Friday, April 29, 2011

OPTIMAL LOCATION OF ROUTE AND STOPS OF PUBLIC TRANSPORTATION

by Tatsuya Kishimoto, Shutaro Kawasaki, Nobuhiko Nagata, and Ryosuke Tanaka

This study is concerned with the optimal location of route and stops of LRT and the effects of LRT in central urban areas. In order to implement LRT, it is important to locate LRT lines and stations in appropriate places and to estimate the effects of LRT. In this study we accordingly address the optimal layout of LRT route network and estimation of the effects. First, for the estimation of the effects of introducing LRT, a new evaluation model based on space syntax theory is proposed. Two indexes which explain traffic flows and the character of location are introduced. One is the average travel cost from nodes to nodes, which corresponds to the depth index of space syntax. The other is the amount of through traffic on each link, which is the frequency that a certain link (a road, train or LRT link) is chosen as a part of route for traveling from nodes to nodes in the city. The former is named C_depth value, and the latter is named Flow value.
Second, the optimal location of LRT route and stops, which minimizes the mean cost in whole area, is considered. The case of the new LRT system in Maebashi city in Japan is examined. Optimal location and its effects are investigated by case study in Maebashi City. Two types of optimal location are studied and compared. In both cases, Flows on LRT and trains are still lower than the Flow by cars on main streets. Thus, one conclusion may be that in order to promote LRT and train usages, policies, such as road-pricing and discount of LRT fare, are needed.


more Space Syntax papers:

A STUDY ON THE CORRELATION BETWEEN PEDESTRIAN NETWORK AND PEDESTRIAN VOLUME ACCORDING TO LAND USE PATTERN

MEASURING THE CONFIGURATION OF STREET NETWORKS: the Spatial profiles of 118 urban areas in the 12 most populated metropolitan regions in the US

Evaluated Model of Pedestrian Movement Based on Space Syntax, Performance Measures and Artificial Neural Nets

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