DESIGN PORTABLE SENSORS SYSTEM FOR VEHICLE COUNTING, CLASSIFICATION, AND SPEED MEASUREMENT IN TRAFFIC JUNCTIONS

Transcription

1 DESIGN PORTABLE SENSORS SYSTEM FOR VEHICLE COUNTING, CLASSIFICATION, AND SPEED MEASUREMENT IN TRAFFIC JUNCTIONS M.USHAKIRANMAI 1, G.SAI THIRUMAL 2 1 ushamaddala463@gmail.com 2 saithirumal472@gmail.com 1 PG Scholar, Dept of Ece Aurobindo institute of engineering and technology, sheriguda (v), Ibrahimpatanam Mandal, Rangareddy Dist, Telangana, India. 2 Guide, M.Tech, Associate professor, Dept of Ece Aurobindo institute of engineering and technology, sheriguda (v), Ibrahimpatanam Mandal, Rangareddy Dist, Telangana, India. Abstract This paper focuses on the development of a portable roadside magnetic sensor system for vehicle counting, classification, and speed measurement. The sensor system consists of wireless anisotropic magnetic devices that do not require to be embedded in the roadway the devices are placed next to the road way and measure traffic in the immediately adjacent lane. An algorithm based on a magnetic field model is proposed to make the system robust to the errors created by larger vehicles driving in the nonadjacent lane. These false calls cause an 8% error if uncorrected. The use of the proposed algorithm reduces this error to only 1%. Speed measurement is based on the calculation of the cross correlation between longitudinally spaced sensors. Fast computation of the cross correlation is enabled by using frequency-domain signal processing techniques. An algorithm for automatically correcting for any small misalignment of the sensors is utilized. A high-accuracy differential Global Positioning System is used as a reference to measure vehicle speeds to evaluate the accuracy of the speed measurement from the new sensor system. The results show that the maximum error of the speed estimates is less than 2.5% over the entire range of 5 27 m/s (11 60 mi/h). Vehicle classification is done based on the magnetic length and an estimate of the average vertical magnetic height of the vehicle. Vehicle length is estimated from the product of occupancy and estimated speed. The average vertical height is estimated using two IR sensors that are vertically spaced by 0.25 m. The developed sensor system is compact, portable, wireless, and inexpensive. Data are presented from a large number of vehicles on a regular busy urban road in the Twin Cities, MN, USA. Key words: road side traffic sensor, speed estimation, vehicle counting. I. Introduction This paper describes a portable sensing system that can be placed adjacent to a road and can be used for vehicle counting, vehicle classification, and vehicle speed measurements. The proposed system can make these traffic measurements reliably for traffic in the lane adjacent to the sensors. The sensor consists of IR transmitter and IR receiver. The developed sensing system is compact, portable, wireless, and inexpensive. IR sensors pairs are placed to evaluate vehicle classification. The main limitation of these works is their inability to distinguish between sedans versus sport utility vehicles (SUVs), pickups, and vans. Mostly, these two classes are combined, or poor classification rates are obtained when these are considered as individual classes. Furthermore, the vehicle classifications that have been evaluated are based on devices that were directly embedded in the roadway lanes. Unlike inductive loops and magnetic sensors that have been evaluated, the sensing system proposed in this paper does not require devices to be embedded in the roadway. Instead, it utilizes sensors that are placed on the side of the road, and hence, there is no need to stop the traffic for their installation. In addition, the sensor system used is very compact and wireless and can provide very high accuracies in vehicle detection, speed measurement, and classification.

2 II. The Hardware System Micro controller: This section forms the control unit of the whole project. This section basically consists of a Microcontroller with its associated circuitry like Crystal with capacitors, Reset circuitry, Pull up resistors (if needed) and so on. The Microcontroller forms the heart of the project because it controls the devices being interfaced and communicates with the devices according to the program being written. ARM7TDMI: ARM is the abbreviation of Advanced RISC Machines, it is the name of a class of processors, and is the name of a kind technology too. The RISC instruction set, and related decode mechanism are much simpler than those of Complex Instruction Set Computer (CISC) designs. Liquid-crystal display (LCD) is a flat panel display, electronic visual display that uses the light modulation properties of liquid crystals. Liquid crystals do not emit light directly. LCDs are available to display arbitrary images or fixed images which can be displayed or hidden, such as preset words, digits, and 7-segment displays as in a digital clock. Fig.2. Vehicle Section Block Diagram This paper describes a portable sensing system that can be placed adjacent to a road and can be used for vehicle counting, vehicle classification, and vehicle speed measurements. The proposed system can make these traffic measurements reliably for traffic in the lane adjacent to the sensors. The developed signal processing algorithms enable the sensor to be robust to the presence of traffic in other lanes of the road. The sensor consists of magneto resistive devices that measure the magnetic field. Signal processing algorithms based on an analysis of the magnetic field around a car are used to enable the sensor estimates. III. Design of Proposed Hardware System IV. Board Hardware Resources Features ZIGBEE: Fig.1.Road side Section Block diagram Zigbee modules feature a UART interface, which allows any microcontroller or microprocessor to immediately use the services of the Zigbee protocol. All a Zigbee hardware designer has to do in this is ensure that the host s serial port logic levels are compatible with the XBee s 2.8- to 3.4-V logic levels. The logic level conversion can be performed using either a standard RS-232 IC or logic level translators such as the 74LVTH125 when the host is directly connected to the XBee UART. The below table gives the pin description of transceiver. The X- Bee RF Modules interface to a host device through a

