Estimation of origin-destination matrix from traffic counts: the state of the art

The estimation of up-to-date origin-destination matrix (ODM) from an obsolete trip data, using current available information is essential in transportation planning, traffic management and operations. Researchers from last 2 decades have explored various methods of estimating ODM using traffic count data. There are two categories of ODM; static and dynamic ODM. This paper presents studies on both the issues of static and dynamic ODM estimation, the reliability measures of the estimated matrix and also the issue of determining the set of traffic link count stations required to acquire maximum information to estimate a reliable matrix

Estimation of origin-destination matrix from traffic counts: the state of the art

The estimation of up-to-date origin-destination matrix (ODM) from an obsolete trip data, using current available information is essential in transportation planning, traffic management and operations. Researchers from last 2 decades have explored various methods of estimating ODM using traffic count data. There are two categories of ODM; static and dynamic ODM. This paper presents studies on both the issues of static and dynamic ODM estimation, the reliability measures of the estimated matrix and also the issue of determining the set of traffic link count stations required to acquire maximum information to estimate a reliable matrix

Joint Estimation of OD Demands and Cost Functions in Transportation Networks from Data

Existing work has tackled the problem of estimating Origin-Destination (OD) demands and recovering travel latency functions in transportation networks under the Wardropian assumption. The ultimate objective is to derive an accurate predictive model of the network to enable optimization and control. However, these two problems are typically treated separately and estimation is based on parametric models. In this paper, we propose a method to jointly recover nonparametric travel latency cost functions and estimate OD demands using traffic flow data. We formulate the problem as a bilevel optimization problem and develop an iterative first-order optimization algorithm to solve it. A numerical example using the Braess Network is presented to demonstrate the effectiveness of our method.Comment: To appear at the Proceedings of the 58th IEEE Conference on Decision and Contro

An Activity-Based Model for the Estimation of Origin-Destination Matrix on Road Networks

Travel demand forecasting in terms of origin-destination (O-D) matrix estimation on transportation networks is an important topic in transportation research. The motivation of each trip is the user’s desire of conducting a compulsory or noncompulsory activity. User activity choices should not be ignored for the trip demand forecasting; nevertheless, there are scare approaches of O-D matrix estimation incorporating users’ activity behavior in the literature. This study develops an activity-based bi-level model for the estimation of O-D matrix on road networks using the activity-based approach where road users’ activity and travel choices are integrated scheduled. In the estimation, the parameter of the user activity and travel choice model is simultaneously calibrated. A heuristic algorithm is explored to solve the model. A numerical example is provided. The example results illustrate that the developed model and heuristic algorithm are applicable and efficient tools to deal with the problem of O-D matrix estimation

Dynamic O-D demand estimation: Application of SPSA AD-PI method in conjunction with different assignment strategies

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 choicemodel and information for the stability and the quality of the offline dynamic demand estimations

Dynamic O-D demand estimation: Application of SPSA AD-PI method in conjunction with different assignment strategies

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

Estimation of trip matrices from traffic counts: An equilibrium approach

In urban traffic management and planning, an important problem is how to obtain estimates of origin-destination (O-D) trip matrices using low-cost data such as traffic counts. Although conventional methods using the data from direct surveys can be used to estimate trip matrices, they appear to be inaccurate and expensive. By contrast, the use of traffic counts is attractive, as it is less expensive and more practical. The main objective of the research reported in this thesis is to develop new methods for estimating trip matrices from traffic counts when congestion effects in networks are considered. The problem and existing methods including the sequential solution method used in the ME2 model are reviewed. A new formulation is given for the problem which solves the two sub-problems of entropy maximization and equilibrium traffic assignment simultaneously. It allows modelled link flows to be constrained so as to equal observed ones without link assignment proportions of the trips. A simultaneous solution method is presented for this new formulation. To reduce the considerable computational burden in solving the problem, a heuristic method has been developed which uses a linear approximation fitted by regression to the equilibrium link flows. Extrapolation and perturbation methods have also been used to speed up the solution process. However, the simultaneous solution method appears to be impractical for use in large networks because of the heavy computational demand. As an alternative, an improved sequential solution method is proposed which uses a penalty function method. This method approximates a solution by solving a sequence of problems, while fixed link assignment proportions are used. The performance of the proposed methods has been tested and compared with that of the existing sequential ME2 method using both small example networks and a larger real network. The results show that the simultaneous method works well and that it performs better than the existing sequential method or the improved sequential method. The improved sequential method is also shown to perform closely to the simultaneous one. Some practical implications of the new methods including the robustness of the solutions and the increased computational burden are discussed and they are also compared with those of the sequential solution method. The conclusions from the main findings of the research are drawn and a number of suggestions for further study are given

Estimation of Parameters of Network Equilibrium Models: A Maximum Likelihood Method and Statistical Properties of Network Flow

Estimation of the parameters in network equilibrium models, including OD matrix elements, is essential when applying the models to real-world net-works. Link flow data are convenient for estimating parameters because it is rela-tively easy for us to obtain them. In this study, we propose a maximum likelihood method for estimating parameters of network equilibrium models using link flow data, and derive first and second derivatives of the likelihood function under the equilibrium constraint. Using the likelihood function and its derivatives, t-values and other statistical indices are provided to examine the confidence interval of es-timated parameters and the model’s goodness-of-fit. Also, we examine which conditions are needed for consistency, asymptotic efficiency, and asymptotic nor-mality for the maximum likelihood estimators with non-I.I.D. link flow data. In order to investigate the validity and applicability, the proposed ML method is ap-plied to a simple network and the road network in Kanazawa City, Japan