TRAFFIC-1, USER TAILORED MEASURING SYSTEM OF ROAD TRAFFIC PARAMETERS. AGH - University of Science and Technology Cracow, Poland.

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1 TRAFFIC-1, USER TAILORED MEASURING SYSTEM OF ROAD TRAFFIC PARAMETERS P.BURNOS J.GAJDA Z. MARSZALEK P. PIWOWAR R. SROKA M.STENCEL T.ZEGLEN AGH - University of Science and Technology Cracow, Poland. Abstract A new prototype system of road traffic parameters measurement Traffic-1 is been presented in this paper. Innovativeness of the solution is manifested in the structure of the system that can be tailored by the user adequately to current measurement needs and in the implemented sophisticated algorithms of signals processing. The paper contains a brief description of constructed system with particular focus on the used innovations that are the result of many years of research work of the authors. Keywords: Road traffic parameters measurement, weigh in motion systems, vehicle classification. Résumé Ce document présente un prototype de système de mesure des paramètres de trafic Trafic-1. Le côté innovant de ce système se manifeste dans sa structure qui peut être modifiée par l utilisateur en fonction des besoins de mesure actuels et des algorithmes de traitement du signal utilisé. Le document contient une brève description du système et met un accent particulier sur les innovations qui sont le fruit du travail de plusieurs années des concepteurs du système. Mots-clés: Mesure des paramètres de trafic, pesage en marche des véhicules, classification des véhicules.

2 1 - Traffic-1 System Structure Traffic-1 system (see Figure 1) has been designed for a wide range of users with varied expectations concerning the applied sensors and measured traffic parameters (Ezell, 2010). The system is portable, and its structure as well as the set of used sensors may be easy tailored by user according to current needs. It allows application of this system on different measurement sites equipped with different kinds of measuring sensors. The structure of the system is open and consists of the central unit and interchangeable signals conditioning modules. The system is equipped with nine modules, what means that it can fulfil the functions of nine different measuring systems. Each module is purposed to work with different configuration of road traffic sensors. The system recognises its configuration automatically and activates an adequate data processing algorithm. Figure 1 - Traffic-1 system with interchangeable signals conditioning modules The following configurations of measuring sensors (Klein, 2001) are acceptable by system: A - single inductive loop, B dual-loop detector (speed trap), C- dual-loop detector and single axle detector, D - dualloop detector and single polymer load sensor, E - dual-loop detector and single quartz load sensor, F three inductive loops array (it contains 2 narrow loops as wheel detectors), G single inductive loop and 2 strip axle detectors, H single inductive loop and 2 polymer load sensors, I single inductive loop and 2 quartz load sensors. Depending on the configuration (A I), the system enables measuring the following vehicle parameters: T - time of arrival in the measurement zone, V - vehicle velocity, L - vehicle length, N axle - number of axles, L axle - distance between successive axles, Trailer - trailer presence, Axle load - loads of individual axles, Total mass - total mass of a vehicle moving with speed between 30km/h up to 80km/h (19 mph up to 50 mph), Cl. magnet - vehicle class based on magnetic profile, Cl. ALT - vehicle class based on the number of axles and the distance between them (different classification schemes are possible, e.g. FHWA F or ALT) (Gajda, et al., 2011). Furthermore, the system enables estimation of the following road traffic characteristics (Maerivoet, 2005): k - traffic density, q - flow of vehicles, ρ traffic lane occupancy, V mean mean speed. Change of the type, number and configuration of the sensors not only affect number of measured parameters, but also in many cases it influences measurement uncertainty (see Table 1). The system has been equipped with internal memory able to storied 150 thousand vehicles records. Touchscreen LCD enables controlling the system performance and real time visualisation of measurement results. Interface RS232 and a GSM modem have been chosen for communication. Both interfaces enable transmission of

3 data to an external computer or ftp server. The device has the capability of automatic detection of specific sensor fault operation. Table 1 - Uncertainty of Measurement of Road Traffic Parameters in Traffic-1 System T [s] V [km/h] L [m] N axle L axle [m] Trailer Axle load [N] Total m. [kg] Cl. mag. Cl. ALT A 0.01 s 6-23% 20% x x x B 0.01 s 1.5% * 2% * x x x C 0.01 s 1.5% * 2% * 2% x x x D 0.01 s 1.5% * 2% * x 2% x 20-30% 15-20% x x E 0.01 s 1.5% * 2% * x 2% x 15-25% 15-20% x x F 0.01 s 7.5% 8% 8% 10% x x x G 0.01 s <1km/h 2% * x ±2.5 cm x x x H 0.01 s <1km/h 2% * x ±2.5 cm x % 10-15% x x I 0.01 s <1km/h 2% * x ±2.5 cm x 10-15% 7-10% x x NOTE: 1km/h = 0.62mph, 1cm = 0.39in, * - in the sense of standard deviation, - not available, x available. 2 - Discussion The system utilises a range of innovative hardware and software solutions, described below, that increase functionality and reliability of the device. 2.1 Algorithm of Speed Estimation in a System equipped with a single inductive Loop In classic ITS systems using axle detectors, vehicle speed measurement requires installing two sensors in the road pavement, in a specific distance, e.g. dual-loop detector, two polymer axle detectors or load sensors. Traffic-1 system enables estimation of speed on the basis of a vehicle magnetic profile signal obtained from single inductive loop, what significantly reduce the cost of the whole system. The method was proposed in 1997 in the study (Gajda, et al., 1997), and also developed by other authors, e.g. (Ritchie et al., 1999). In (Wang et al., 2003) authors present algorithm of speed estimation and vehicle-classification which based on signals from single inductive loop. The algorithm used in Traffic-1 system is illustrated by the dependence given by equation (1) and in Figure 2. k, q, r V mean normalised amplitude [-] 1,0 0,8 0,6 0,4 0,2 inductive loop signal ΔU 0,0 0,00 0,04 0,08 0,12 Δt n time [s] Figure 2 - Example of signal from inductive loop and estimation of the rise time with the method of two levels

