Dynamic System Optimal Routing in Multimodal Transit Network

Ma, Tai-Yu; Lebacque, Jean-Patrick

doi:10.3141/2351-09

“…Quantum computing algorithms are also being explored for dynamic rerouting, harnessing the power of quantum mechanics to solve complex optimization problems more efficiently in real-time scenarios. The dynamic system optimal routing model for multimodal transit systems represents a significant advancement, addressing congestion issues in traditional shortest-path-based systems [17].…”

Section: Algorithms In Dynamic Reroutingmentioning

confidence: 99%

Transportation Transformed: A Comprehensive Review of Dynamic Rerouting in Multimodal Networks

Pratap

2024

Preprint

0

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<p>The emergence of dynamic rerouting in multi-modal transportation networks has emerged as a crucial area in operations research, revolutionizing routine optimization. The review study analyzes multiple research publications on algorithms and techniques related to dynamic rerouting to give a thorough summary of the state of research in this field and provide future suggestions. The research paper explains the importance of dynamic rerouting in modern transportation systems and recognizes its critical role in tackling issues like accidents, traffic congestion, and infrastructure constraints. In addition, the review examines the development of dynamic rerouting techniques by examining several studies to uncover the theoretical foundation, technological developments, and effects of the practices on various forms of transportation. The paper emphasizes the potential of technological advancements such as artificial intelligence, the Internet of Things, and big data in transforming routing efficiencies. Further, the review presents specific difficulties and best practices for each mode of transportation, highlighting the many uses of dynamic rerouting in air, sea, rail, and road transportation. The review also digs deeper into the integration barriers common in multi-modal networks, highlighting successful case studies that overcome these obstacles as well as strategic approaches and regulatory modifications. Lastly, the research paper assesses the impact of dynamic rerouting on urban development, sustainability, and potential directions for future research such as the integration of large language models. The comprehensive literature review incorporates multiple research perspectives to offer significant insights into the efficacy, challenges, and potential future pathways for dynamic rerouting within multi-modal transportation networks.</p>

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“…Quantum computing algorithms are also being explored for dynamic rerouting, harnessing the power of quantum mechanics to solve complex optimization problems more efficiently in real-time scenarios. The dynamic system optimal routing model for multimodal transit systems represents a significant advancement, addressing congestion issues in traditional shortest-path-based systems [17].…”

Section: Algorithms In Dynamic Reroutingmentioning

confidence: 99%

Transportation Transformed: A Comprehensive Review of Dynamic Rerouting in Multimodal Networks

Pratap

2024

Preprint

0

View full text Add to dashboard Cite

<p>The emergence of dynamic rerouting in multi-modal transportation networks has emerged as a crucial area in operations research, revolutionizing routine optimization. The review study analyzes multiple research publications on algorithms and techniques related to dynamic rerouting to give a thorough summary of the state of research in this field and provide future suggestions. The research paper explains the importance of dynamic rerouting in modern transportation systems and recognizes its critical role in tackling issues like accidents, traffic congestion, and infrastructure constraints. In addition, the review examines the development of dynamic rerouting techniques by examining several studies to uncover the theoretical foundation, technological developments, and effects of the practices on various forms of transportation. The paper emphasizes the potential of technological advancements such as artificial intelligence, the Internet of Things, and big data in transforming routing efficiencies. Further, the review presents specific difficulties and best practices for each mode of transportation, highlighting the many uses of dynamic rerouting in air, sea, rail, and road transportation. The review also digs deeper into the integration barriers common in multi-modal networks, highlighting successful case studies that overcome these obstacles as well as strategic approaches and regulatory modifications. Lastly, the research paper assesses the impact of dynamic rerouting on urban development, sustainability, and potential directions for future research such as the integration of large language models. The comprehensive literature review incorporates multiple research perspectives to offer significant insights into the efficacy, challenges, and potential future pathways for dynamic rerouting within multi-modal transportation networks.</p>

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“…Previous multimodal transport networks have been modeled with weighted nodes and edges in a multilayer structure. For example, Ma and Lebacque (2013) provided a multimodal transport network with a multilayer structure comprised of a combination of unimodal subnetworks of bus and metro. However, it was tested with a simple network unable to fully reflect the complexity of true transport structures.…”

Section: Liter Ature Re Vie Wmentioning

confidence: 99%

Framework for constructing multimodal transport networks and routing using a graph database: A case study in London

Park

¹

,

Cheng

²

2023

Transactions in GIS

7

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Most prior multimodal transport networks have been organized as relational databases with multilayer structures to support transport management and routing; however, database expandability and update efficiency in new networks and timetables are low due to the strict database schemas. This study aimed to develop multimodal transport networks using a graph database that can accommodate efficient updates and extensions, high relation‐based query performance, and flexible integration in multimodal routing. As a case study, a database was constructed for London transport networks, and routing tests were performed under various conditions. The constructed multimodal graph database showed stable performance in processing iterative queries, and efficient multi‐stop routing was particularly enhanced. By applying the proposed framework, databases for multimodal routing can be readily constructed for other regions, while enabling responses to diversified routings, such as personalized routing through integration with various unstructured information, due to the flexible schema of the graph database.

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Dynamic System Optimal Routing in Multimodal Transit Network

Cited by 13 publications

References 15 publications

Transportation Transformed: A Comprehensive Review of Dynamic Rerouting in Multimodal Networks

Transportation Transformed: A Comprehensive Review of Dynamic Rerouting in Multimodal Networks

Transportation Transformed: A Comprehensive Review of Dynamic Rerouting in Multimodal Networks

Framework for constructing multimodal transport networks and routing using a graph database: A case study in London

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