As a significant part of intelligent traffic systems, the short-term traffic forecasting system undertakes the important task of providing basic data for traffic control and route guidance. In view of the real-time requirements and accuracy of short-term traffic forecasting systems, this study designs a data-driven, short-term traffic forecasting system based on a non-parametric regression model. First, this paper adopts distributed architecture, which assigns forecasting tasks of different roads to several host computers. Second, a short-term traffic forecasting algorithm based on non-parametric regression is improved in terms of feature of application, promoting search efficiency, and continuously self-adaptive. Finally, according to the characteristics of different databases, it optimizes storage structure, raises I/O efficiency, and improves the system performance. The experiment results show that this system has higher prediction speed and reliability and can handle the prediction tasks of large-scale transportation network systems. At the same time, this system can optimize and adjust itself in the process. It also has low dependence of initial setting and better adaptation.
A Distributed Short-Term Traffic Forecasting System Based on Non-Parametric Regression Approach
14th COTA International Conference of Transportation Professionals ; 2014 ; Changsha, China
CICTP 2014 ; 233-246
24.06.2014
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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