development of a sensor system for traffic data collection

Journal of Advanced Transportation, Vol. 43, No. 1, pp. 1-20 www.advanced-transport.com

Mahesh Atluri, Mashrur Chowdhury, Ryan Fries, Wayne Sarasua and Jennifer Ogle, Department of Civil Engineering, Clemson University, Clemson, SC Neeraj Kanhere, Department of Electrical & Computer Engineering, Clemson University, Clemson, SC Received: May 2006 Accepted: March 2007

Development of a Sensor System for Traffic Data Collection

Mahesh Atluri Mashrur Chowdhury

Neeraj Kanhere Ryan Fries

Wayne Sarasua Jennifer Ogle

Although many types of traffic sensors are currently in use, all have some drawbacks, and widespread deployment of such sensor systems has been difficult due to high costs. Due to these deficiencies, there is a need to design and evaluate a low cost sensor system that measures both vehicle speed and counts. Fulfilling this need is the primary objective of this research. Compared to the many existing infrared-based concepts that have been developed for traffic data collection, the proposed method uses a transmission-based type of optical sensor rather than a reflection-based type. Vehicles passing between sensors block transmission of the infrared signal, thus indicating the presence of a vehicle. Vehicle speeds are then determined using the known distance between multiple pairs of sensors. A prototype of the sensor system, which uses laser diode and photo detector pairs with the laser directly projected onto the photo detector, was first developed and tested in the laboratory. Subsequently this experimental prototype was implemented for field testing. The traffic flow data collected were compared to manually collected vehicle speed and traffic counts and a statistical analysis was done to evaluate the accuracy of the sensor system. The analysis found no significant difference between the data generated by the sensor system and the data collected manually at a 95% confidence interval. However, the testing scenarios were limited and so further analysis is necessary to determine the applicability in more congested urban areas. The proposed sensor system, with its simple technology and low cost, will be suitable for

M. Atluri, M. Chowdhury, N. Kanhere, R. Fries, W. Sarasua, J. Ogle

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saturated deployment to form a densely distributed sensor network and can provide unique support for efficient traffic incident management. Additionally, because it may be quickly installed in the field without the need of elaborate fixtures, it may be deployed for use in temporary traffic management applications such as traffic management in road work zones or during special events.

Introduction Sensor technology represents the cornerstone of highway traffic monitoring and incident management. Inductive loop detectors, which are the most widely used type of sensor, are an example of intrusive detectors that require cutting into the pavement for installation and maintenance. Several past research projects have proposed improved algorithms for enhancing the capabilities of loop detectors (Coifman, 2004 and Sun and Ritchie, 1999). However, major complaints against these sensors have been that these can easily become damaged and their maintenance requires closing traffic lanes. A second type of sensor technology, video detection, is installed in roadway right-of-ways or on overhead structures(Cheng, et al., 2005). Techniques to detect incidents based on video detection require an accurate estimate of the traffic flow on the image (Cheung and Chandrika, 2004, Elgammal, et al., 1999, Gupte, et al., 2002). Unless specific steps are taken to account for shadows (Prati, et al., 2001 and Cucchiara, et al., 2003), the accuracy of video detection systems degrades, particularly with long shadows typical occurring during morning and dusk. Video sensors often provide inaccurate traffic counts due to phenomena such as sudden lighting changes, headlight reflections on wet roads and adverse weather conditions (Martin, et al., 2004). In addition, when used for vehicle counting, video-based sensors must be mounted at heights above 25 feet (Peek Traffic – Field Guide and Installation Manual VideoTrak, 1999), which makes installation and maintenance difficult. Despite the inferior nature of existing loop detection systems, the U.S. transportation community has yet to adopt newer detectors as an industry standard. In the past few years, several novel non-intrusive sensing technologies have emerged, and while some of these such as active infrared and acoustic array sensors are promising, they are quite costly (Klein 2001). Others such as passive infrared and ultrasonic sensors are comparatively less expensive but are weather-sensitive. One

