Dynamic O-D Demand Estimation: Application of SPSA AD-PI Method in Conjunction with Different Assignment Strategies

Marialisa Nigro,Akmal Abdelfatah,Gaetano Fusco,Ernesto Cipriani,Chiara Colombaroni,Andrea Gemma

doi:10.1155/2018/2085625

Abstract

This paper examines the impact of applying dynamic traffic assignment (DTA) and quasi-dynamic traffic assignment (QDTA) models, which apply different route choice approaches (shortest paths based on current travel times, User Equilibrium: UE, and system optimum: SO), on the accuracy of the solution of the offline dynamic demand estimation problem. The evaluation scheme is based on the adoption of a bilevel approach, where the upper level consists of the adjustment of a starting demand using traffic measures and the lower level of the solution of the traffic network assignment problem. The SPSA AD-PI (Simultaneous Perturbation Stochastic Approximation Asymmetric Design Polynomial Interpolation) is adopted as a solution algorithm. A comparative analysis is conducted on a test network and the results highlight the importance of route choice model and information for the stability and the quality of the offline dynamic demand estimations.

Highlights

Dynamic Traffic Assignment (DTA) models are among the most effective tools for analysis and prediction of traffic conditions, especially in congested road networks
Since Dynasmart is assumed as term of reference in this laboratory application, the quasi-dynamic traffic assignment (QDTA) model has been calibrated by applying a Particle Swarm Optimization algorithm to determine the parameters of volume-delay functions that better approximate the results provided by Dynasmart on the test network used in this experiment
First comments on the results are related to the efficacy of the simultaneous perturbation stochastic approximation (SPSA) Asymmetric Design (AD)-Polynomial Interpolation (PI) in terms of the average objective function (OF) reduction achieved in each set of tests and the related standard deviation of OF reduction obtained by applying different OFs specifications (Table 2)

Summary

Introduction

Dynamic Traffic Assignment (DTA) models are among the most effective tools for analysis and prediction of traffic conditions, especially in congested road networks. To provide accurate and reliable estimates, DTA models need information on the distribution of the trips in space and time (dynamic demand matrices) that are assigned to the network. The offline estimate of the dynamic demand matrices assumes a starting demand value to be known based on the available information on traffic conditions on the network. This is a highly undetermined, nonlinear, nonconvex problem, which was the object of a relevant research effort in the last years [1]

Methods

Results

Conclusion