 (3.95%) 5.32 (3.91%) 3.53 (2.59%) (21.93%) 7.30 (5.75%) (11.57%) 1.35 (1.06%) (12.35%) 3.84 (3.03%) 7.90 (6.22%) 2.68 (2.11%) (23.48%) 5.75 (4.08%) (11.35%) 1.50 (1.06%) (16.72%) 2.95 (2.09%) (8.62%) 2.95 (2.09%) (16.20%) 7.01 (5.07%) (8.74%) 2.14 (1.54%) (10.58%) 4.23 (3.06%) 8.27 (5.98%) 3.08 (2.23%) All (13.39%) 5.62 (4.07%) (7.36%) 2.61 (1.88%)

100 87 A series of paired t-tests were performed to check if there is indeed a statistical difference between the error in the travel time samples calculated from the first-tofirst, last-to-last, and average-to-average travel time calculation methods, when compared to the error in the travel time samples obtained with the RSSI-based method. The results show that significant differences exist (with a maximum p-value of 0.006) between the RSSI-based method and each of the other methods, for both test cell phones on all four road segments. Additionally, a series of Pitman-Morgan tests for equal variance between the RSSI-based method and the other methods were conducted. This test is appropriate for paired data. Of the 24 tests conducted, 16 rejected the null hypothesis of equal variance at a 95% significance level. The tests where the null hypothesis was not rejected were mostly with the last-to-last method, which was the second most accurate. The aggregated test results in Table 4.2 for both Bluetooth-enabled cell phones tested over all road segments suggest that the travel time samples generated with the RSSI-based method are significantly better (i.e., have less error) than the travel time samples calculated by utilizing the first-to-first, last-to-last, and average-to-average methods. Among the methods used in the literature, the travel time samples calculated using the last-to-last detection timestamps are better than first-to-first and average-toaverage. This makes sense since the last detection timestamps are closer to the time that a particular vehicle has left the intersection. Due to the wait time delay that vehicles experience at signalized intersections, the first-to-first approach results in poor accuracy.

101 88 Travel times between the DCUs available for use in this research were collected continuously from June 9, 2011 through February 29, 2012, and from May 7, 2012 through May 29, Table 4.3 shows the average daily traffic volumes, and the average number of travel time samples collected by the system. Table 4.3. The Volume Of Travel Time Samples Generated Compared To Traffic Volume Road Segment DCU 9 -DCU 10 DCU 10 -DCU 11 DCU 11 -DCU 12 DCU 12 -DCU 13 Travel Direction Avg. Daily # of Travel Time Samples Avg. Daily Traffic Volume Avg. # Travel Times/Avg. Traffic Vol. North % South % North % South % North % South % North % South % 4.2 INTERSECTION PERFORMANCE This section evaluates the application of the proposed Bluetooth-based vehicle point detection system to acquire travel times for vehicles approaching and leaving an intersection. The travel time data samples collected at the intersection also include the time that it takes for a vehicle to interact with the control mechanism at the intersection. This additional time duration introduces some delay to the traffic passing through an intersection. The validity of the approach was investigated through an experiment conducted at an intersection with large amounts of pedestrian and cyclist

102 89 traffic relative to vehicle traffic. The test location was at the intersection of NW Monroe Ave and NW Kings Blvd near the Oregon State University campus in Corvallis, Oregon (Figure 4.2). The test was completed on a Friday, during noon rush hour (from 11:00 am to 12:00 pm). This three-way stop-controlled intersection has several businesses in the vicinity and experiences high/medium levels of traffic in different modes, including passenger vehicles, buses, pedestrians and bicyclists. The frequent occurrence of different travel modes introduces a very high level of complexity to the system since active Bluetooth devices are present in all travel modes. This makes the test intersection ideal for the purposes of this study since it represents one of the more complicated environments where such a system may be deployed. Figure 4.2. Test Setup Utilized in the Intersection Control Delay Experiment