4 V = a where: V - vehicle speed estimation, Δ t n ΔU + 1 b 1 Δtn - rise time, Δ U - difference between predefined levels, a 1,b 1 - constant coefficients, specified at the stage of calibration of the algorithm. Example results of speed measurement in a system with single inductive loop in relation to a double sensor reference system have been presented in figure 3. (1) speed estimate [km/h] reference characteristic estimation results vehicle speed [km/h] Figure 3 - Speed estimate of passenger vehicles specified according to algorithm (1) in the function of vehicle speed Axle Detection on the Basis of the magnetic Profile The common method used to measure number of axles and mutual distances between them uses system cooperating with piezoelectric, resistance, fibre optic or other axle detectors sensors (Klein, 2001). Systems cooperating with inductive loops for this purpose are rarely in use (Sun 2000), (Gajda et al., 2011), (Oh et al., 2007), (Diamond, 2010). An important advantage of using loop sensors is significantly lower cost in comparison with other axle sensors. The Traffic-1 system uses two narrow (10 cm) and one wide loop sensors with standard dimensions. Narrow loop sensors are used as axle detectors. Example signals from a narrow loop sensor have been presented in Figure 4. Some lorries are able to lift one or two axles. In such cases systems equipped with typical detectors give incorrect results of counting axles. Systems equipped with inductive loop sensors do not have this disadvantage and count correctly even those axles that are not in contact with the road pavement. The developed method is a subject in patent proceedings and is presented in details in (Gajda et al., 2011).

5 6 analogue signal digital signal 5 amplitude [V] ,0 0,2 0,4 0,6 0,8 time [s] Figure 4 - Example of analogue and digital signals from a conditioning system cooperating with a narrow inductive loop 2.2 Autocalibration and Temperature Correction of Weighing Results One of the major reasons of high uncertainty of weighing results in Weigh in Motion (WIM) systems equipped with polymer (piezoelectric) sensors installed in the road pavement is non stationarity of the system. This phenomenon is caused by changes of pavement properties under the influence of temperature changes and has special significance in case of the polymer load sensors used (LPTT, 2010). It has been verified experimentally that changes of weighing results caused by the daily temperature cycle can reach 40% (Burnos, 2008). Authors developed two methods that enable limitation of the influence of this phenomenon on the accuracy of the results i.e. autocalibration and temperature correction of weighing results. The concept of autocalibration consists in permanent estimation of the calibration coefficient C of the WIM system and modification of weighing results according to the current estimation result: () i y() i y s = 1 (2) C where: y s () i calibrated result of weighing the i-th vehicle i.e. estimation of total mass of the vehicle or static load of a selected axle, y () i non-calibrated result of weighing the i-th vehicle i.e. result of processing of the load signal from the WIM system sensors. A necessary condition that enables WIM system autocalibration is occurrence of so-called reference vehicles in the stream of vehicles moving through the calibrated WIM site. These vehicles are regular road traffic participants and are distinguished by arrangement of three semi-trailer axles in mutual distances of 131 cm, which significantly facilitates their identification, as well as small relative random variability of the first axle load (std=10%), with mean value μ 0 =61677 N. It means that the load of this axle in small degree depends on the carried load, and can be used as a reference value during autocalibration of the system. For the calibration coefficient calculation a recursive least squares algorithm with modified exponential forgetting factor has been used (Burnos, 2008). It has been verified experimentally that the use of the autocalibration method in a non stationary system reduces uncertainty of weighing results by five times. Temperature correction method requires the model of temperature characteristics of the WIM site and asphalt temperature measurement. Such model can be specified using the results of weighing the first axle of reference vehicles over the long time period. Figure 5 presents experimentally specified temperature

