Optimization of Real-Time Traffic Flow Detection Methods Based on Sensor Networks

As modern society develops rapidly, people’s requests for traffic convenience and traffic safety become greater and greater, and it is essential to eliminate traffic congestion and traffic accident to sustainable development of urban areas. Therefore, this paper brings forward novel solution based on hybrid sensor networks to observe the status of traffic in road networks in order to alleviate traffic jam and prevent traffic accident. With the collection of precise traffic flow information at the time, it realizes traffic flow control at crossroads, gives warning in advance with the congestion or accident. We carried out a bunch of simulation experiments in succession, the main discoveries are as follows. a. The energy consumption is great reduced under the sensor deployment rate between 1:50–1:60 (sensor : vehicles). b.The sampling rates can keep a very high level of precise and efficiency under the appropriate range between 1:50–1:60 (sensor : vehicles).The critical segments of roadways are fitted with the radar sensors to accomplish not only reliable surveillance of traffic congestions but also timeearlies warning of traffic accidents as opposed to relying on the single-sensor network. As reflected in Fig. 17, the heterogeneous sensor network is more robust against sensor errors because of the complementarity effects without relying on individual sensors. The experimental results highlight the potential for hybrid sensing architecture for intelligent transportation systems(ITS) and provides a well-technical basis to alleviate urban traffic jam and improve transportation efficiency.