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

Rahimi, Mahsa; Ghandeharioun, Zahra; Kouvelas, Anastasios; Corman, Francesco

doi:10.1109/mt-its49943.2021.9529305

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Capacity management can be interpreted as both adding to the capacity of running services, for example, longer buses or trains, or adding to the frequency of running services. Such an analysis has also been investigated for one specific transport line within limited time and locations in Rahimi et al ( 14 ). Their experiment showed that adding to the frequency of service on a line leads to less delay than adding capacity to that service.…”

Section: Disruption Management Actionsmentioning

confidence: 99%

“…Previous work also used approaches from the perspective of the network operator and associated cost incurred by them ( 11 , 12 ), based on traffic queue management ( 13 ), information strategies for public transport passengers ( 14 ), real-time information for road disruption ( 15 ), and bus bridging for transport disruption ( 16 , 17 ) to tackle the issue of disruption in road and public transport networks. In summary, the performance of the individual modes (e.g., railway or road traffic) is optimized separately.…”

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confidence: 99%

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Evaluating the Effects of Disruptions on the Behavior of Travelers in a Multimodal Network Utilizing Agent-Based Simulation

Siegrist

Büchel

Corman

2022

Transportation Research Record: Journal of the Transportation R

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Disruptions in transport networks have major adverse implications on passengers and service providers, as they can yield delays, decreased productivity, and inconvenience for travelers. Previous studies have considered the vulnerability of connections and infrastructures. Although such studies provide insights on general disruption management approaches, there is a lack of knowledge concerning integrated multi-level traffic management and its effects on travelers to reduce the impacts of disruptions. Integrated multi-level traffic management refers to coordinating individual network operations to create an interconnected mobility management system. This study sought to assess the management of road disruption utilizing multi-level disruption management. Multi-level disruption management is proposed that integrates an information dissemination strategy and allows changing the functionality of parking spaces to traffic lanes to facilitate the movement of travelers. The capacity/frequency of public transport vehicles is also increased to help travelers reach their destinations by changing to public transport mode. To achieve such goals, an extension to an agent-based simulation was developed. Numerical experiments are applied to a part of the city of Zürich. The results indicate that the proposed approach, multi-level disruption management in a multimodal network, can shorten travelers’ delays, especially comparing the effects of disruption management. Results show heterogeneity of behavior among agents. Adding lanes as a disruption management enhances the usage of car-mode by all agents, whereas it reduces the usage of car-mode by the directly affected agents, those who cannot pass the disrupted roads. In the presence of full information and increased capacity of transit vehicles, delay is reduced by 47%.

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Section: Disruption Management Actionsmentioning

confidence: 99%

mentioning

confidence: 99%

Evaluating the Effects of Disruptions on the Behavior of Travelers in a Multimodal Network Utilizing Agent-Based Simulation

Siegrist

Büchel

Corman

2022

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

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State of the art of passenger redirection during incidents in public transport systems, considering capacity constraints

Bachmann,

Tsakarestos,

Busch

et al. 2023

Public Transp

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This paper gives a comprehensive insight into the investigations done in passenger redirection during incidents in public transport systems. In public transport operations, incidents such as traffic accidents, deployment of emergency forces, or technical failures happen every day and disrupt the service. Most of the investigations done in the field of incident management focus on the readjustment of the supply towards the incident situation and are therefore referred to as supply-centric part of incident management. However, especially in recent years, more and more investigations have also been done on the passenger-centric part of incident management. These rather focus on the effects of incidents on passengers or even include them in the solution of the incident situation, either by informing them adequately about the given situation (passive redirection) or by providing them with concrete path advice (active redirection). The results show that adequate passenger information during incidents can reduce the average delay of affected passengers and support the recovery of the public transport system. This improves the reliability of a public transport system and boosts its attractiveness.

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Spatio-temporal characterisation and compensation method based on CNN and LSTM for residential travel data

Alhudhaif,

Polat

2024

PeerJ Computer Science

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Currently, most traffic simulations require residents’ travel plans as input data; however, in real scenarios, it is difficult to obtain real residents’ travel behavior data for various reasons, such as a large amount of data and the protection of residents’ privacy. This study proposes a method combining a convolutional neural network (CNN) and a long short-term memory network (LSTM) for analyzing and compensating spatiotemporal features in residents’ travel data. By exploiting the spatial feature extraction capability of CNNs and the advantages of LSTMs in processing time-series data, the aim is to achieve a traffic simulation close to a real scenario using limited data by modeling travel time and space. The experimental results show that the method proposed in this article is closer to the real data in terms of the average traveling distance compared with the use of the modulation method and the statistical estimation method. The new strategy we propose can significantly reduce the deviation of the model from the original data, thereby significantly reducing the basic error rate by about 50%.

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

Cited by 3 publications

References 10 publications

Evaluating the Effects of Disruptions on the Behavior of Travelers in a Multimodal Network Utilizing Agent-Based Simulation

Evaluating the Effects of Disruptions on the Behavior of Travelers in a Multimodal Network Utilizing Agent-Based Simulation

State of the art of passenger redirection during incidents in public transport systems, considering capacity constraints

Spatio-temporal characterisation and compensation method based on CNN and LSTM for residential travel data

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