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Title: TRAFFIC FLOW MODELING WITH REAL- TIME DATA FOR ON-LINE NETWORK TRAFFIC ESTIMATION AND PREDICTION
| Content Provider | CiteSeerX |
|---|---|
| Author | Qin, Xiao |
| Abstract | This research addresses the problem of modeling time-dependent traffic flow with real-time traffic sensor data for the purpose of online traffic estimation and prediction to support ATMS/ATIS in an urban transportation network. The fundamental objectives of this study are to formulate and develop a dynamic traffic flow model driven by real-world observations, which is suitable for mesoscopic type dynamic traffic assignment simulation. A dynamic speed-density relation is identified by incorporating the physical concept in continuum and kinetic models, coupled with the structural formulation of the transfer function model which is used to represent dynamic relationship. The model recognizes the time-lagged response of speed to the influential factors (speed relaxation, speed convection and density anticipation) as well as the potential autocorrelated system noise. The procedures adapted from transfer function theory are presented for the model estimation and speed prediction using the real-time data. Speed prediction is performed by means of minimum mean square error and conditional on the past information. |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
