Bus schedule coordination for the last train service in an intermodal bus-and-train transport network

Dou, Xueping; Meng, Qiang; Guo, Xiucheng

doi:10.1016/j.trc.2015.09.006

Cited by 43 publications

(9 citation statements)

References 19 publications

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“…Application of ESA next to MPOM deepens a difference to the models presented by [2,19] and [20] as well. Technically, ESA is implemented as a macro in Microsoft Excel.…”

Section: Model Formulationmentioning

confidence: 99%

“…Presumption d) is a reason why it is not possible to realize so called 'timed transfer' [2]. It means that all services (vehicles) of LDT as well as UPT arrive short before defined moment.…”

Section: Fig 1 Illustration Of Problem With Nttij or Nttjimentioning

confidence: 99%

“…Mixed integer linear programming model is proposed for synchronization of last evening trains and buses by [2]. All interchange nodes served by one trunk (backbone) line must be synchronized together.…”

Section: Fig 1 Illustration Of Problem With Nttij or Nttjimentioning

confidence: 99%

“…, 02001 (2018) https://doi.org/10.1051/matecconf /201823902001 MATEC Web of Conferences 239 2018 TransSiberia All departures UPTDu i of service u on the UPT line i can be found by using of dwell times DTi, see formula (2).…”

Section: Basic Model Variablesmentioning

confidence: 99%

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Timetable synchronisation: Urban public transport in busy hubs of long-distance transport

Bulíček

2018

MATEC Web Conf.

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The article is focused on timetable synchronization problem. Interconnection of urban public transport and long-distance transport in busy hubs is researched. Intension is to find adequate time positions of urban public transport arrivals and departures with an effort to minimize passengers’ time loss related to this interchange. Timetable of long distance transport is considered as constant (given). Timetable of urban public transport is result of optimization (and presupposed to be periodic). Nonlinear integer mathematical programming model is formulated. Model is implemented in Microsoft Excel Solver. Parallel way based on exhaustive-search algorithm is introduced as well. This algorithm provides extended set of output data able to be used in transport planning. Two different criterions are proposed. Each interchange (pair of vehicles) has the same weight in the case of uniform approach. Intensity approach considers interchanges as weighted by numbers of interchanging passengers. Some additional modifications of proposed model are mentioned for possibility to regard some important technological aspects and to reach more individualized solution. Model is illustrated by the case study located to Pardubice main station as a hub of passenger railway transport. Interface with urban public transport is mentioned as an illustrational example of synchronization.

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“…Application of ESA next to MPOM deepens a difference to the models presented by [2,19] and [20] as well. Technically, ESA is implemented as a macro in Microsoft Excel.…”

Section: Model Formulationmentioning

confidence: 99%

Section: Fig 1 Illustration Of Problem With Nttij or Nttjimentioning

confidence: 99%

Section: Fig 1 Illustration Of Problem With Nttij or Nttjimentioning

confidence: 99%

Section: Basic Model Variablesmentioning

confidence: 99%

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Timetable synchronisation: Urban public transport in busy hubs of long-distance transport

Bulíček

2018

MATEC Web Conf.

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“…For reducing transfer time in real time, Chowdhury and Chien 19 optimized dynamic dispatching vehicles at transfer stations by connecting four transit routes. Dou et al 20 researched the coordination between buses and trains. These studies challenged special physical network structures from different perspectives.…”

Section: Introductionmentioning

confidence: 99%

A dynamic programming approach to synchronize train timetables

Tian

Niu

2017

Advances in Mechanical Engineering

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This article focuses on synchronizing timetables of train services at a rail transfer station. The main aim is to determine an optimal schedule of train services, given that the departure and arrival times of some particular trains are known. An exponential utility function is introduced to measure the synchronization levels between different train services. A nonlinear integer programming model is proposed to achieve the objective of a synchronized timetable. A dynamic programming approach is then designed to solve the developed model. Finally, a numerical example with real-world datasets is implemented to demonstrate the effectiveness of the proposed approaches.

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Tactical Planning of Public Transport Services: Multi-modal Synchronization

Gkiotsalitis

2022

Public Transport Optimization

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Bus schedule coordination for the last train service in an intermodal bus-and-train transport network

Cited by 43 publications

References 19 publications

Timetable synchronisation: Urban public transport in busy hubs of long-distance transport

Timetable synchronisation: Urban public transport in busy hubs of long-distance transport

A dynamic programming approach to synchronize train timetables

Tactical Planning of Public Transport Services: Multi-modal Synchronization

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