Open transport data for maximising reuse in multimodal route planners: a study in Flanders

Colpaert, Pieter; Compernolle, Mathias Van; Walravens, Nils; Mechant, Peter; Adriaenssens, Jan; Ongenae, Femke; Verborgh, Ruben; Mannens, Erik

doi:10.1049/iet-its.2016.0269

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Hong [28] proposed an improved large neighbourhood search algorithm to solve the dynamic VRP with hard time window. Colpaert et al [29] studied the data published from the Department of Transport and Public Works in Flanders for maximum reuse. The purpose is to reduce the cost during transportation.…”

Section: Literature Reviewmentioning

confidence: 99%

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Automatic generation of optimal road trajectory for the rescue vehicle in case of emergency on mountain freeway using reinforcement learning approach

Yang

Yuan

et al. 2021

IET Intelligent Trans Sys

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Rapid rescue response has the highest priority in case of emergency randomly happening on the freeway network, which allows rescue vehicles to have many trajectory options. Searching for the fastest way is not easy within a short time after traffic accident happens especially for the mountainous area with special characteristics such as limited traffic capacity, enclosed internal space and so on. Here, road segment model is proposed to determine smallest road segment covering possible rescue ways. Other than traditional optimal search methods, modified reinforcement-leaning is introduced to find the optimal road trajectory. The proposed methods are tested in the freeway of Qinling Tunnel group, Xihan Freeway of Shaanxi province, China as a case study. Compared with traditional shortest path method, the rescue vehicle arrival time to the accident location is shortened from 22.9 to 6.5 min and dissipation time is also shortened from 52.4 to 25.6 min. Both of them show the proposed road trajectory could improve the rescue effectiveness and reduce the influence to road network. Successful application of these case study shows they could probably extend to use to other scenarios and contribute to improve the intelligence transportation system.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Literature Reviewmentioning

confidence: 99%

“…Colpaert et al. [29] studied the data published from the Department of Transport and Public Works in Flanders for maximum reuse. The purpose is to reduce the cost during transportation.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic generation of optimal road trajectory for the rescue vehicle in case of emergency on mountain freeway using reinforcement learning approach

Yang

Yuan

et al. 2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Currently, long-distance freight transport is shifting towards multimodal transport, the combination of multiple freight transport modes [9][10][11][12][13]. Multimodal transport can effectively enhance transport efficiency, reduce freight losses, lower transport costs, and ease traffic congestion [14][15][16][17][18][19][20][21][22][23][24]. As an important means of improving logistics efficiency, the reasonable path design and planning (PDP) and investment and construction mode (ICM) of multimodal transport networks, which rely on the integration of valuable big data on freight services, can reduce the economic and time costs of freight transport to the maximum degree, and help freight demanders, as well as multimodal freight transport platforms, obtain the maximal profit.…”

Section: Introductionmentioning

confidence: 99%

Path Design and Planning and Investment and Construction Mode of Multimodal Transport Network Based on Big Data Analysis

Wang

2022

Discrete Dynamics in Nature and Society

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Currently, long-distance freight transport is shifting towards multimodal transport, the combination of multiple freight transport modes. Multimodal transport enables enterprises with the same logistics function to operate on the same level of the supply chain. Through horizontal cooperation, these enterprises can give play to their advantages, make up their deficiencies, improve service levels, reduce cost input, and thereby enhance market status. Therefore, multimodal transport is an intensive development model that promotes the alliance between giants. The reasonable path design and planning (PDP) and investment and construction mode (ICM) of the multimodal transport network help freight demanders, as well as multimodal freight transport platforms, obtain the maximum profit. Therefore, this paper explores the PDP and ICM of the multimodal transport network based on big data analysis. Firstly, the influencing factors and behavioral features of multimodal transport were deeply examined, drawing on the logit model and the big data on multiple freight services, namely, railway transport, highway transport, waterway transport, and airway transport. After classifying the freights, the authors analyzed the modeling and decision-making of path design and optimization (PDO) for multimodal transport network. The proposed model was proved effective through experiments. This paper theoretically explores the goals, principles, and needs of path selection in the modern transportation industry. In a realistic sense, the research findings help decision-makers optimize their decisions on the multimodal transport network and operate the network at the minimum transport cost.

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Characterizing user behavior in journey planning

et al. 2020

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Open transport data for maximising reuse in multimodal route planners: a study in Flanders

Cited by 5 publications

References 8 publications

Automatic generation of optimal road trajectory for the rescue vehicle in case of emergency on mountain freeway using reinforcement learning approach

Automatic generation of optimal road trajectory for the rescue vehicle in case of emergency on mountain freeway using reinforcement learning approach

Path Design and Planning and Investment and Construction Mode of Multimodal Transport Network Based on Big Data Analysis

Characterizing user behavior in journey planning

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