Stochastic Model for Traffic Flow Prediction and Its Validation

This research presents and explores a stochastic model to predict traﬃc ﬂow along a highway and provides a framework to investigate some potential applications, including dynamic congestion pricing. Traﬃc congestion in urban areas is posing many challenges, and a traﬃc ﬂow model that accurately predicts traﬃc conditions can be useful in re-sponding to them. Traditionally, deterministic partial diﬀerential equations, like the LWR model and its extensions, have been used to capture traﬃc ﬂow conditions. However, a traﬃc system is subject to many stochastic factors, like erratic driver behavior, weather conditions etc. A deterministic model may fail to capture the many eﬀects of randomness, and thus may not adequately predict the traﬃc conditions. The model proposed in this research uses a stochastic partial diﬀerential equation to describe the evolution of traﬃc ﬂow on the highway. The model as calibrated and validated by real data shows that it has better predictive power than the deterministic model. Such a model has many uses, including dynamic toll pricing based on congestion, estimating the loss of time due to congestion, etc.

Corporate Authors:
Transportation Research Board

500 Fifth Street, NW
Washington, DC United States 20001
Authors:
- Yang, Li
- Chu, Chih-peng
Conference:
- Transportation Research Board 90th Annual Meeting
- Location: Washington DC, United States
- Date: 2011-1-23 to 2011-1-27
Date: 2011

Language

English

Media Info

Media Type: DVD
Features: Figures; References; Tables;
Pagination: 29p
Monograph Title: TRB 90th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

TRT Terms: Mathematical prediction; Traffic congestion; Traffic flow; Traffic flow rate; Urban areas
Uncontrolled Terms: Stochastic analysis
Subject Areas: Operations and Traffic Management;

Filing Info

Accession Number: 01341998
Record Type: Publication
Report/Paper Numbers: 11-0086
Files: TRIS, TRB
Created Date: Jun 15 2011 11:22AM