Travel time prediction using the GPS test vehicle and Kalman filtering techniques

A sudden traffic surge immediately after special events (e.g., conventions, concerts) can create substantial traffic congestion in the area where the events are held. It is desired that the special events related traffic performance can be measured so that the traffic flow can be improved via some existing methods such as a temporary traffic signal timing adjustment. This paper focuses on the study of the arterial travel time prediction using the Kalman filtering and estimation technique, and a graduation ceremony is chosen as our case study. The Global Positioning System (GPS) test vehicle technique is used to collect after events travel time data. Based on the real-time data collected, a discrete-time Kalman filter is then applied to predict travel time exiting the area under study. An assessment of the performance and its effectiveness at the test site are investigated. The approaches to further improve the accuracy of the prediction error are also discussed.

[1]  Guanrong Chen,et al.  Introduction to random signals and applied kalman filtering (second edition), Robert Grover Brown and Patrick Y. C. Hwang, John Wiley, New York, 1992, 512 p.p., ISBN 0–47152–573–1, $62.95 , 1992 .

[2]  H. W. Sorenson,et al.  Kalman filtering : theory and application , 1985 .

[3]  I Okutani,et al.  Dynamic prediction of traffic volume through Kalman Filtering , 1984 .

[4]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[5]  Haitham Al-Deek,et al.  Travel Time Prediction with Non-Linear Time Series , 1998 .

[6]  William L Eisele,et al.  TRAVEL TIME DATA COLLECTION HANDBOOK , 1998 .

[7]  Douglas Laird.  Douglas Emerging Issues in the Use of GPS for Travel Time Data Collection , 1998 .

[8]  Steven I-Jy Chien,et al.  Dynamic Freeway Travel-Time Prediction with Probe Vehicle Data: Link Based Versus Path Based , 2001 .

[9]  Dongjoo Park,et al.  Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks , 1998 .

[10]  Nagui M. Rouphail,et al.  ANALYSIS OF CORRELATION BETWEEN ARTERIAL TRAVEL TIME AND DETECTOR DATA FROM SIMULATION AND FIELD STUDIES , 1994 .

[11]  Steven I-Jy Chien,et al.  Dynamic freeway travel time prediction using probe vehicle data: Link-based vs , 2001 .

[12]  Donald J Dailey,et al.  Travel-time estimation using cross-correlation techniques , 1993 .

[13]  Dongjoo Park,et al.  Direct Forecasting of Freeway Corridor Travel Times Using Spectral Basis Neural Networks , 2001 .

[14]  Jim Gallagher,et al.  TRAVEL TIME DATA COLLECTION USING GPS , 1996 .