IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v607y2022ics037843712200721x.html
   My bibliography  Save this article

Generalizing the probability of reaching a destination in case of route blockage

Author

Listed:
  • Yamada, Takashi

Abstract

When road blockages occur owing to disasters or congestion, traffic is unable to pass, paralyzing urban functions. Though previous studies have analyzed road blockages in specific cities, a more general approach valid for a wide range of road networks is required. In this study, different blockage rates were applied to three square lattice sizes as well as square, triangular, and hexagonal lattice configurations to simulate an assortment of road network geometries, and agent-based modeling was applied to quantitatively evaluate the resulting arrival probabilities and travel times. The results did not change significantly with the blockage rate, regardless of lattice size. For the square lattice, the route from the starting point to the destination point was secured when the blockage rate was ≤30%, but the probability of not arriving at the destination point increased considerably when the blockage rate was >30% and reached an infinitesimally small value when the blockage rate was >60%. When fewer routes connected the lattice intersections, the destination was more likely to be unreachable, even for a low blockage rate. These results were then validated by percolation theory, demonstrating an effective basis for travel feasibility evaluations when an urban road network is blocked.

Suggested Citation

  • Yamada, Takashi, 2022. "Generalizing the probability of reaching a destination in case of route blockage," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
  • Handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s037843712200721x
    DOI: 10.1016/j.physa.2022.128163
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843712200721X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2022.128163?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hara, Yusuke & Kuwahara, Masao, 2015. "Traffic Monitoring immediately after a major natural disaster as revealed by probe data – A case in Ishinomaki after the Great East Japan Earthquake," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 1-15.
    2. Hou, Guangyang & Chen, Suren & Bao, Yulong, 2022. "Development of travel time functions for disrupted urban arterials with microscopic traffic simulation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    3. Balijepalli, Chandra & Oppong, Olivia, 2014. "Measuring vulnerability of road network considering the extent of serviceability of critical road links in urban areas," Journal of Transport Geography, Elsevier, vol. 39(C), pages 145-155.
    4. Chang, Stephanie E. & Nojima, Nobuoto, 2001. "Measuring post-disaster transportation system performance: the 1995 Kobe earthquake in comparative perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(6), pages 475-494, July.
    5. Panjamani Anbazhagan & Sushma Srinivas & Deepu Chandran, 2012. "Classification of road damage due to earthquakes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 60(2), pages 425-460, January.
    6. Kerner, Boris S., 2013. "Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory: A brief review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5261-5282.
    7. Muriel-Villegas, Juan E. & Alvarez-Uribe, Karla C. & Patiño-Rodríguez, Carmen E. & Villegas, Juan G., 2016. "Analysis of transportation networks subject to natural hazards – Insights from a Colombian case," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 151-165.
    8. Chen, Bi Yu & Lam, William H.K. & Sumalee, Agachai & Li, Qingquan & Li, Zhi-Chun, 2012. "Vulnerability analysis for large-scale and congested road networks with demand uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 501-516.
    9. Jenelius, Erik & Petersen, Tom & Mattsson, Lars-Göran, 2006. "Importance and exposure in road network vulnerability analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(7), pages 537-560, August.
    10. Ruan, Zhongyuan & Song, Congcong & Yang, Xu-hua & Shen, Guojiang & Liu, Zhi, 2019. "Empirical analysis of urban road traffic network: A case study in Hangzhou city, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    11. Laurie Schintler & Rajendra Kulkarni & Sean Gorman & Roger Stough, 2007. "Using Raster-Based GIS and Graph Theory to Analyze Complex Networks," Networks and Spatial Economics, Springer, vol. 7(4), pages 301-313, December.
    12. Zhou, Yaoming & Wang, Junwei & Sheu, Jiuh-Biing, 2019. "On connectivity of post-earthquake road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 1-16.
    13. Wu, Chao-Yun & Hu, Mao-Bin & Jiang, Rui & Hao, Qing-Yi, 2021. "Effects of road network structure on the performance of urban traffic systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    14. Antonio Santo & Nicoletta Santangelo & Giovanni Forte & Melania De Falco, 2017. "Post flash flood survey: the 14th and 15th October 2015 event in the Paupisi-Solopaca area (Southern Italy)," Journal of Maps, Taylor & Francis Journals, vol. 13(2), pages 19-25, November.
    15. Dong, Shangjia & Wang, Haizhong & Mostafizi, Alireza & Song, Xuan, 2020. "A network-of-networks percolation analysis of cascading failures in spatially co-located road-sewer infrastructure networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    16. Bono, Flavio & Gutiérrez, Eugenio, 2011. "A network-based analysis of the impact of structural damage on urban accessibility following a disaster: the case of the seismically damaged Port Au Prince and Carrefour urban road networks," Journal of Transport Geography, Elsevier, vol. 19(6), pages 1443-1455.
