Gradient method for the estimation of travel demand using traffic counts on the large scale network

Tae Jun Ha, Seungjae Lee, Jonghyung Kim, Chungwon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this study, the surveyed Trip Length Frequency Distribution (TLFD) is determined as a criterion for the reliability of evaluating the true O/D matrix. The surveyed TLFD can be used to check the similarity between the surveyed (true) Trip Length Distribution and the Trip Length Distribution of the estimated O/D matrix by the traffic counted models. When the surveyed TLFD is similar to the estimated TLFD, the reliability and correctness of the estimated O/D are high. Therefore, the objective of this paper is the development of the travel demand (O/D matrix) estimation using traffic counts on the large-scaled network. The Gradient Method is used for the model and the multi-class assignment technique is used for the equilibrium loading procedure in the model. This leads to the good guideline to the usage of the traffic count based O/D estimation in practice and gives a confidence to the transport planner. It is because the traffic counted O/D estimation models gives multiple solutions by its characteristics. In this paper we analyze the merits and demerits in each of a single-class based model and a multi-class based model in a large scale network. As a result, we have concluded that the multi-class based model has a closer value to the surveyed (true) TLFD than the TLFD of the estimated O/D matrix by the single-class based gradient method.

Original languageEnglish
Title of host publicationAdvances in Multimedia Modeling - 13th International Multimedia Modeling Conference, MMM 2007, Proceedings
Pages599-605
Number of pages7
EditionPART 2
DOIs
StatePublished - 2007
Event13th International Multimedia Modeling Conference, MMM 2007 - Singapore, Singapore
Duration: 9 Jan 200712 Jan 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4352 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Multimedia Modeling Conference, MMM 2007
Country/TerritorySingapore
CitySingapore
Period9/01/0712/01/07

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