Distributed Approach for Estimation of Dynamic Origin–Destination Demand

Abstract

The problem of dynamic origin–destination (O-D) demand estimation aims at estimating the unknown demand values for all O-D pairs and departure times with the use of available time-varying link flow observations. This paper presents a distributed algorithm for estimating the dynamic O-D tables for urban transportation networks. The new algorithm supports the deployment of systems for real-time traffic network management that adopt dynamic traffic assignment methodology for network state estimation and prediction. It encapsulates available link information and reduces the data size required by conventional algorithms for O-D demand estimation. The algorithm adopts a two-stage approach. In the first stage, the study area under consideration is divided into a number of subareas, and an O-D demand table is estimated independently for each subarea. These local O-D tables are then integrated to construct an O-D table for the entire study area. An application of the new algorithm for a typical freeway network is presented as an example.

A distributed origin–destination demand estimation approach for real-time traffic network management

“…For instance for the case with four subareas, the average running time is recorded to be 17.76 seconds. A running time in the magnitude of hours is recorded for similar size networks using the traditional estimation methodology as described in Etemadnia and Abdelghany (2009).…”

“…In the first step, the OD demand table for each subarea is estimated. The local demand for each subarea d c ijs is assumed to be estimated following the approach presented in Cascetta et al (1993) and applied by Etemadnia and Abdelghany (2009). The approach assumes the availability of a historical data for each subarea in the form of timedependent link-flow proportions.…”

A hierarchical approach for dynamic origin-destination matrix estimation on large-scale congested networks

“…Chang & Tao [9] propose to decompose the network into a top-level network consisting of freeways and major urban arterials, and low-level networks. Etemadnia & Abdelghany [10] also subdivide the large-scale network into a number of subareas, but here no specifications are necessary on how the network should be subdivided. The focus of their approach is limited to the reduction of the first component of the computation time, i.e.…”

Time Coefficient Estimation for Hourly Origin-Destination Demand from Observed Link Flow Based on Semidynamic Traffic Assignment

“…Tsekeris and Stathopoulos [15] proposed estimating the dynamic OD matrix by an efficient algorithm in an entropy modelling. Etemadnia and Abdelghany [16] proposed a dynamic OD demand estimation through dynamic traffic assignment on the basis of the least square method. This method provides estimates for about 10-minute intervals at peak hours by dividing an urban network into several subareas.…”