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PDF Traffic Congestion Detection System Using Wireless Sensor Network

Nguyễn Gia Hào

Academic year: 2023

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Traffic congestion is known to cause delays, high fuel consumption and high pollution index. To prevent major traffic congestion, a reliable and accurate detection system is needed. By using traffic parameters such as speed and density of vehicles on the road, an application of Wireless Sensor Network (WSN) could be used.

The evaluation of the traffic congestion detection system includes the presence of congestion and the degree of congestion itself. This sensing system will be implemented on a scale prototype/model to test the effectiveness. Many thanks to my family back home for their sacrifices along with their constant encouragement and support and guiding me towards the stars.

Special thanks to all my friends who helped me to finish this project and think together to face the problem.


To design a working scale-down prototype of a WSN system that takes the sensor readings and transmits information about the congestion. Determine the sensors that can work and provide the data needed for traffic congestion detection. Determine the best technique to analyze the data obtained from the sensors to decide on the traffic congestion level.

Produce a working, scaled-down prototype with the appropriate WSN system to prove the system's effectiveness.


  • Intelligent Transport System
  • Wireless Sensor Networks
  • Quantifying Road Traffic Congestion
  • Sensors in Traffic Monitoring

Research [11, 12] used this technology to provide a fast and wireless data collection and analysis system for traffic congestion detection. They used the WSN to predict traffic flow and control traffic congestion. Therefore, to detect traffic congestion even on a scale model, appropriate technologies must be selected.

To have a fast response of a system, a good real-time data handling is necessary. Since one of the goals of this project is to build a scale-down prototype to simulate the traffic congestion system, a WSN system is really a suitable technology to be used. So far, no definitive method has been laid to measure the level of congestion.

Nevertheless, various parameters are widely used to quantify road traffic congestion. In one of many studies [17], the average speed of vehicles was used to quantify the degree of congestion. As for analyzing the collected data, [19, 20] uses the fuzzy logic to infer different levels of traffic congestion.

Where M refers to road saturation, N refers to flow and C refers to capacity. Based on the finding, there are many ways to quantify the level of congestion. To simulate traffic congestion in a small-scale prototype, appropriate traffic parameters must be selected. Many studies have been conducted to apply sensor technology to monitor road traffic conditions.

In addition to a vision-based sensor [22], the loop detector was also used to monitor the traffic status of a certain road section. They used the sound waves of the moving vehicles to monitor the traffic situation. In [24] they use vehicle sensors with implementation of WSN to monitor road traffic status.

Sensors should also be able to provide data that will help quantify and detect traffic congestion.

Figure 1 Traffic Congestion Detection Scheme by using Queue-Length utilising the  VANET technology [6]
Figure 1 Traffic Congestion Detection Scheme by using Queue-Length utilising the VANET technology [6]


  • Project Methodology
  • Project Activities
    • Preliminary Research
    • Lab Experiment
    • Analysis of Data and Result
  • Tools and Software Required
    • XM2111CA
    • MDA300CA
    • MIB520CB
    • Speed sensor
    • Proximity sensor
    • Remote Control Car
    • MATLAB
    • MoteWorks
    • MoteView
  • Gantt-Chart and Key Milestone
  • Experiment Procedures
    • Sensor Identification
    • Sensor Purchasing
    • Software Installation
    • MEMSIC’s Product Familiarisation
    • Sensor Verification
    • Sensor Mounting
    • Scaled Down Prototype
    • Data Analysis

All the collected data will be used to justify and fulfill the purpose of the project, which is to detect traffic jams. The sensor provides output according to the distance of the object in front of it. This sensor will be used to measure the density of the road in the project.

Based on the research conducted, it can be concluded that the main parameters that can detect traffic congestion are the speed of the vehicle and the density of the road. For the time being, the following parameters will be used to detect congestion: vehicle speed and road saturation. To quantify the degree of congestion, the same parameters will be used as the speed and density of the road.

M is the saturation of the road, while N is the traffic flow and C is the capacity of the road, which will need to be determined when the scaled prototype design is completed. The ability of the sensor to measure these distances really fulfills the requirements for the project, since one of the necessary parameters is the distance between the cars. One of the pins is for powering the sensor which needed 5v to work.

Installation files for both software are provided by MEMSIC along with wireless weather purchases. The same procedures apply in this method where each of the sensor nodes and the base station have their own executable files. The test setup is as shown in Figure 19 and the wiring connection between the MDA300CA and the sensor is shown in Figure 20.