Development of a Sensor…

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of the most promising sensor technologies is microwave radar, which is insensitive to weather, multilane capable and moderately priced. However, in traffic applications, microwave radar cannot detect vehicles stopped or moving at speeds below 15 mph (Zhang, et al., 2004), speeds that are characteristic after the occurrence of a highway incident, and at locations of recurrent congestion. Several studies have been conducted on infrared laser-based sensor systems as researchers have begun to investigate the potential of these sensors for traffic counting, classification and speed estimating. The theory behind this technology is that laser receivers detect the absence and presence of single or dual laser beams to determine the “presence” of vehicles (Harlow and Peng, 2001, Abramson and Chenoweth, 2000, Scientific Technologies Inc., 2006). However, much of this work focused on the overhead laser sensor (Hussain, et al., 1993, Hussain, 1995, Tropartz, et al., 1999, Cheng, et al., 2000, Cheng, et al., 2001, Lin, et al., 2001, Wang, et al., 2003). Hussain et al. developed a reflection-based infrared laser system which transmits laser signals on the road and senses vehicles by receiving laser signals reflected back from vehicle exteriors, to monitor traffic (Hussain, et al., 1993, Hussain, 1995). After testing it in New York City and Syracuse, New York, they concluded that the reflection-based infrared laser sensor can effectively detect vehicles in all weather conditions. Tropartz et al. presented the test results of using a reflection-based infrared laser sensor system to classify vehicles at eight toll plazas in New York City (Tropartz, et al., 1999). Their findings indicated that the product, built by MBB SensTech, could accurately differentiate vehicle types using infrared lasers. In 1995, researchers from the University of Victoria developed the automatic vehicle dimension measure system (AVDMS), employing the system used the Schwartz Electro-Optics Autosense 3 sensor (Cheng, et al., 2000, Cheng, et al., 2001, Lin, et al., 2001, Wang, et al., 2003). These sensors can detect the distance between the detector and the object to determine the presence, size, and shape of the vehicle. Cheng et al. designed and tested another reflection-based laser detection system, capable of detecting traffic presence, classifying vehicles and estimating speed (Cheng, et al., 2000, Cheng, et al., 2001, Lin, et al., 2001, Wang, et al., 2003). The detector consists of an infrared laser diode, sensor optics, photodiode array, sensor electronics, and a data processing computer system. The real highway condition test shows that the system can achieve high accuracy of vehicle identification and speed estimation under different weather conditions. However, this system is limited in its


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one-to-one coverage in that a single pair of sensors can cover only one lane of traffic. In 2004, Chowdhury et al. developed a prototype transmission-based laser sensor system and they concluded that the proposed systems successfully estimated traffic count and speed measurement for single lane applications (Chowdhury, et al., 2004). Several products are now commercially available that use similar infrared concepts (CEOS Industrial Pty. Ltd., 2003 and TRIGG Industries International, Inc., 2004). One Australian company has developed an Infra-Red Traffic Logger (TIRTL) to count, classify and estimate the speed of traffic. However, their product is limited in that the system cannot recognize vehicles traveling side-by-side, which is very common for “platoon traffic” on US roadways. TRIGG industries also used transmission based infrared lasers in their products to detect if the height of a vehicle exceeded the pre-selected values. Although the fundamental mechanism is similar to the system proposed in this research, their product is an over-height vehicle detection system and is incapable of counting and classifying traffic. STI’s product, photoelectric sensors 705 series, is suitably designed for general applications such as traffic monitoring and over-height barrier sensing using transmission based infrared lasers (Scientific Technologies Inc., 2006). However, it can not count multilane platoon-travel traffic on busy roadways. To achieve this capability, the authors of the study suggested that several sensors must work separately and in coordination.

The proposed transmission-based optical sensor system discussed in this paper is a potentially low cost system requiring simple laser transmitters and receivers, and does not require any intrusion into the pavement for installation. These sensors have a long life span which is supported by their use in non-traffic applications. The proposed technology is similar to infrared detectors used in auto garage door openers commonly found in residential homes. Because of its simplicity, this low cost sensor system is quite suitable for large scale deployment on highway networks. Simple installation requirements, needing less than an hour for field set up, permit the system’s installation on any section of highway. This simplicity of design also enables sensor deployment for use in temporary traffic management applications, such as traffic management in roadwork zones or during special events.

