Origin-Destination Matrices Estimated with a Genetic Algorithm from Link Traffic Counts

Abstract

Several approaches have been developed to cope with the limits of conventional origin-destination (O-D) trip matrix collecting methods. One is the bilevel programming method, which uses a sensitivity analysis-based (SAB) algorithm to solve a generalized least-squares problem. However, the SAB algorithm has revealed a critical shortcoming when there is a significant difference between the target O-D matrix and the true O-D matrix. This problem stems from the heavy dependence of the SAB algorithm on historical O-D information. Such dependence may lead to a state in which the O-D estimator cannot produce a correct solution, especially when travel patterns are dramatically changed. To avoid the problem of dependency, a robust and stable method is required. A solution method is developed with a genetic algorithm, which is widely used in optimization problems to obtain a global solution. From the results of numerical examples, the proposed algorithm is superior to the SAB algorithm regardless of travel patterns.

Optimization–Simulation Model for Planning Supply Transport to Large Infrastructure Public Works Located in Congested Urban Areas

“…For this type of linear bi-level programming, there are applications in BenAyed et al (1992) anddell'Olio et al (2006); network design applications bearing the effect of congestion on the network, as in Marcotte (1986); various algorithms and heuristic implementations such as those in Marcotte and Marquis (1992) and non-linear bi-level programming, as in Suh and Kim (1992). & Another common application is the problem of estimating demand, as in Florian and Chen (1991) and in Kim et al (2001), where bi-level programming is presented to estimate the origin-destination (O-D) matrix with traffic counts for some links. These models use traffic volume data, including more economic information, as opposed to the expensive home survey.…”

Method for estimating population OD matrix based on probe vehicles

“…Their automatic collection is also well advanced. Regarding this OD matrices estimation, researchers have proposed several approaches including the following: generally entropy maximizing (Van Zuylen and Willumsen, 1980), maximum likelihood (Spiess, 1987), generalized least squares (GLS; Cascetta, 1984;Bell, 1991), Bayesian inference estimation techniques (Maher, 1983), and genetic algorithm (Kim et al, 2001, Baek et al, 2002. The basic concept of these models is to improve old OD matrices so that estimated link volumes are consistent with observed ones.…”

Wi-Fi based city users’ behaviour analysis for smart city

“…Maximum Likelihood methods minimize the likelihood of computing the OD matrix and the guessing traffic. Other methods based on traffic counts include Combined Distribution and Assignment (CDA) [15] , Bi-level Programming [19] , [30] , Heuristic Bi-level Programming [32] , Path Flow Estimation (PFE) [35] , or Neural Networks [25] . For example, Ashok and Ben-Akiva [3] used a Kalman filtering technique to update the OD matrix.…”