Research On Urban Traffic Incident Detection Based On Floating Car

With the progress and development of ITS (Intelligence Transportation System), there is a growing awareness of the importance of traffic, as well as the daily lives of urban construction and development. Advanced technology and tools are integrated into the management and control of traffic by more and more researchers. It is wished that we can take this path to optimize and improve the urban traffic conditions. Traffic abnormal incident detection is one of the important elements in the field. At present, there are many of models and algorithms and is a huge system on traffic incident detection at home and abroad. Each algorithm has its advantages, disadvantages and different scope of application for different detector and the environment. On the other hand, with the development of floating car, more and more foreign researchers apply floating car to detect traffic abnormal incident while they discover its potential in traffic information collection and work on researching the development and application of floating car. However, in the domestic, the development and application for floating car mainly concentrate in Beijing, Hangzhou, and other big cities, and further development is pending. Therefore, there will be a more substantial development on the urban road traffic abnormal incident detection after floating car technology is integrated into traffic abnormal incident detection.The purpose of this paper is to discover unusual events on the road using data collected by floating cars and effective incident detection algorithm in order to take an effective and rapid treatment to receive ease traffic flow timely and restore free trafficflow.The completed major work in this paper includes the following five sections:First of all, on the basis of a great deal of reading and researching literature and algorithm, a abnormal traffic incident detection integrated model that is mainly composed by short-term traffic flow forecasting model and prediction error analysis model is presented.Second, the basic framework of short-term traffic flow forecasting model, which is the ARIMA model to improve, is elaborated, and calculation formula related to the model is derived.Third, the basic principle of prediction error analysis model is introduced, and the key parameters of the model are analyzed.Fourth, the procedures for the key steps of model calculation are prepared and used to calculate and analyze data.Finally, with the actual data collected by floating vehicles and data obtained through Flowsim simulation, analysis and verification of the integrated model are made, and model validity is evaluated.