Transfer penalties in multimodal public transport networks

García-Martínez, Andrés; Jiménez, Rocío Cascajo; Jara-Dı́az, Sergio; Chowdhury, Subeh; Monzón, Andrés

doi:10.1016/j.tra.2018.01.016

Cited by 52 publications

(57 citation statements)

References 32 publications

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“…Next, respondents were requested to choose a scenario that they were familiar with and to complete the SP survey [20][21][22]. The alternatives are described by four attributes: Commute time, transfer waiting time, and transfer-walking time.…”

Section: Survey Designmentioning

confidence: 99%

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Typical Combined Travel Mode Choice Utility Model in Multimodal Transportation Network

Liu

Chen

et al. 2019

Sustainability

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The primary purpose of this paper is to explore the mechanism of combined travel mode choice in multimodal networks. To meet the objective, stated preference survey and revealed preference survey are designed under short, middle, and long travel distance scenarios. Data including travelers’ socio-economic/personal information, trip characteristics, and mode choice are collected and analyzed. To recognize the influential factors of mode choice, a nested logit model is established. A value of time estimation and sensitivity analysis are conducted to quantify the influencing degree. The results reveal that cost has a significant influence on the short-distance travel mode; waiting time is perceived as the most important factor in short-distance scenario, and transfer-walking time as the most significant in middle and long distance scenario. Moreover, the traveler is more sensitive to the decrease of the transfer walking time than increase. Regarding socio-economic/personal information, travelers aged 40–50 prefer to choose combined travel mode than other ages; female travelers have a greater acceptance of metro-based transfer travel than male; individuals with higher economic level have a positive image of metro than bus.

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Section: Survey Designmentioning

confidence: 99%

“…Its advantages are: Lower cost, quick delivery, benefit for collecting and analysis data, randomness of respondents, and ease of switching scenario. However, there are still some limitations: Multiple submissions, lack of on line experience, and instructions [22]. Thus, the on-site survey was supplemented.…”

Section: Survey Implementationmentioning

confidence: 99%

Typical Combined Travel Mode Choice Utility Model in Multimodal Transportation Network

Liu

Chen

et al. 2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…An online survey was finally used to conduct data collection as it had some advantages which paper survey didn't have, such as time and cost reductions, rapid delivery and response, flexible question design (conditional questions), and automated data statistics [7]. By the end of 2016, 133 metro stations and seven metro lines were operating in Nanjing, covering 224 km [3].…”

Section: Survey Design and Implementationmentioning

confidence: 99%

“…Thus, it is a sustainable choice for commuters to take. However, transfer disutility always makes it less competitive compared to a direct way [6,7].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Transfer Commuting Attitudes Using a Market Segmentation Approach

Bai

Yue

2018

Sustainability

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Abstract:Commuting by transfer in the public transit network is a green travel choice compared to private cars which should be encouraged when direct transit lines cannot take the commuters to their destinations. Therefore, transfer commuting attitudes are important for finding appropriate ways to attract more transfer commuters. Firstly, since attitudes are usually unobserved, a combined revealed preference (RP) and stated preference (SP) survey was conducted in Nanjing, China to obtain the observed attitudinal variables. Then the market segmentation approach including the factor analysis, the structural equation modelling (SEM) model and the K-means clustering method was used to identify the underlying attitudinal factors and variables and analyze the interrelationship between them. Six segments were identified by four key factors including the willingness to transfer, the sensitivity to time, the need for flexibility and the desire for comfort. The sensitivity to time is the most important factor for commuters influencing their willingness to transfer. The socio-economic features of each segment were also analyzed and compared. The result shows that socio-economic features have a great impact on the willingness to transfer. Corresponding policy and strategy implications to increase transfer commuting proportion were finally proposed.

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“…The transfer penalty value between subway lines was estimated to be about 5.35 minutes per transfer, and thus the study suggested that the transfer penalty value varies with the number of transfers. Garcia-Martinez et al (2018) analyzed with commuters the correlation between transfer penalty and the elements of walking travel time, wait time, and so on. Their study suggested that transfer penalty is influenced by temporal elements, such as transfer wait time and transfer walking time, and non-temporal elements, such as transfer to other transport modes and complexity.…”

Section: Related Researchmentioning

confidence: 99%

Improved Public Transit Routing Algorithm for Finding the Shortest K-Path

Jeon

Nam

Chulmin

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Most of the existing public transit routing algorithms were developed on the basis of graph theory. Recently, algorithms are being developed that can compute for O-D public transit paths by using timetable information only, not using network structure consisting of nodes and links. The timetable-based public transit routing algorithm produces one shortest path to destination, using departure time and arrival time by stop. But it has limitations in reflecting additional factors, such as transfer penalty and alternative path selection, in the process of path calculation. In addition, since public transit passengers tend to choose one among various alternative paths, it is necessary to calculate multiple paths rather than a single path as in the existing methods. Therefore, this study proposes an improved RAPTOR algorithm that can consider transfer penalty and produce multiple paths, while it is based on RAPTOR, the existing timetable-based public transit routing algorithm. The transfer penalty was applied at the point of transfer, and differently according to transfer types. As a result of analyzing computed paths of the algorithms before and after improvement, it was found that computed paths with the improved RAPTOR algorithm proposed by this study were more similar to Seoul public transit passengers' actual travel paths than computed paths by the existing RAPTOR alone.</p>

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Transfer penalties in multimodal public transport networks

Cited by 52 publications

References 32 publications

Typical Combined Travel Mode Choice Utility Model in Multimodal Transportation Network

Typical Combined Travel Mode Choice Utility Model in Multimodal Transportation Network

Analyzing Transfer Commuting Attitudes Using a Market Segmentation Approach

Improved Public Transit Routing Algorithm for Finding the Shortest K-Path

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