Advances in origin-destination trip table estimation for transportation planning and traffic simulation

Dynamic transportation models are becoming widespread owing to the ready access to powerful computing resources. This has led to the increasing use of Dynamic Traffic Assignment (DTA) and traffic simulation for a variety of planning applications. Timevarying origin-destination (OD) trip tables are key inputs to such models. However, they are generally not directly observed and must be estimated from other traffic measurements such as counts. We discuss several dynamic OD estimation approaches found in the literature and describe one of the more recent methodologies in detail. This method was tested in the TransModeler traffic simulation package using two examples: a small case study with simulated data, and a real network and count data from the I-5 corridor in California. The results confirm that dynamic OD estimation is hard in practice, but can provide significant improvements over the current state of the practice.

[1]  J. Kiefer,et al.  Stochastic Estimation of the Maximum of a Regression Function , 1952 .

[2]  Michel Bierlaire,et al.  An Efficient Algorithm for Real-Time Estimation and Prediction of Dynamic OD Tables , 2004, Oper. Res..

[3]  Haris N. Koutsopoulos,et al.  Nonlinear Kalman Filtering Algorithms for On-Line Calibration of Dynamic Traffic Assignment Models , 2006, IEEE Transactions on Intelligent Transportation Systems.

[4]  J. Spall.  Implementation of the simultaneous perturbation algorithm for stochastic optimization , 1998 .

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

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

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

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

[9]  Otto Anker Nielsen.  Multi-Path OD-Matrix Estimation (MPME) based on Stochastic User Equilibrium Traffic Assignment , 1997 .

[10]  J. Spall.  Stochastic Optimization, Stochastic Approximation and Simulated Annealing , 1999 .

[11]  Ramachandran Balakrishna,et al.  Off-line calibration of Dynamic Traffic Assignment models , 2006 .

[12]  Qi Yang,et al.  Hybrid Traffic Simulation Model , 2006 .

[13]  Francesco Russo,et al.  Calibrating aggregate travel demand models with traffic counts: Estimators and statistical performance , 1997 .

[14]  H Spiess.  A GRADIENT APPROACH FOR THE O-D MATRIX ADJUSTMENT PROBLEM , 1990 .

[15]  Time-Dependent Origin-Destination Estimation Without Assignment Matrices , 2008 .