To verify the functionality of the purchased rotating sensor, a test was performed to observe if the sensor provides a counter for each of the transitions of the provided slotted disc. The configuration for the wiring of the rotary sensor to the MDA300CA board is based on Figure 21. Since all four cars are identical, manipulation of the situation can only be done at the beginning of the experiment.

The nature of the experiment was the same as in Experiment 1, where the cars moved together at low speed.

Table 2 Relationship of velocity in quantifying level of congestion
Table 2 Relationship of velocity in quantifying level of congestion


  • Software Installation
  • Sensor Verification
  • Scaled Down Prototype
  • Data Analysis

To solve the problem, a study was made on the default code of the XMDA300M.nc module. As a result of the study, a modification of the module code was made to obtain a fixed excitation value. The modifications are as in Figure 31. Since the modification is made, the excitation voltages given by the pin are constant.

Based on the result, it can be said that the sensor can measure and give correct counter for the number of revolutions of the motors. The first was that the potential drop between the 5V pin and the ground pin of the sensors was nowhere near 5V. The reason was suspected that the RE08A draws a lot of power from the XM211CA and would result in malfunction of the proximity sensor as it shared the same 5V power supply from the wireless motes. The new configuration of sensors, wireless mote and battery holder mounting on the remote control trolley can be seen as Figure 33.

Since the output of the rotary sensors would be a counter, the counter channel of the MDA300CA board is required. Due to the limitation, the only way to simulate traffic conditions is to determine the initial position of the car before starting the simulation. The traffic situation can be deduced from the data provided by the wireless sensor network.

From the graph it can be observed that the number of rotation that determines what condition of the road traffic it is. This is due to the same average speed of the cars maintaining their distance with other cars throughout the experiments. From the data taken, it is noted that no conditions were categorized as overloading.

This is due to the average distance of the cars which is always more than 25 cm apart. From the data it can be seen that, once the average distance between cars is far, the traffic is good even with different speeds.

Figure 29 Spike value of voltage given by excitation voltage pin
Figure 29 Spike value of voltage given by excitation voltage pin




Hong, "Research on Traffic Monitoring Network and Its Traffic Flow Forecast and Congestion Control Models Based on Wireless Sensor Networks", in Mechatronic Technology and Automation Measurement, 2009. Cheqfah, "Road traffic congestion detection and distribution using vehicle communication ", in Microwave Symposium (MMS), 2009 Mediterranean, 2009, p. page

Sanchez-Soriano, "Road Traffic Congestion Detection via Cooperative Inter-Vehicle Communications," in Local Area Computing Networks (LCN), 2010 IEEE 35th Conference, 2010, p. Khan, “VANET-based Intelligent Road Traffic Signaling System,” in Telecommunications Networks and Applications Conference (ATNAC), 2012 Australasian, 2012, p. Xu, “Traffic congestion prediction model in urban road network based on probe vehicle technology,” Journal of Highway and Transportation Research and Development (English Edition), vol.

Singh, “Wireless over Road: RF based Traffic Congestion Detection,” in Communication Systems and Networks (COMSNETS), 2011 Third International Conference on, 2011, pp. Hongchi, "Adaptive Traffic Light Control with Wireless Sensor Networks", in Consumer Communications and Networking Conference, 2007. Thajchayapong, "Estimating Road Traffic Congestion Using Vehicle Velocity", in ITS Telecommunications Proceedings, 2006 6th International Conference on, 2006, pp.

Li, "Congestion evaluation from traffic flow information based on fuzzy logic," in Intelligent Transportation Systems, 2003. Fu, "Study on urban traffic congestion evaluation method based on fuzzy comprehensive evaluation," in Digital Manufacturing and Automation (ICDMA) , 2012 Third International Conference on, 2012, p. Guojie, “A roadside wireless sensor network approach for urban traffic condition monitoring,” in Intelligent Transportation Systems, 2008.

Manes, "Real-time traffic monitoring based on wireless sensor network technologies," in Conference on Wireless Communications and Mobile Computing (IWCMC), 2011 7th International, 2011, p. Chen, “A small-scale traffic monitoring system in urban wireless sensor networks,” in Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, 2013, p.


Figure 1 Traffic Congestion Detection Scheme by using Queue-Length utilising the  VANET technology [6]
Figure 2 Model outline of detecting traffic congestion by using probe vehicle (PV)  technology [10]
Figure 3 Traffic congestion definition in using RF technology [11]
Figure 4 Congestion detection scheme by using RFID and GSM technology [12]



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