Traffic Congestion Forecasting Based on Possibility Theory
| Autor: | Zhanquan Sun, Yanling Zhao, Zhao Li |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Mathematical optimization
Engineering Traffic conflict Aerospace Engineering 02 engineering and technology 01 natural sciences 010104 statistics & probability 0202 electrical engineering electronic engineering information engineering 0101 mathematics Traffic generation model Simulation InSync adaptive traffic control system Traffic congestion reconstruction with Kerner's three-phase theory business.industry General Neuroscience Applied Mathematics ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Floating car data Traffic flow Network traffic control Computer Science Applications Traffic congestion Control and Systems Engineering Automotive Engineering 020201 artificial intelligence & image processing business Software Information Systems |
| Zdroj: | International Journal of Intelligent Transportation Systems Research. 14:85-91 |
| ISSN: | 1868-8659 1348-8503 |
| Popis: | Traffic congestion state identification is one of the most important tasks of ITS. Traffic flow is a nonlinear complicated system. Traffic congestion state is affected by many factors, such as road channelization, weather condition, drivers’ different driving behavior and so on. It is difficult to collect all necessary traffic information. Traffic congestion auto identification result based on incomplete traffic information exists uncertain. Little work has been done to analyze the uncertainty. Possibility theory introduced by Zadeh is an efficient means to present incomplete knowledge. Possibility distribution determination is an important task of possibility theory. In this paper, possibility theory is used to describe the uncertainty of traffic state. The possibility distribution of traffic state is determined according to the probability distribution of traffic flow parameters, such as volume, speed and occupancy and so on. The multi-variable distribution of traffic flow parameters is determined with large-scale traffic flow data. Large-scale traffic congestion samples are generated with parallel k-mean clustering method. Traffic congestion forecasting is based on the forecasting of traffic flow parameters with SVM (Support Vector Machines). At last, a practical example is analyzed with the proposed method. |
| Databáze: | OpenAIRE |
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