3 logic-level asynchronous Serial port. Through its serial port, the module can communicate with any logic and voltage Compatible UART; or through a level translator to any serial device. Data is presented to the X-Bee module through its DIN pin, and it must be in the asynchronous serial format, which consists of a start bit, 8 data bits, and a stop bit. Because the input data goes directly into the input of a UART within the X- Bee module, no bit inversions are necessary within the asynchronous serial data stream. All of the required timing and parity checking is automatically taken care of by the X-Bee s UART. MEMS: Micro electro mechanical systems (MEMS) are small integrated devices or systems that combine electrical and mechanical components. Their size range from the sub micrometer (or sub micron) level to the millimeter level and there can be any number, from a few to millions, in a particular system. MEMS extend the fabrication techniques developed for the integrated circuit industry to add mechanical elements such as beams, gears, diaphragms, and springs to devices. Examples of MEMS device applications include inkjet-printer cartridges, accelerometers, miniature robots, microengines, locks, inertial sensors, micro transmissions, micromirrors, micro actuators, optical scanners, fluid pumps, transducers and chemical, pressure and flow sensors. Many new applications are emerging as the existing technology is applied to the miniaturization and integration of conventional devices. These systems can sense, control and activate mechanical processes on the micro scale and function individually or in arrays to generate effects on the macro scale. The micro fabrication technology enables fabrication of large arrays of devices, which individually perform simple tasks, but in combination can accomplish complicated functions. MEMS are not about any one application or device, or they are not defined by a single fabrication process or limited to a few materials. They are a fabrication approach that conveys the advantages of miniaturization, multiple components and microelectronics to the design and construction of integrated electromechanical systems. MEMS are not only about miniaturization of mechanical systems but they are also a new pattern for designing mechanical devices and systems. Reflection sensor: Transmitter and receiver are incorporated in a single housing. The modulated infrared light of the transmitter strikes the object to be detected and is reflected in a diffuse way. Part of the reflected light strikes the receiver and starts the switching operation. The two states i.e. reflection received or no reflection are used to determine the presence or absence of an object in the sensing range. This system safely detects all objects that have sufficient reflection. For objects with a very bad degree of reflection (matt black rough surfaces) the use of diffuse reflection sensors for short ranges or with background suppression is recommended. Fig: PHOTO TRANSMITTER IR SENSOR The TSOP21. - series are miniaturized receivers for infrared remote control systems. PIN diode and preamplifier are assembled on lead frame, the epoxy package is designed as IR filter. The demodulated output signal can directly be decoded by a

4 microprocessor. The main benefit is the operation with short burst transmission codes and high data rates. Photo detector and preamplifier in one package Internal filter for PCM frequency Improved shielding against electrical field disturbance TTL and CMOS compatibility Output active low Low power consumption High immunity against ambient light The circuit of the TSOP21 is designed in that way that unexpected output pulses due to noise or disturbance signals are avoided. A bandpass filter, an integrator stage and an automatic gain control are used to suppress such disturbances. The distinguishing mark between data signal and disturbance signal are carrier frequency, burst length and duty cycle. GPS: Global Positioning System tracking is a method of working out exactly where something is. A GPS tracking system, for example, may be placed in a vehicle, on a cell phone, or on special GPS devices, which can either be a fixed or portable unit. GPS works by providing information on exact location. It can also track the movement of a vehicle or person. So, for example, a GPS tracking system can be used by a company to monitor the route and progress of a delivery truck, and by parents to check on the location of their child, or even to monitor high-valued assets in transit. Fig: GPS location tracking A GPS tracking system can work in various ways. From a commercial perspective, GPS devices are generally used to record the position of vehicles as they make their journeys. Some systems will store the data within the GPS tracking system itself (known as passive tracking) and some send the information to a centralized database or system via a modem within the GPS system unit on a regular basis (known as active tracking) or 2-Way GPS V. CONCLUSION This paper has proposed a portable and lowcost sensing system based on magnetic sensors that can be placed adjacent to the road and be used for traffic counting, speed measurement, and vehicle classification in the lane adjacent to the sensors. The vehicle classification and speed measurement in this paper are enabled using multiple spatially separated magnetic sensors. Through experimental data from 188 vehicles, it is shown that the traffic counting accuracy of the system is 99%. A method is also proposed to make the system robust to the traffic in the nonadjacent lane. The false calls caused by the traffic in the nonadjacent lane, if uncorrected, can cause an 8% detection error. Digital signal processing methods are adopted to reduce the computation effort. The speed estimation method is verified by mounting a test vehicle with a GPS. Experimental results show a maximum of 2.5% error in speed estimates over the range of 5 27 m/s (11 60 mi/h). Vehicle classification is performed based on the magnetic length and average magnetic height of vehicles. Vehicle length is estimated by multiplying the detection time by the estimated speed. The average vertical magnetic height is estimated using two magnetic sensors that are vertically spaced by about 0.3 m. Finally, it is shown that the sensing system can be used to reliably count the number of right turns at an intersection, with an accuracy of 95%. The challenge in counting the number of right turns is the false calls created by larger straightdriving vehicles, which, if uncorrected, cause 31% over detection. Two methods are proposed based on

