Development and Empirical Study of Real-Time Simulation-Based Dynamic Traffic Assignment Model

This research aims at developing a system of real-time simulation-based dynamic traffic model under mixed traffic flow conditions. The system consists of two layers, namely, simulation layer and real-time control layer. The system is implemented based on rolling horizon approach, which is advanced for each stage; thus real-time data can be incorporated within the framework. In order to predict normative, as well as predictive, information, a simulation-based dynamic traffic assignment model is employed within each stage. Empirical data for a real city network, such as flows from vehicle detectors, are used to validate the model in a real-time environment. The values of mean absolute percentage error and root-mean-squared percentage error are within 15%, and the results show promising agreements between observed and simulated flows.

[1]  Hani S. Mahmassani,et al.  A structural state space model for real-time traffic origin–destination demand estimation and prediction in a day-to-day learning framework , 2007 .

[2]  Ivar Jacobson,et al.  The Unified Modeling Language User Guide , 1998, J. Database Manag..

[3]  Yung-Kuei Huang,et al.  Dynamic Simulation-Assignment Model (DynaTAIWAN) under Mixed Traffic Flows for ITS Applications , 2007 .

[4]  Kendall Scott,et al.  UML distilled - a brief guide to the Standard Object Modeling Language (2. ed.) , 2000, notThenot Addison-Wesley object technology series.

[5]  Yupo Chan,et al.  Estimating an origin‐destination matrix with fuzzy weights: Part II: Case studies , 1993 .

[6]  Moshe E. Ben-Akiva,et al.  Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin-Destination Flows , 2000, Transp. Sci..

[7]  Ta-Yin Hu,et al.  Dynamic route choice behaviour and simulation-based dynamic traffic assignment model for mixed traffic flows , 2017, KSCE Journal of Civil Engineering.

[8]  Aleksandar Stevanovic,et al.  Design/build vs traditional construction user delay modeling : an evaluation of the cost effectiveness of innovative construction methods for new construction. Part 2 : VISUM Online for Salt Lake, Davis, and Utah Counties , 2007 .

[9]  Hani S. Mahmassani,et al.  Dynamic Network Traffic Assignment and Simulation Methodology for Advanced System Management Applications , 2001 .

[10]  Taehyung Park,et al.  Estimation of dynamic origin-destination trip tables for a general network , 2001 .

[11]  Athanasios K. Ziliaskopoulos,et al.  Foundations of Dynamic Traffic Assignment: The Past, the Present and the Future , 2001 .

[12]  Haris N. Koutsopoulos,et al.  Network State Estimation and Prediction for Real-Time Traffic Management , 2001 .

[13]  C. Lewis.  Industrial and business forecasting methods : a practical guide to exponential smoothing and curve fitting , 1982 .

[14]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[15]  Haris N. Koutsopoulos,et al.  Simulation Laboratory for Evaluating Dynamic Traffic Management Systems , 1997 .

[16]  Ennio Cascetta,et al.  Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts , 1993, Transp. Sci..

[17]  Hani S. Mahmassani,et al.  ESTIMATION OF DYNAMIC ORIGIN-DESTINATION FLOWS FROM SENSOR DATA USING BI-LEVEL OPTIMIZATION METHOD , 2001 .