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

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.

Investigating the quality of Spiess-like and SPSA approaches for dynamic OD matrix estimation

“…Simultaneous Perturbation Stochastic Approximation (SPSA) is commonly used in OD matrix estimation (Antoniou et al 2015;Cantelmo et al 2014;Cipriani et al 2011;Lu et al 2015), and it can easily account for additional measurements (Antoniou et al 2016;Bullejos et al 2014;Carrese et al 2017;Nigro et al 2018). However, before extending its use, we have considered it necessary to learn about its computational performance.…”

A New Model for Locating Plate Recognition Devices to Minimize the Impact of the Uncertain Knowledge of the Routes on Traffic Estimation Results

“…For example, Sánchez-Cambronero et al [13] used Bayesian networks; Bai and Chen [14] used neural networks; and Lui et al [15] used deep learning. In any case, without going into detail concerning the models used to predict traffic flows, to conduct such traffic analysis both for static and dynamic studies, technicians need two things: a good representation of the traffic network and a good data set with which to simulate routes of the network and to predict the flows (Nigro et al [16]). is means that the optimal number and locations of the sensors that can collect such data must be determined.…”

Método escalável de estimação de demanda origem destino a partir de dados de identificação automática de veículos.

“…Although the classical 4-step model (ORTÚZAR & WILLUMSEN, 1990) is still the central line adopted by most transportation engineering companies, government agencies, and professionals, newer techniques come to supply several shortcomings due to non-computational data in previous methods concerning passenger or cargo trips. Nigro et al (2018) state that to conduct such traffic analysis both for static and dynamic studies, specialists must have quality in: a representation of the traffic network and a data set to simulate network routes and predict the traffic flows.…”

“…The origin-destination (OD) matrix is essential for efficient traffic management. It designates the demand for trips between traffic zones (M. Nigro et al, 2018).…”