IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v98y2020icp91-104.html
   My bibliography  Save this article

On the possibility of short-term traffic prediction during disaster with machine learning approaches: An exploratory analysis

Author

Listed:
  • Chikaraishi, Makoto
  • Garg, Prateek
  • Varghese, Varun
  • Yoshizoe, Kazuki
  • Urata, Junji
  • Shiomi, Yasuhiro
  • Watanabe, Ryuki

Abstract

Since the cost and time required to finetune parameters in traditional short-term traffic prediction models such as traffic simulators are very high, the prediction models have been developed mainly for managing recurrent congestion, rather than non-recurrent congestion caused, for example, by disaster. Machine learning models are promising candidates for traffic prediction during non-recurrent congestion due to their ability to tune parameters without a-priori knowledge, while their applicability to non-recurrent conditions has rarely been explored. To fill in this gap, this study conducts an exploratory analysis on the applicability of various machine learning models during a transportation network disruption with particular focuses on their ability to predict traffic states and the interpretability of the results. The analysis is conducted by using data obtained during the massive transport network disruption which occurred in Hiroshima in July 2018 due to heavy rain and subsequent landslides. The models tested include random forest, support vector machine, XGBoost, shallow feed-forward neural network, and deep feed-forward neural network. The results indicate that random forest and XGBoost methods produced the best results in terms of prediction accuracy. On the other hand, deep neural network models produce better results in terms of the interpretability of the results, i.e., the results can be logically explained from the perspective of existing traffic flow theory. These findings indicate that the model which produces the best prediction accuracy is not always the best for practical use since it does not mimic the mechanisms of congestion occurrence.

Suggested Citation

  • Chikaraishi, Makoto & Garg, Prateek & Varghese, Varun & Yoshizoe, Kazuki & Urata, Junji & Shiomi, Yasuhiro & Watanabe, Ryuki, 2020. "On the possibility of short-term traffic prediction during disaster with machine learning approaches: An exploratory analysis," Transport Policy, Elsevier, vol. 98(C), pages 91-104.
  • Handle: RePEc:eee:trapol:v:98:y:2020:i:c:p:91-104
    DOI: 10.1016/j.tranpol.2020.05.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X20304194
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tranpol.2020.05.023?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. Xiaolei Ma & Haiyang Yu & Yunpeng Wang & Yinhai Wang, 2015. "Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-17, March.
    2. Van Arem, Bart & Kirby, Howard R. & Van Der Vlist, Martie J. M. & Whittaker, Joe C., 1997. "Recent advances and applications in the field of short-term traffic forecasting," International Journal of Forecasting, Elsevier, vol. 13(1), pages 1-12, March.
    3. Zhu, Shanjiang & Levinson, David & Liu, Henry X. & Harder, Kathleen, 2010. "The traffic and behavioral effects of the I-35W Mississippi River bridge collapse," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(10), pages 771-784, December.
    4. Lo, Shih-Che & Hall, Randolph W., 2006. "Effects of the Los Angeles transit strike on highway congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(10), pages 903-917, December.
    5. Alireza Ermagun & David Levinson, 2018. "Spatiotemporal traffic forecasting: review and proposed directions," Transport Reviews, Taylor & Francis Journals, vol. 38(6), pages 786-814, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Yuwei & Li, Zhuoxuan & Chen, Jun & Liu, Zhiyuan & Cao, Jinde, 2024. "TRELM-DROP: An impavement non-iterative algorithm for traffic flow forecast," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    2. Johan Rose Santos & Nur Diana Safitri & Maya Safira & Varun Varghese & Makoto Chikaraishi, 2021. "Road network vulnerability and city-level characteristics: A nationwide comparative analysis of Japanese cities," Environment and Planning B, , vol. 48(5), pages 1091-1107, June.
    3. Ekinci, Esra & Mangla, Sachin Kumar & Kazancoglu, Yigit & Sarma, P.R.S. & Sezer, Muruvvet Deniz & Ozbiltekin-Pala, Melisa, 2022. "Resilience and complexity measurement for energy efficient global supply chains in disruptive events," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    4. E. Mary Jasmine & A. Milton, 2022. "The role of hyperparameters in predicting rainfall using n-hidden-layered networks," 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. 111(1), pages 489-505, March.
    5. Xiaoqing Dai & Han Qiu & Lijun Sun, 2021. "A Data-Efficient Approach for Evacuation Demand Generation and Dissipation Prediction in Urban Rail Transit System," Sustainability, MDPI, vol. 13(17), pages 1-15, August.

