Abstract
Travel demand information is one of the most important inputs in transportation planning. Today, the access to origin-destination (OD) matrix using traffic volume count information has caught the researchers’ attention because these methods can estimate OD matrices based on the flow volume in the links of network with a high accuracy at a much lower cost over a short time. In such algorithms, the number and location of links are one of the main parameters for traffic volume count; hence a better OD matrix can be achieved by choosing the optimum links. In this paper, an algorithm is presented to determine the number and location of optimum links for traffic volume count. The method specifies the minimum links to cover the maximum elements of OD matrix. This algorithm is especially useful for the estimation of ODM through gradient method, because only the O-D pairs covered by link traffic counts are adjusted and estimated in the gradient method. The algorithm is then scripted via EMME/2 and FoxPro and implemented for a large-scale real network (Mashhad). The results show that about 95% of the ODM can be covered and then adjusted by counting only 8% of the links in the network of Mashhad.
Highlights
Travel demand information expressed in form of origin-destination (OD) matrices are one of the most important and essential inputs in transportation planning and engineering, considered as the basic information for design and management of transportation systems
Among a variety of approaches presented for estimating the Origin-Destination Matrix (ODM) using traffic volume count data, the gradient method proposed by Spiess [1] is more effective to solve real problems on large scales
In the algorithm for determining optimum link traffic volume counts, the stopping criterion can be any of three conditions below: 1) The “number of optimum links” criterion that, is the counter l in the algorithm
Summary
Travel demand information expressed in form of origin-destination (OD) matrices are one of the most important and essential inputs in transportation planning and engineering, considered as the basic information for design and management of transportation systems. Wang and Mirchandani used a Bayesian statistical method to select the location of links through a decision-making technique according to the previous data and observations on traffic volume counts [8] They developed the model and evaluated it. Abd-al-Shakour and Sashama noticed the calculation of optimum link traffic counts using screen lines based on covering the all paths with a certain high traffic volume in order to cover the maximum volume [10] They suggested two models of which the first one determined the optimum number of links and the second one specified their locations. An algorithm is proposed to obtain optimum network links for traffic volume counting and estimation (adjustment) of the origin-destination matrix. Step 7: If “stopping criterion” is not met, go to step 2; otherwise stop
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