Analysis of rate of train overcrowding by stored data at ticket gates and load weight units

Kubota, Takato; Morita, Yasutomo; Ota, Masafumi; Furuya, Satoshi; Ieda, Hitoshi

doi:10.2208/journalip.25.641

Cited by 1 publication

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“…In these investigations, investigators on platforms count and record the number of passengers on each train. Also, in order to grasp the transportation situation, train operating companies have begun to estimate the number of passengers on each car based on its weight and passengers' trip data stored at automatic ticket gate (Kubota et al [1]). …”

Section: Workflow Of Timetabling and Problems Associated With Timetabmentioning

confidence: 99%

Interactive timetabling system with function of estimating train congestion on timetable planned

Tatsui¹,

Kunimatsu²,

Sakaguchi³

2017

Int. J. TDI

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It is important for railway operators to make suitable timetables. On the assumption that passengers' train paths are invariant even if the timetable changes, the timetables are determined according to the various statistical data under the current timetable. In planning the timetable, it is difficult to grasp the change of passengers' paths due to timetable modifications because it is too complex. In this paper, we propose a framework of timetabling with due consideration of the path change and propose a practical timetabling system which can quickly estimate the congestion of each train by using passengers' origin-destination (OD) data collected by automatic ticket checkers. With the system, timetable planners can interactively make and modify timetables by trial and error while confirming the congestion, and finally, they can reach the most preferable one. In order to realize such an interactive system, it is important to develop a fast estimation algorithm of train congestion. We developed a new shortest path search algorithm to determine which trains each passenger gets on. The algorithm devised based on the Dijkstra method has two features. First, the shortest path search from each node to all the destination stations in the composed graph network is executed only once. Second, overlapping the path searches are omitted using stored information of the shortest paths which have already been searched. By applying this algorithm, it took only about 10 s to estimate the train congestion under a timetable of the whole day on an urban commuter line.

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Section: Workflow Of Timetabling and Problems Associated With Timetabmentioning

confidence: 99%

Interactive timetabling system with function of estimating train congestion on timetable planned

Tatsui¹,

Kunimatsu²,

Sakaguchi³

2017

Int. J. TDI

View full text Add to dashboard Cite

show abstract

Analysis of rate of train overcrowding by stored data at ticket gates and load weight units

Cited by 1 publication

References 0 publications

Interactive timetabling system with function of estimating train congestion on timetable planned

Interactive timetabling system with function of estimating train congestion on timetable planned

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