    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. Nazmul Arefin Khan & Muhammad Ahsanul Habib, 2018. "Evaluation of Preferences for Alternative Transportation Services and Loyalty towards Active Transportation during a Major Transportation Infrastructure Disruption," Sustainability, MDPI, vol. 10(6), pages 1-14, June.
    2. Mohandu Anjaneyulu & Mohan Kubendiran, 2022. "Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    3. Shuanfeng Zhao & Chao Wang & Pei Wei & Qingqing Zhao, 2020. "Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    4. Nguyen-Phuoc, Duy Q. & Currie, Graham & De Gruyter, Chris & Young, William, 2018. "Transit user reactions to major service withdrawal – A behavioural study," Transport Policy, Elsevier, vol. 64(C), pages 29-37.
    5. Stefan Bauernschuster & Timo Hener & Helmut Rainer, 2017. "When Labor Disputes Bring Cities to a Standstill: The Impact of Public Transit Strikes on Traffic, Accidents, Air Pollution, and Health," American Economic Journal: Economic Policy, American Economic Association, vol. 9(1), pages 1-37, February.
    6. Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
    7. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    8. Jenelius, Erik & Mattsson, Lars-Göran, 2012. "Road network vulnerability analysis of area-covering disruptions: A grid-based approach with case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(5), pages 746-760.
    9. Erik Jenelius & Lars-Göran Mattsson, 2011. "The impact of network density, travel and location patterns on regional road network vulnerability," ERSA conference papers ersa10p448, European Regional Science Association.
    10. Kilgarriff, Paul & McDermott, T.K.J. & Vega, Amaya & Morrissey , Karyn & O’Donoghue, Cathal, 2018. "Flooding disruption and the impact on the spatial distribution of commuter’s income," Working Papers 309608, National University of Ireland, Galway, Socio-Economic Marine Research Unit.
    11. Younes, Hannah & Nasri, Arefeh & Baiocchi, Giovanni & Zhang, Lei, 2019. "How transit service closures influence bikesharing demand; lessons learned from SafeTrack project in Washington, D.C. metropolitan area," Journal of Transport Geography, Elsevier, vol. 76(C), pages 83-92.
    12. Gutierrez-Lythgoe, Antonio, 2023. "Movilidad urbana sostenible: Predicción de demanda con Inteligencia Artificial [Sustainable Urban Mobility: Demand Prediction with Artificial Intelligence]," MPRA Paper 117103, University Library of Munich, Germany.
    13. Muhammad Aqib & Rashid Mehmood & Ahmed Alzahrani & Iyad Katib & Aiiad Albeshri & Saleh M. Altowaijri, 2019. "Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs," Sustainability, MDPI, vol. 11(10), pages 1-33, May.
    14. Bagloee, Saeed Asadi & Sarvi, Majid & Wolshon, Brian & Dixit, Vinayak, 2017. "Identifying critical disruption scenarios and a global robustness index tailored to real life road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 98(C), pages 60-81.
    15. Shanjiang Zhu & David Levinson, 2011. "A Portfolio Theory of Route Choice," Working Papers 000096, University of Minnesota: Nexus Research Group.
    16. Jin, Feng & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2020. "Forecasting air passenger demand with a new hybrid ensemble approach," Journal of Air Transport Management, Elsevier, vol. 83(C).
    17. Xu, Zhaoyi & Saleh, Joseph Homer, 2021. "Machine learning for reliability engineering and safety applications: Review of current status and future opportunities," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    18. Krzysztof Cebrat & Maciej Sobczyński, 2016. "Scaling Laws in City Growth: Setting Limitations with Self-Organizing Maps," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-11, December.
    19. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    20. Spyropoulou, Ioanna, 2020. "Impact of public transport strikes on the road network: The case of Athens," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 651-665.

    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:trapol:v:98:y:2020:i:c:p:91-104. 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.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    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.