    17. Adam Pel & Michiel Bliemer & Serge Hoogendoorn, 2012. "A review on travel behaviour modelling in dynamic traffic simulation models for evacuations," Transportation, Springer, vol. 39(1), pages 97-123, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khademi, Navid & Babaei, Mohsen & Schmöcker, Jan-Dirk & Fani, Amirhossein, 2018. "Analysis of incident costs in a vulnerable sparse rail network – Description and Iran case study," Research in Transportation Economics, Elsevier, vol. 70(C), pages 9-27.
    2. Ahmad Mohamad El-Maissi & Sotirios A. Argyroudis & Fadzli Mohamed Nazri, 2020. "Seismic Vulnerability Assessment Methodologies for Roadway Assets and Networks: A State-of-the-Art Review," Sustainability, MDPI, vol. 13(1), pages 1-31, December.
    3. Balijepalli, Chandra & Oppong, Olivia, 2014. "Measuring vulnerability of road network considering the extent of serviceability of critical road links in urban areas," Journal of Transport Geography, Elsevier, vol. 39(C), pages 145-155.
    4. Victor Cantillo & Luis F. Macea & Miguel Jaller, 2019. "Assessing Vulnerability of Transportation Networks for Disaster Response Operations," Networks and Spatial Economics, Springer, vol. 19(1), pages 243-273, March.
    5. Bíl, Michal & Vodák, Rostislav & Kubeček, Jan & Bílová, Martina & Sedoník, Jiří, 2015. "Evaluating road network damage caused by natural disasters in the Czech Republic between 1997 and 2010," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 90-103.
    6. Rodríguez-Núñez, Eduardo & García-Palomares, Juan Carlos, 2014. "Measuring the vulnerability of public transport networks," Journal of Transport Geography, Elsevier, vol. 35(C), pages 50-63.
    7. Amir Al Hamdi Redzuan & Rozana Zakaria & Aznah Nor Anuar & Eeydzah Aminudin & Norbazlan Mohd Yusof, 2022. "Road Network Vulnerability Based on Diversion Routes to Reconnect Disrupted Road Segments," Sustainability, MDPI, vol. 14(4), pages 1-22, February.
    8. Gu, Yu & Chen, Anthony & Xu, Xiangdong, 2023. "Measurement and ranking of important link combinations in the analysis of transportation network vulnerability envelope buffers under multiple-link disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 118-144.
    9. Jafino, Bramka Arga, 2021. "An equity-based transport network criticality analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 204-221.
    10. Aghababaei, Mohammad T. (Siavash) & Costello, Seosamh B. & Ranjitkar, Prakash, 2021. "Measures to evaluate post-disaster trip resilience on road networks," Journal of Transport Geography, Elsevier, vol. 95(C).
    11. Li, Tao & Rong, Lili & Yan, Kesheng, 2019. "Vulnerability analysis and critical area identification of public transport system: A case of high-speed rail and air transport coupling system in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 127(C), pages 55-70.
    12. Caterina Malandri & Luca Mantecchini & Filippo Paganelli & Maria Nadia Postorino, 2021. "Public Transport Network Vulnerability and Delay Distribution among Travelers," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    13. Dong, Shangjia & Gao, Xinyu & Mostafavi, Ali & Gao, Jianxi & Gangwal, Utkarsh, 2023. "Characterizing resilience of flood-disrupted dynamic transportation network through the lens of link reliability and stability," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    14. Zhang, Nan & Huang, Hong & Su, Boni & Zhao, Jinlong, 2015. "Analysis of dynamic road risk for pedestrian evacuation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 171-183.
    15. Bucar, Raif C.B. & Hayeri, Yeganeh M., 2020. "Quantitative assessment of the impacts of disruptive precipitation on surface transportation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    16. Bell, Michael G.H. & Kurauchi, Fumitaka & Perera, Supun & Wong, Walter, 2017. "Investigating transport network vulnerability by capacity weighted spectral analysis," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 251-266.
    17. Ling Zhang & Jingjing Hao & Xiaofeng Ji & Lan Liu, 2019. "Research on the Complex Characteristics of Freight Transportation from a Multiscale Perspective Using Freight Vehicle Trip Data," Sustainability, MDPI, vol. 11(7), pages 1-20, March.
    18. Yin, Kai & Wu, Jianjun & Wang, Weiping & Lee, Der-Horng & Wei, Yun, 2023. "An integrated resilience assessment model of urban transportation network: A case study of 40 cities in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    19. Katerina Tzavella & Alexander Fekete & Frank Fiedrich, 2018. "Opportunities provided by geographic information systems and volunteered geographic information for a timely emergency response during flood events in Cologne, Germany," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(1), pages 29-57, April.
    20. Nima Haghighi & S. Kiavash Fayyaz & Xiaoyue Cathy Liu & Tony H. Grubesic & Ran Wei, 2018. "A Multi-Scenario Probabilistic Simulation Approach for Critical Transportation Network Risk Assessment," Networks and Spatial Economics, Springer, vol. 18(1), pages 181-203, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s037843712200721x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.