6 characteristics within the range from -10 to 30 C. The data has been registered from November 2005 until January 2008 at WIM site in Gardawice, south Poland (Burnos et al., 2007). In this time (with short pauses for system maintenance) over 100 thousand weighting results of reference vehicles has been recorded. Each point in Figure 5 (measurement data) is a mean value of weighing the first axle of thousands of reference vehicles in a selected temperature. relative mean result of weighing the first axle [-] 1,4 1,3 1,2 1,1 1,0 0,9 0,8 measurement data adjusted model asphalt temperature [ o C] Figure 5 - Temperature characteristics of WIM system within the range from -10 to 30 C Experiments have proved that the correction method makes possible even 4-fold reduction of uncertainty of weighing results in non stationary systems (Burnos 2008). 2.3 Alternative Automatic Vehicle Classification Method In system Traffic-1 it is possible to select one of the implemented algorithms of automatic vehicle classification: FHWA or ALT (Burnos, 2010). In comparison with other methods, ALT classification algorithm is distinguished by universality resulting from the open structure of vehicle classification scheme, as well as the use of fuzzy sets and data fusions in the identification algorithm. The classification is based on the elementary group of components (motorbike, car, delivery vehicle, lorry, tractor, trailer, semi-trailer, bus) out of which the user can build any number of vehicle categories. Due to that the method is characterised by high selectivity and flexibility because the number and type of vehicle categories can be adjusted to the nature of traffic in a given area. For example, 20 categories of vehicles most frequently occurring on Polish roads were created using elementary group of components (8 categories of single vehicles, 6 categories of vehicles combination, 6 categories of articulated vehicles). The functional basis of the identification algorithm is the measurement of parameters characterising the vehicle, such as: number of axles, axle spacing and vehicle length that constitute so-called vector of characteristic parameters. Decision on classification of a vehicle in a proper category is made by means of comparison of the characteristic vector value with the vector that is the model of this category. The vehicle categories models in ALT classification (unlike classic solutions) have been built on the basis of fuzzy sets. Experimental verification of the algorithm implemented in Traffic-1 system was conducted on the basis of 1097 recorded vehicles. The result of the automatic classification was compared with the result of the visual specification of vehicle types, and the ratio of correctly classified vehicles to the total number of test vehicles in a given category was assumed as a measure of effectiveness. Effectiveness of classification of all the vehicles was 95%, while in case of lorries it was 100%. The overall effectiveness of ALT classification is 10% higher than that of FHWA. Furthermore, there were no

7 unclassified vehicles. Simplicity of the method (measuring the distance between axles and vehicle length), its universality and high effectiveness are definitely its advantages. 3 - Conclusion The presented measurement system Traffic-1 is a universal tool for both long-term measurements of road traffic parameters as well as short-term measurements realised within the scope of research works. It can be easily moved between different measurement stations. The developed algorithms enable measuring wide selection of vehicle parameters as well as traffic stream characteristics, at the same time minimising the costs related with the used measurement sensors. More information about Traffic-1 system may be found on References Ezell, Stephen. Intelligent Transportation Systems (2010), The Information Technology & Innovation Foundation. January Klein, Lawrence (2001), Sensor Technologies and Data Requirements fot ITS. Artech House. Gajda, J., et al (2011), Application of inductive loops as wheel detectors, Transport. Res. Part C, (in press). Maerivoet, Sven and De Moor, Bart (2005), Traffic Flow Theory. arxiv.org e-print archive. Janusz Gajda, Ryszard Sroka, Tadeusz Żegleń (2007), Accuracy analysis of WIM systems calibrated using pre-weighed vehicles method. Metrology and Measurement Systems vol. XIV, number 4, pp Gajda Janusz, Ryszard Sroka, Marek Stencel, and Tadeusz Żegleń (1997), Measurement of Road Traffic Parameters Using an Inductive Single-Loop Detector. 9th International Symposium on Electrical Instruments in Industry. Ritchie, Stephen and Sun, Carlos (1999), Individual Vehicle Speed Estimation Using Single Loop Inductive Waveforms. Call No: UCB-ITS-PWP California PATH Working Paper, California. Wang, Yinhai and Nihan, Nancy (2003), Can Single-Loop Detectors Do the Work of Dual-Loop Detectors? Journal of Transportation Engineering Vol. 129, No. 2, pp Sun, Carlos (2000), An Investigation in the Use of Inductive Loop Signatures for Vehicle Classification. Publication UCI-ITS-TS-WP California PATH Research Report. California. Oh, C., Ritchie, S.G., (2007), Recognizing vehicle classification information from blade sensor signature. Pattern Recognition Letters 28, Diamond Consulting Service Ltd. (2010), Essential collection. LTPP (2002), Temperature Prediction and Correction Guide. Burnos, Piotr (2008). Auto-Calibration and Temperature Correction of WIM Systems. Proceedings of the International Conference on Heavy Vehicles, 5th International Conference on Weigh-in-Motion of Heavy Vehicles. Paris. Wiley, pp Burnos, Piotr, Janusz Gajda, Piotr Piwowar, Ryszard Sroka, Marek Stencel, and Tadeusz Żegleń (2007). Accurate Weighing of Moving Vehicles. Metrology and Measurement Systems vol. XIV, number 4, pp Burnos, Piotr (2010). Alternative Automatic Vehicle Classification Method. Metrology and Measurement Systems vol. XVII, number 3, pp