文章基本信息

标题：A maximum entropy-least squares estimator for elastic origin-destination trip matrix estimation
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作者：Chi Xie ; Chi Xie ; Kara M. Kockelman 等
期刊名称：Procedia - Social and Behavioral Sciences
印刷版ISSN：1877-0428
出版年度：2011
卷号：17
页码：189-212
DOI：10.1016/j.sbspro.2011.04.514
语种：English
出版社：Elsevier
摘要：AbstractIn transportation subnetwork-supernetwork analysis, it is well known that the origin-destination (O-D) flow table of a subnetwork is not only determined by trip generation and distribution, but also by traffic routing and diversion, due to the existence of internal-external, external-internal and external-external flows. This result indicates the variable nature of subnetwork O-D flows. This paper discusses an elastic O-D flow table estimation problem for subnetwork analysis. The underlying assumption is that each cell of the subnetwork O-D flow table contains an elastic demand function rather than a fixed demand rate and the demand function can capture all traffic diversion effect under various network changes. We propose a combined maximum entropy-least squares (ME-LS) estimator, by which O-D flows are distributed over the subnetwork so as to maximize the trip distribution entropy, while demand function parameters are estimated for achieving the least sum of squared estimation errors. While the estimator is powered by the classic convex combination algorithm, computational difficulties emerge within the algorithm implementation until we incorporate partial optimality conditions and a column generation procedure into the algorithmic framework. Numerical results from applying the combined estimator to a couple of subnetwork examples show that an elastic O-D flow table, when used as input for subnetwork flow evaluations, reflects network flow changes significantly better than its fixed counterpart.
关键词：Origin-destination trip table;Elastic demand;Maximum entropy;Least squares;Subnetwork analysis;Convex combination;Unconstrained optimization;Column generation