A Nonparametric Probability Change-point Model for Traffic Flow Recognition
Tan De-rong · Systems Engineering · 2006
Traffic flow condition recognition is one of the important issues for Intelligent Transportation Systems research,especially for Advanced Traffic Management Systems and Advanced Traveler Information Systems research.The former (algorithms) were mainly concentrated on traffic flow recognition in advance(namely traffic flow forecasting) and real-time recognition(namely incident detection or traffic flow breakdown detection),but recognition of quantitative change of key parameters of traffic flow was neglected.Based on traffic flow theory and nonparametric statistical method of change-point,a nonlinear probability change-points model to identify quantitative change was firstly established with free flow of Shiji Road of Zibo city as an example,the hypothesis-testing problem of change-points and the algorithm to search the change-points were discussed.Finally,the method was calibrated and tested with the field data to verify the validity and the feasibility of the(theory.)