5 using two and four magnetic sensors, which totally eliminate this error. REFERENCES [1] S. Jeng and S. Ritchie, Real-time vehicle classification using inductiveloop signature data, Transp. Res. Rec., J. Transp. Res. Board, vol. 2086, pp. 8 22, [Online]. Available: [2] S. Gupte, O. Masoud, R. F. K. Martin, and N. P. Papanikolopoulos, Detection and classification of vehicles, IEEE Trans. Intell. Transp. Syst., vol. 3, no. 1, pp , Mar [3] H. Cheng, H. Du, L. Hu, and C. Glazier, Vehicle detection and classification using model-based and fuzzy logic approaches, Transp. Res. Rec., J. Transp. Res. Board, vol. 1935, pp , [Online]. Available: [4] J. Medina, M. Chitturi, and R. Benekohal, Effects of fog, snow, and rain on video detection systems at intersections, Transp. Lett., Int. J. Transp. Res., vol. 2, no. 1, pp. 1 12, Jan [Online]. Available: org/ /tl [5] M. J. Caruso and L. S. Withanawasam, Vehicle detection and compass applications using AMR magnetic sensors, in Sens. Expo Proc., 1999, pp [6] S. Cheung, S. Coleri, B. Dundar, S. Ganesh, C. Tan, and P. Varaiya, Traffic measurement and vehicle classification with single magnetic sensor, Transp. Res. Rec., J. Transp. Res. Board, vol. 1917, pp , [Online]. Available: [7] S. Kaewkamnerd, J. Chinrungrueng, R. Pongthornseri, and S. Dumnin, Vehicle classification based on magnetic sensor signal, in Proc. IEEE ICIA, 2010, pp [8] M. Bottero, B. Dalla Chiara, and F. P. Deflorio, Wireless sensor networks for traffic monitoring in an logistic center, Transp. Res. Part C: Technol., vol. 26, pp , Jan [9] Y. He, Y. Du, and L. Sun, Vehicle classification method based on singlepoint magnetic sensor, Proc. Social Behav. Sci., vol. 43, pp , [10] J. Lan and Y. Shi, Vehicle detection and recognition based on a MEMS magnetic sensor, in Proc. 4th IEEE Int. Conf. NEMS, 2009, pp [11] Z. Feng andw. Mingzhe, A new SVM algorithm and AMR sensor based vehicle classification, in Proc. 2nd ICICTA, 2009, pp [12] J. Medina, A. Hajbabaie, and R. Benekohal, Detection performance of wireless magnetometers at signalized intersection and railroad grade crossing under various weather conditions, Transp. Res. Rec., J. Transp. Res. Board, vol. 2259, pp , [Online]. Available: doi.org/ / [13] F. Ahdi, M. K. Khandani, M. Hamedi, and A. Haghani, Traffic Data Collection and Anonymous Vehicle Detection Using Wireless Sensor Networks, Univ. Maryland, College Park, MD, USA, [14] S. Taghvaeeyan and R. Rajamani, Use of vehicle magnetic signaturesfor position estimation, Appl. Phys. Lett., vol. 99, no. 13, pp , Sep [15] E. Alpaydin, Introduction to Machine Learning. Cambridge, MA, USA: MIT Press, [16] X. Sun and R. Horowitz, Set of new trafficresponsive ramp-metering algorithms and microscopic simulation results, Transp. Res. Rec., J. Transp. Res. Board, vol. 1959, pp. 9 18, [17] Y.-K. Ki and D.-K. Baik, Model for accurate speed measurement using double-loop detectors, IEEE Trans. Veh. Technol., vol. 55, no. 4, pp , Jul [18] L. A. Klein,M. K.Mills, and D. R. P. Gibson, Traffic Detector Handbook: Volume I. Springfield, VA, USA: Nat. Tech. Inf. Service, 2006.

6 [19] Anonymous, VSN240 Wireless Flush Mount Sensor Data Sheet Sensys Networks. [Online]. Available: VSN240 Wireless Flush Mount Sensor Data Sheet [20] D. Nan, T. Guozhen, M. Honglian, L. Mingwen, and S. Yao, Low-power vehicle speed estimation algorithm based on WSN, in Proc. 11th IEEE Int. Conf. ITSC, 2008, pp