This paper discusses research conducted by the authors at Clemson University that evaluates the ability of this simple transmission-based optical sensor system, which does not require the transmitted signals to


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be reflected back from the vehicles for sensing to detect traffic on multilane divided highways. The objectives of this research were to identify suitable configurations and to evaluate how well the sensor system accurately measured the number, presence and speed of individual vehicles. This research limited the application to two lanes in one direction of traffic flow and involved a two-phased methodology. Phase I included design and evaluation in a laboratory environment and Phase II included a scale version of the sensor system to measure vehicle counts and speeds in a field evaluation. Conceptual Framework This section outlines the basic theoretical concept of using optical sensors to measure traffic parameters such as volume and speed. Speed of vehicles on a multi-lane highway can be calculated by obtaining the time taken to travel a known distance. For a one-way single lane configuration, speed can be measured using two lasers, L1 and L2, placed on one side of a road with a known distance d between them. Laser beams are projected to photo detectors, PD1 and PD2, placed on the opposite side of the road at the same separation distance d. The infrared beam from the laser to its respective photo detector is continuous unless a vehicle enters the line of sight between them, blocking the beam from the detector. At the instant the first beam (L1 to PD1) is blocked, the current time is recorded; similarly the subsequent time is recorded when the second beam (L2 to PD2) is blocked. The time t taken to travel the known distance is obtained by subtracting the time the first sensor is blocked from the second time. Velocity, V, is obtained by dividing the distance d between the lasers by the time t taken to travel the distance (i.e., V = d / t). This one lane uni-directional sensor was evaluated previously with success (Chowdhury et al., 2004 and Goodhue, 2004). In the case of multiple lanes of traffic, multiple laser and photo detectors are placed along a highway as shown in Figure 1. As seen at L1 to PD1, vehicles can pass through the beam simultaneously. This research assumes that velocities of the vehicles are not identical. The measurement and data acquisition process is automated so that the velocity readout is obtained in real time. These data can then be stored in a computer or transmitted in real time to a remote traffic surveillance and control center.


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Figure 1. Detection system for simultaneous adjacent vehicles Laboratory Prototype Following the testing of the first laboratory prototype for a single lane (Chowdhury et al., 2004), the system was then configured to record vehicles in a multilane scenario. In order to obtain vehicle counts without failing to detect the presence of two vehicles positioned directly adjacent to each other, three L/PD pairs were used, spaced 20 cm or 0.67 ft apart. It was assumed that the difference in vehicle speeds would allow separation of vehicles between any pairs of sensors. The laser diodes used for the laboratory testing were infrared lasers with a peak emission of 780 nanometers. The photo detectors used in testing had a peak detection range between 600 and 1,000 nanometers. Improving on the equipment used in the initial laboratory testing, the communication link from the photo detectors to the microprocessor utilized a 900 MHz wireless communication, which is cost-effective and easily used in field applications. Three RF transmitters and an RF receiver were the primary components of this system. The transmitters and receiver allowed high performance wireless transfer of analog and/or digital information in the popular 902-928 MHz band. When a receiver was paired with a transmitter, a reliable link was created that could transmit analog and digital information up to 1000 ft. The transmission system also had an extended temperature range, varying from -30o C to +85o C (Goodhue, 2004). Figure 2 illustrates a typical wireless transmitter, and Figure 3 shows the laser and photo detector pairs placed on either side of the test track.


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Figure 2. The transmitter

Figure 3. Laser and photo detector pairs


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In this prototype the amplified signal from the photo detector is transmitted to the RF transmitter (T) through a wire-line connection, which subsequently transmitted the data to the RF receiver (R) through a wireless interface. A channel selector allowed the wireless communication system to operate between three (3) channels. The RF receiver forwarded the collected data to the microprocessor and on to the computer for further computation. A schematic of the two-lane, wireless transmission-based laser sensor system used in this prototype is shown in Figure 4.

