Ma Zengqiang

Short-Term Traffic Flow Prediction Based on ANFIS

Accurate short-term traffic flow prediction has become a critical problem in intelligent transportation systems (ITS). In the paper, a kind of adaptive prediction method for short-term traffic flow based on ANFIS (adaptive-network-based fuzzy interference system) model was presented. ANFIS is a fuzzy interference tool implemented in the framework of adaptive network. It combines the comprehensibility of fuzzy rules and the adaptability and self-learning algorithms of neural networks. The traffic flow prediction model with 104 changeable parameters will be established through the training process, the goal of which is reduce the prediction errors between real predicting output the ANFIS model and the desired output. The result of simulation research demonstrates that this method has the advantage of high precision and good adaptability. This scheme is novel and advanced in the domain of the road traffic flow prediction. The application of the scheme will remarkably improve the response efficiency and precision degree of the road traffic inducement and control system in our country.

Road Safety Evaluation from Traffic Information Based on ANFIS

With the rapid growth of the number of various vehicles, the ratio of the traffic accidents to vehicle number is increasing greatly. To improve road safety, it is necessary to monitor and evaluate the traffic safety degree first of all. The factors that influence the road traffic safety include the basic traffic parameters such as velocity and density, the peccancy behaviors such as overspeed, overweight and retrogradation and the weather conditions such as the plane visibility on the road. In this paper, an index, level of safety (LOS), is defined to indicate the road safety extent of traffic state. It is a continuous number and should much fit human perception on safety. In this paper, a new method is present to evaluate LOS on freeway based on ANFIS (adaptive-network-based fuzzy interference system). ANFIS applied in this paper is based on zero-order Sugeno fuzzy model embedded into a framework of adaptive networks. In the evaluating system the inputs are mean velocity, mean density and the plane visibility on the road detected, and the output is LOS. After a simulation with MATLAB, the traffic safety evaluation system based on ANFIS is put up and a series of fuzzy logic rules are trained. As a result, the evaluating system based on ANFIS has the adaptive capacity and will be helpful to enhance the safety level on the road.

Wireless monitoring system of vehicle overspeed on freeway based on GPRS

Vehicle overspeed is an important reason for the traffic accident on the freeway and it is helpful and necessary to monitor the peccancy behavior of over speed. In this paper, design method of the overspeed wireless monitoring system based on GPRS (general packet radio system) is discussed, and the configuration of its hardware and software is introduced. The whole system consists of the subsystems of the vehicle overspeed monitoring stations that are distributed on the freeway and the remote control center. As the over speed action is detected, two images will be captured, analyzed and processed. Then the information of the overspeed vehiclepsila speed, peccancy time, peccancy place serial number, compressed image and etc, will be written to the wireless modem of Motorola G20 through the serial interface and sent to the remote monitoring center through the GPRS network and the GGSN gateway. The remote control center is responsible for data displaying and saving. Integrating the technologies of sensor, number plate recognition and GPRS, the whole monitoring system stands up the trend of vehicle overspeed surveillance on freeway. The design and application of the new type of vehicle overspeed monitoring system will undoubtedly improve the automatic and intelligent management on freeway.