Developments and Applications of Simulation-Based Online Travel Time Prediction System

The framework and field application of a simulation-based online system for travel time prediction are presented. The proposed system is designed to contend with most critical issues associated with real-time operations, which include estimation of missing volumes, detection of incidents, data filtering, and computation of traffic volumes for projected time intervals so as to activate the simulation function. The proposed system was deployed on two routes of 30 mi between Salisbury and Ocean City, Maryland, with a total of 10 detectors. The preliminary application results clearly indicate that with proper integration the proposed system offers a cost-effective tool for real-time travel time prediction.

[1]  Yi Qi,et al.  Application of wavelet technique to freeway incident detection , 2003 .

[2]  Laurence R. Rilett,et al.  Simplex-Based Calibration of Traffic Microsimulation Models with Intelligent Transportation Systems Data , 2003 .

[3]  Brian Lee Smith,et al.  Use of Local Lane Distribution Patterns to Estimate Missing Data Values from Traffic Monitoring Systems , 2002 .

[4]  Van Hai Tran,et al.  Validation of microscopic traffic simulation models , 2005 .

[5]  Edward Chung,et al.  Characterization of incidents on an urban arterial road , 2001 .

[6]  Pravin Varaiya,et al.  Measuring Traffic , 2008, 0804.2982.

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

[8]  Alexander Skabardonis,et al.  Detecting Errors and Imputing Missing Data for Single-Loop Surveillance Systems , 2003 .

[9]  Byungkyu Park,et al.  Development and Evaluation of a Procedure for the Calibration of Simulation Models , 2005 .

[10]  David B Roden.  FORECASTING TRAVEL TIME , 1995 .

[11]  Haitham Al-Deek,et al.  Travel-Time Prediction for Freeway Corridors , 1999 .

[12]  S G Ritchie,et al.  Incident detection issues Task A report: automatic freeway incident detection: a state-of-the-art review , 1993 .

[13]  C. B. Tilanus,et al.  Applied Economic Forecasting , 1966 .

[14]  Steven I-Jy Chien,et al.  DYNAMIC TRAVEL TIME PREDICTION WITH REAL-TIME AND HISTORICAL DATA , 2003 .

[15]  R. Jayakrishnan,et al.  Calibration and Path Dynamics Issues in Microscopic Simulation for Advanced Traffic Management and Information Systems , 2001 .

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

[17]  Nancy L. Nihan,et al.  DETECTING ERRONEOUS LOOP DETECTOR DATA IN A FREEWAY TRAFFIC MANAGEMENT SYSTEM , 1990 .

[18]  Haris N. Koutsopoulos,et al.  Calibration and Validation of Microscopic Traffic Simulation Tools: Stockholm Case Study , 2003 .

[19]  William T. Scherer,et al.  DATA IMPUTATION STRATEGIES FOR TRANSPORTATION MANAGEMENT SYSTEMS , 2003 .

[20]  Byungkyu Park,et al.  Microscopic Simulation Model Calibration and Validation: Case Study of VISSIM Simulation Model for a Coordinated Actuated Signal System , 2003 .