Figure 4. Multi-lane, one-way laboratory laser sensor system. The prototype developed for a multilane one-way flow has the ability to count vehicles as well as measure vehicle presence and velocity. Figure 5 shows the final output of the processed data displayed on the computer with the velocity of each vehicle after it passes through the sensor system in the laboratory. Through careful analysis of data from the three sensors, adjacent vehicles can be identified with some degree of accuracy. The evaluation of this analysis is discussed later in this paper. The output generated on the personal computer is in real-time and is continually updated as long as the sensor system is available online.


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Figure 5. System output with velocity Figure 6 shows a photograph of the transmission-based laser sensor system in the laboratory. The three pairs of laser and photo detectors, transmitters and microcontroller board are also shown.

Figure 6. Transmission-based laser sensor system in the laboratory


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Algorithmic Framework In the absence of a vehicle in front of a detector, its output is close to the ground reference voltage, representing a Low state. When a passing vehicle obstructs the laser beam, the detector output swings close to the battery voltage, representing a High state. The output then swings back to Low state when a vehicle completely passes the detector. Microcontrollers record the start time and duration of pulses generated in this manner from all of three detectors by sequential polling. In a single lane scenario, as shown in Figure 7, little ambiguity exists for associating detections with vehicles among the three detectors. However, in a multilane scenario, there are difficulties in discerning the presence of moving vehicles that are adjacent to one another.

Figure 7. Single lane scenario

Figure 8. Multiple-lane scenario For multilane cases, the algorithm is initialized when all three detectors are in a Low state. In the multiple-lane case, the algorithm


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takes into account the scenario of two vehicles traveling side-by-side. Should one detector fail to detect the presence of the adjacent vehicle in a different lane, another detector down-range is able to discern two different vehicles, based on the assumption that at least one of the three sensors can verify separate instances of both vehicles. Figure 8 shows an example of two vehicles moving side by side in adjacent lanes. Sensors 1 and 2 discern the presence of a single vehicle, while Sensor 3 detects more than one. Microcontrollers monitor the outputs of all three sensors, and provide data such as starting time and duration of the detection pulse (hit-time and passing time) to the subsequent stages of the algorithm. The timing data from each sensor is separated into groups based on a time interval (e.g. 60 sec) that is selected by the program with finding a sufficient gap or headway between the detections of vehicles in the first sensor. The implementation of the algorithm relies on the use of the initial mean speed vm to select the range of detections in three sensors. vm is updated for the next analysis period based upon the mean speed of the previous interval. The speed-association matrices for sensors 1 and 2, and sensors 2 and 3 are shown in Table 1. Di,j the jth detection by ith sensor and each element in the matrix (Sp,q) represents the speed of the vehicle if the vehicle corresponds to pth detection in one sensor and qth detection in the next sensor. For example, as shown in the example in Table 1, the cell S3,4 where D1,3 and D2,4 intersect, represents the speed of a vehicle (56 mph in this case) if the vehicle corresponds to the third detection in sensor 1 and the fourth detection in sensor 2. Table 1. Speed association matrices for sensors 1 and 2 (left table) and sensors 2 and 3 (right table)

D21 D22 D23 D24 D25 D31 D32 D33 D34 D35 D11 63 38 16 14 3 D21 61 41 40 25 7 D12 110 54 31 27 8 D22 - 56 58 50 18 D13 - 87 58 56 51 D23 - - - 55 31 D14 - - 84 83 61 D24 - - - 98 47 D25 - - - - 63

The empty cells in Table 1 represent values which are impractical, such as the speed of a vehicle detected by sensor 2 prior to its detection in sensor 1, and values greater than threshold vt (vt was selected as 120


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mph). Given the initial mean speed (In this example, vm = 60 mph), the cost matrix is developed as shown in Table 3. Cost represented in each cell of Table 2 simply represents the absolute difference between vm and corresponding values in the speed association matrix in Table 1. Table 2. Example of Finding a path (associations) in tables using the minimum cost method

