Predicting disruptions and their passenger delay impacts for public transport stops

Yap, Menno; Cats, Oded

doi:10.1007/s11116-020-10109-9

Cited by 30 publications

(22 citation statements)

References 32 publications

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“…This paper represents a first step in the analysis of different types of disruptions and several future directions are possible, such as using a different disruption identification method or a different impact function, for instance including crowding factors in affected lines ( 13 ). Testing the same methodology on big disruptions, involving the whole network, can help to make a better distinction between small and big disruptions.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the impact of a disruption is primarily measured based on the whole traffic in the network ( 2 , 9 , 10 ). Computing the impact on passengers based on realized automated fare collection (AFC) and realized disruption (i.e., a complementary approach based on observed passenger choices) has been proposed concurrently to the present work, for larger and planned disruptions ( 13 ). To the best of the authors’ knowledge, the impact is never analyzed on single ODs or considering the entire choice set of a user.…”

Section: State Of the Artmentioning

confidence: 99%

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From Delay to Disruption: Impact of Service Degradation on Public Transport Networks

Marra

Corman

2020

Transportation Research Record: Journal of the Transportation R

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Public transport networks (PTN) are affected daily by different types of disturbances. In fact, between a single delay and a long service interruption, there is a range of disruptions with different impacts, depending on their characteristics. Despite this, in literature, the common definition of disruption is a link closure for a certain amount of time. Low interest is given to different types of disruptions or to the connection between delays and disruptions. In addition, in multimodal PTN a physical link closure is not always observable, but rather people experience delays or cancelled stops on different lines. The aim of this work is to explore the relationship between delays and disruptions, analyzing different degrees of disruptions, in relation to duration, delay, size, and network characteristics. Real disturbances of the PTNs in Zürich and Bern, Switzerland, are analyzed to identify disruptions with different characteristics. Therefore, the disruption impact is computed on simulated origin–destinations (ODs), based on the sets of possible paths with and without the disruption. For this purpose, a choice set generation algorithm is used. Finally, relationships between the disruption characteristics and the impact are analyzed to identify the main features of a disruption.

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Section: Discussionmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

From Delay to Disruption: Impact of Service Degradation on Public Transport Networks

Marra

Corman

2020

Transportation Research Record: Journal of the Transportation R

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“…Non-equilibrium is a non-iterative approach which is appropriate for the simulation of unexpected events, in a non equilibrium state the passengers have not reached the most efficient plan in the network by iteration. Practically, there is insufficient empirical data available from past experienced disruptions to replicate them and study their impacts in simulation models [8]. Instead, a simulation can assist in choosing effective actions in real life, by means of an analysis of their respective results, including quantifying delays and associated monetary values for passengers and the whole PT network.…”

Section: Problem Description and Contributionmentioning

confidence: 99%

Multi-modal management actions for public transport disruptions: an agent-based simulation

Rahimi

Ghandeharioun

Kouvelas

et al. 2021

2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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In a public transport disruption, management of the disruption plays a significant role in reducing passengers' inconvenience. This research aims to evaluate how the delay that passengers experience in a public transport disruption can be reduced, using multi-modal management actions. We consider various traffic management actions implemented in real-time to mitigate the downsides of a disruption, including route and mode adjustments, and capacity increase. Transport operators can implement disruption management actions, such as assigning more vehicle capacity/frequency to the running services; and can inform passengers about the disruption. The benefits of providing updated information to mitigate the downsides of the disruption are large, and could possibly lead to a better mode and route choice. The simulation of the impacts and dynamics of a reallife disruption in our study is performed by an agent-based framework. We demonstrate how much delay could be avoided by implementing multi-modal management actions and analyzing the extent to which they change passengers' travel behavior, in terms of travel mode and travel time. Results indicate that employing multi-modal management can lead to a reduction of 18% in the delay of affected passengers, when information and capacity management are used together.

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“…Xu et al [19] proposed the last train delay management especially for serious effect on transfer passengers' regular trips, using bi-objective Mixed Integer Programming (MIP) model and Genetic Algorithm (GA). Yap et al [20] propose a supervised learning approach to predict multiple types of disruption occurrence at different stations of a public transport network and measure the impacts on passenger delays.…”

Section: Related Workmentioning

confidence: 99%

Contributing Factors for Delays during the Morning Commute Hours and the Impact of the Spread of COVID-19 for Metropolitan Train Lines in Japan

Ohshima¹,

Yamamoto²

2021

JTTs

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The present study aims to conduct 2 types of statistical analysis to reveal the impact of the spread of COVID-19 on train delays by comparing the potential contributing factors before, during and after the outbreak of the virus in the metropolitan train lines in Japan. First of all, the result of the present study clearly revealed the changes in contributing factors for train delays caused by the spread of COVID-19. Specifically, the contributing factors for train delays changed due to the decrease of passengers by the effect of the outbreak of the virus. Additionally, though large terminal stations were considered to be a major contributing factor in causing and increasing train delays in the past, this was not the case after the spread of COVID-19. Therefore, under such conditions, it is more effective to make improvements in small to medium stations and tracks rather than terminal stations. Furthermore, as the decrease in passengers also decreased train delays in commuter lines going to the suburbs due to the spread of COVID-19, the contributing factor for such lines is the excessive number of passengers. Therefore, as for countermeasures for train delays after the effects of COVID-19, it is necessary to disperse passengers in order to avoid passengers concentrating in the same time zones and train lines.

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Predicting disruptions and their passenger delay impacts for public transport stops

Cited by 30 publications

References 32 publications

From Delay to Disruption: Impact of Service Degradation on Public Transport Networks

From Delay to Disruption: Impact of Service Degradation on Public Transport Networks

Multi-modal management actions for public transport disruptions: an agent-based simulation

Contributing Factors for Delays during the Morning Commute Hours and the Impact of the Spread of COVID-19 for Metropolitan Train Lines in Japan

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