Algorithm to Compute Urban Road Network Resilience

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2047

Special Issue Editor

Center for Transportation Research (CTR), The University of Texas at Austin, Austin, TX 78759, USA
Interests: resilient infrastructure systems; sustainability; risk assessment; emerging techniques; roadway network analysis; optimal resource allocations; smart cities

Special Issue Information

Dear Colleagues,

Following the immense impact that the COVID-19 pandemic had on economies and societies, many countries are now including renewed infrastructure investment as a stimulus measure. Infrastructure resilience is the ability to reduce the magnitude and/or duration of disruptive events. The effectiveness of a resilient infrastructure or enterprise depends upon its ability to anticipate, absorb, adapt to, and/or rapidly recover from a potentially disruptive event. On the other hand, the development of the urban road network plays an important role in the economic development of a country. The appropriate development of the urban road network not only reduces the cost of transportation, but also aids in the integration of various regions within the country and the better understanding of neighboring countries at the international level. Developing a resilient urban roadway network is important to reduce direct losses and the indirect costs of disruption.

This Special Issue provides a cross-disciplinary forum for researchers to disseminate innovative research and engineering practices in algorithms to compute and quantify urban roadway network resilience, such as artificial intelligence, agent-based modeling, machine learning, etc. Papers may report on original research, discuss methodological aspects, review the current state of the art, or offer perspectives on future prospects.

Dr. Zhe Han
Guest Editor

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Keywords

  • urban road network
  • emerging techniques and algorithms
  • resilience
  • machine learning
  • agent-based modeling
  • roadway network reliability
  • risk assessment under extreme weather

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Published Papers (1 paper)

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Research

15 pages, 1280 KiB  
Article
Analyzing the Passenger Waiting Tolerance during Urban Rail Transit Service Interruption: Using Stated Preference Data in Chongqing, China
by Binbin Li, Zhefan Ye, Jue Li, Siyuan Shao and Chenlu Wang
Computation 2023, 11(2), 33; https://doi.org/10.3390/computation11020033 - 14 Feb 2023
Cited by 2 | Viewed by 1601
Abstract
To reduce traffic congestion and pollution, urban rail transit in China has been in a stage of rapid development in recent years. As a result, rail transit service interruption events are becoming more common, seriously affecting the resilience of the transportation system and [...] Read more.
To reduce traffic congestion and pollution, urban rail transit in China has been in a stage of rapid development in recent years. As a result, rail transit service interruption events are becoming more common, seriously affecting the resilience of the transportation system and user satisfaction. Therefore, determining the changing mechanism of the passenger waiting tolerance, which helps establish a scientific and effective emergency plan, is urgent. First, the variables and levels of the urban rail service interruption scenarios were screened and determined, and the stated preference questionnaire was designed using the orthogonal design method. Further, the data of the waiting tolerance of passengers during service interruptions were obtained through questionnaires. Second, combined with the questionnaire data, an accelerated failure time model that obeys the exponential distribution was constructed. The results indicate that factors such as the service interruption duration, travel distance, bus bridging, information accuracy, attention to operation information, travel frequency and interruption experience affect the waiting tolerance of passengers during service interruptions. Finally, combined with the sensitivity analysis of the key influencing factors, the policy analysis and suggestions are summarized to provide theoretical support for the urban rail operation and management department to capture the passenger waiting tolerance accurately during service interruptions and formulate an efficient, high-quality emergency organization plan. Full article
(This article belongs to the Special Issue Algorithm to Compute Urban Road Network Resilience)
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