D2,1 D2,2 D2,3 D2,4 D2,5 D3,1 D3,2 D3,3 D3,4 D3,5 D1,1 3 22 44 46 57 D2,1 1 19 20 35 53 D1,2 50 6 29 33 52 D2,2 - 4 2 10 42 D1,3 - 27 2 4 9 D2,3 - - - 5 29 D1,4 - - 24 23 1 D2,4 - - - 38 13 D2,5 - - - - 3

Table 3. Dynamic Programming example D2,1 D2,2 D2,3 D2,4 D2,.5

D1,1 3 3 + 22 =

25 9 + 44 = 53 38 + 46 = 84

44 + 57 = 101

D1,2 3 + 50 = 53

3 + 6 = 9 9 + 29 = 38

11 + 33 = 44 15 + 52 = 67

D1,3 - 27 + 9 = 36 9 + 2 = 11 11 + 4 = 15 15 + 9 = 24

D1,4 - - 11 + 24 = 35

11 + 23 = 34 15 + 1 = 16

The number of possible cases that we need to evaluate for associating all detections between two adjacent sensors is very large when setting no constraints. To keep the problem computationally feasible the authors assumed that except for vehicles that merge or split, the order of detection is preserved over the three sensors. If the sensors are placed very far apart, the uncertainty in the order of detection increases. Conversely, if the sensors are placed very close to each other, vehicles traveling side-by-side are less likely to be detected as separate instances by any sensors downstream. Therefore, the authors placed them at varying distances in the field to identify a suitable distance between the sensors, ultimately settling upon a distance of 150 feet between sensors based on field observations, where the above assumptions were rarely violated.


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With the above assumption, the problem becomes one of finding a minimum cost path from the first element to the last element in both cost matrices in Table 2. For this example case, the first element represents first detection by sensor 1 and first detection in sensor 2 and the last element represents the fourth detection in sensor 1 and fifth detection in sensor 2. Dynamic Programming (Wagner, 1995) mitigates this difficulty by finding the minimum cost path from the first (top-left) to the last (bottom-right) element of the table in a computationally efficient manner. The basic idea behind dynamic programming is to break the problem into simpler sub-problems, and using solutions to these sub-problems to find the optimum solution to the original problem. Table 3 illustrates this concept. Each element in the table is the minimum cost to reach that element from the starting element of the cost matrix. Starting from the first element, the minimum cost path is traced to the last element and corresponding associations between sensor detections are established. The minimum cost path is used to associate detections across the sensors. In the above example (Table 2) the third detection of sensor 1 is associated with two detections (3 and 4) of sensor 2. This implies that two vehicles were traveling side-by-side when they crosses sensor 1 but were not traveling side-by-side when they crossed sensor 2. In the same manner, the process of associating vehicles is extended from sensors 1-2 to sensors 2-3. Figure 9 illustrates these associations.

Figure 9. Identification of shortest path through dynamic programming In this example, none of the three sensors counted the correct number (six) of vehicles due to instances of vehicles traveling side-by-side. However, by associating detections of all three sensors over the


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entire batch, the algorithm enabled the evaluation of the cases where vehicles traveled side-by-side and provided the correct number of vehicles. After establishing most likely correspondences between the sensor detections, speeds of individual vehicles are computed using the corresponding detection times in sensors 1 and 3 and the known distance between the two sensors. Field Evaluation After the experimental prototype system was built and tested in the laboratory, the system was field tested on a section of highway in South Carolina. All the basic components of the sensor system were taken to the site. The 20-centimeter laboratory spacing between each of the two successive L/PD pairs was increased to 150 feet, considering vehicle length and field observations. Similarly, the 40 cm laboratory distance between the laser diode and the photo detector, which were placed on either side of the track, was increased to 35 feet for a two-lane road. Camera tripods were used to place the three sets of L/PD pairs in the field. If the height of L/PD placement was too low, the sensor would detect the wheels, axles or any objects below the bumper on some vehicles. If the placement was too high, the front bumper of smaller vehicles would be below the height of the L/PD pairs. Taking these facts into consideration, the heights of the tripods were adjusted so that the laser and photo diodes were positioned approximately two feet above the pavement. Figure 10 shows the setup of the laser and the photo detector on the tripods at U.S.123 near Clemson, South Carolina. Data Collection The first field test was conducted on Seneca Creek Road in Clemson, South Carolina, which is less than one mile from the Clemson University campus. This test was conducted to verify whether hardware components of the prototype worked well on-site. This test also recorded the amount of time necessary for setting up and aligning all three pairs of laser and photo detectors. After testing the prototype, modifications were made to the amplifier and later implemented on the sensor apparatus at U.S. 123 test site in Clemson, SC (see Figure 10). The highway width between the laser and


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the photo detector was approximately 35 feet. The L/PD pairs were placed on camera tripods to achieve the 2-foot height above the roadway surface necessary for vehicle detection. Each laser was set up and spaced 150 feet directly perpendicular to each of the photo detectors across the highway. The entire field setup took less than one hour to bring the sensor system online and to verify that it was recording data. Data was collected for two hours; the researchers recorded an average of 336 vehicles per hour, nine of which were considered single unit trucks and fifteen were classified as single and multi-trailers. Test measurements were conducted in clear weather.

Figure 10. Laser diode and photo detector pairs along US 123 Statistical Analysis of Data After the data was collected in the field, a statistical analysis was performed to determine whether the data collected from the sensors represented the observed traffic conditions. The evaluation of the speed


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and vehicle detection was conducted using SAS, an integrated software system that allows users to perform numerous statistical tests. Analysis of Speed For the speed data collected, the independent t-test was used to compare the difference between the manually-collected mean speed and the sensor-system-generated mean speed. The t-test is a statistical test that determines any statistically significant difference between two means while accounting for the different sample sizes. A 95% confidence interval with a level of significance (alpha) of 0.05 was used for accuracy, which is a reliability measure of the sensor system. Statistically, the SAS output data found no significant difference between the means of the manually collected data and the sensor-generated data (null hypothesis). As a result, the null hypothesis was accepted and the alternative rejected, meaning the speed data collected by the system was within tolerance. The SAS output for the velocity data analysis is summarized in Table 4. Table 4. T-Test for Speed Data

Independent t-test Velocity (mph) Data Statistical Parameter Manual Sensor Mean 58.033 58.182 Variance 7.882 7.822 Observations 364 336 Mean Difference -0.148 Degrees of Freedom 698 P Value 0.8031

Analysis of Vehicle Count A z-test was used to compare the number of vehicles counted by the sensor with the number manually counted. Since the different numbers of vehicles in each sample set is the focus of the statistical analysis not a weighting factor as in the previous speed analysis, this analysis does not account for the different sample sizes. A 95% confidence interval with a


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level of significance (alpha) of 0.05 was again used for accuracy and the absolute z values were calculated with the observed value expected to be between -1.645 to 1.645. The calculations showed that there was no statistically significant difference between the manually collected vehicle count and the sensor-generated count. Conclusions This research focused on the design, development and evaluation of an optical sensor system for a multi-lane road with one-way traffic flow. The application of dynamic programming in the algorithm was able to successfully separate vehicles when they were moving side by side for some distance. Field evaluations found no statistically significant variations between the system-generated data and manually collected data. However, the field evaluation was limited by the small sample size, ideal weather conditions and relatively light traffic volume. Continued development of the laser sensor system presented in this paper can yield a productive, cost-efficient system to measure traffic data for more than two lanes in one direction and in both direction on multi-lane highways. Further testing is necessary under a wider range of traffic conditions, such as in high volume urban highways, to produce significant information on the system performance due to the higher occlusion rate caused by high traffic volumes. The system set up time was less than one hour and requires simple fixtures for field implementation. These benefits can make the system suitable for temporary applications, such as in work zones and special event traffic management. Low cost of the system will facilitate saturated deployment in a large scale highway network. References Abramson, N. J. and Chenoweth, A., “Ray of Light: Traffic Sensors

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development of a sensor system for traffic data collection

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