ABSTRACT
The COVID-19 pandemic has severely affected many aspects of people's daily lives. While many countries are in a reopening stage, some effects of the pandemic on people's behaviors are expected to last much longer, including how they choose between different transport options. Experts predict considerably delayed recovery of the public transport options, as people try to avoid crowded places. In turn, significant increases in traffic congestion are expected, since people are likely to prefer using their own vehicles or taxis as opposed to riskier and more crowded options such as the railway. In this paper, we propose to use financial incentives to set the tradeoff between risk of infection and congestion to achieve safe and efficient transportation networks. To this end, we formulate a network optimization problem to optimize taxi fares. For our framework to be useful in various cities and times of the day without much designer effort, we also propose a data-driven approach to learn human preferences about transport options, which is then used in our taxi fare optimization. Our user studies and simulation experiments show our framework is able to minimize congestion and risk of infection.
- Martin J Beckmann, Charles B McGuire, and Christopher B Winsten. 1955. Studies in the Economics of Transportation. (1955).Google Scholar
- Moshe Ben-Akiva and Steven R Lerman. 2018. Discrete choice analysis: theory and application to travel demand. Transportation Studies.Google Scholar
- Erdem Biyik, Nicolas Huynh, Mykel J. Kochenderfer, and Dorsa Sadigh. 2020. Active Preference-Based Gaussian Process Regression for Reward Learning. In Proceedings of Robotics: Science and Systems (RSS).Google ScholarCross Ref
- Erdem Bıyık, Daniel A Lazar, Ramtin Pedarsani, and Dorsa Sadigh. 2018. Altruistic Autonomy: Beating Congestion on Shared Roads. In Workshop on Algorithmic Foundations of Robotics (WAFR).Google Scholar
- Erdem Bıyık, Daniel A. Lazar, Dorsa Sadigh, and Ramtin Pedarsani. 2019. The Green Choice: Learning and Influencing Human Decisions on Shared Roads. In Proceedings of the 58th IEEE Conference on Decision and Control (CDC).Google ScholarDigital Library
- Erdem Bıyık, Malayandi Palan, Nicholas C. Landolfi, Dylan P. Losey, and Dorsa Sadigh. 2020. Asking Easy Questions: A User-Friendly Approach to Active Reward Learning (Proceedings of Machine Learning Research, Vol. 100), Leslie Pack Kaelbling, Danica Kragic, and Komei Sugiura (Eds.).PMLR, 1177--1190. http://proceedings.mlr.press/v100/biy-ik20a.htmlGoogle Scholar
- Philip N Brown and Jason R Marden. 2016. The robustness of marginal-cost taxes in affine congestion games. IEEE Trans. Automat. Control 62, 8 (2016), 3999--4004.Google ScholarCross Ref
- Susan Carpenter. 2020. LA Traffic Could Be Worse Post Pandemic. Spectrum News 1 (Jul 2020). https://spectrumnews1.com/ca/la-west/transportation/2020/07/30/la-traffic-could-be-worse-post-pandemicGoogle Scholar
- José R Correa, Andreas S Schulz, and Nicolás E Stier-Moses. 2008. A geometric approach to the price of anarchy in nonatomic congestion games. Games and Economic Behavior 64, 2 (2008), 457--469.Google ScholarCross Ref
- Thomas M Cover. 1999. Elements of information theory. John Wiley & Sons.Google Scholar
- Zhiyong Cui, Meixin Zhu, Shuo Wang, Pengfei Wang, Yang Zhou, Qianxia Cao, Cole Kopca, and Yinhai Wang. 2020. Traffic Performance Score for Measuring the Impact of COVID-19 on Urban Mobility. arXiv preprint arXiv:2007.00648 (2020).Google Scholar
- Mark S Daskin. 1985. Urban transportation networks: Equilibrium analysis with mathematical programming methods.Google Scholar
- Richard G Dowling, Rupinder Singh, and Willis Wei-Kuo Cheng. 1998. Accuracy and performance of improved speed-flow curves. Transportation research record 1646, 1 (1998), 9--17.Google Scholar
- Beth Ewoldsen. 2020. COVID-19 Trends Impacting the Future of Transportation Planning and Research. National Academies of Sciences, Engineering, and Medicine (Aug 2020). https://www.nationalacademies.org/trb/blog/covid-19-trends-impacting-the-future-of-transportation-planning-and-researchGoogle Scholar
- Lisa Fleischer, Kamal Jain, and Mohammad Mahdian. 2004. Tolls for heterogeneous selfish users in multicommodity networks and generalized congestion games. In 45th Annual IEEE Symposium on Foundations of Computer Science. IEEE, 277--285.Google ScholarDigital Library
- Michael Florian and Sang Nguyen. 1976. Recent experience with equilibrium methods for the study of a congested urban area. In Traffic Equilibrium Methods. Springer, 382--395.Google Scholar
- Daniel Golovin and Andreas Krause. 2011. Adaptive submodularity: Theory and applications in active learning and stochastic optimization. Journal of Artificial Intelligence Research 42 (2011), 427--486.Google ScholarDigital Library
- Martin Hattrup-Silberberg, Saskia Hausler, Kersten Heineke, Nicholas Laverty, Timo Möller, Dennis Schwedhelm, and Ting Wu. 2020. Five COVID-19 aftershocks reshaping mobility's future. McKinsey & Company (Sep 2020). https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/five-covid-19-attershocks-reshaping-mobilitys-futureGoogle Scholar
- Patrick Hertzke, Simon Middleton, Guillaume Neu, and Henry Weaver. 2020. Moving forward: How COVID-19 will affect mobility in the United Kingdom. McKinsey & Company (June 2020). https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/moving-forward-how-covid-19-will-affect-mobility-in-the-united-kingdomGoogle Scholar
- Winnie Hu and Nate Schweber. 2020. Will Cars Rule the Roads in Post-Pandemic New York? The New York Times (Aug 2020). https://www.nytimes.com/2020/08/10/nyregion/nyc-streets-parking-dining-busways.htmlGoogle Scholar
- Yue Hu, William Barbour, Samitha Samaranayake, and Dan Work. 2020. Impacts of Covid-19 mode shift on road traffic. arXiv preprint arXiv:2005.01610 (2020).Google Scholar
- Yue Hu, Will Barbour, Samitha Samaranayake, and Dan Work. 2020. The rebound --- How Covid-19 could lead to worse traffic. Medium (Apr 2020). https://medium.com/@barbourww/the-rebound-how-covid-19-could-lead-to-worse-traffic-cb245a5b1da2Google Scholar
- Larry Kahaner. 2020. Working from home a major factor in post-pandemic traffic. FleetOwner (Aug 2020). https://www.fleetowner.com/covid-19-coverage/article/21139861/working-from-home-a-major-factor-in-postpandemic-trafficGoogle Scholar
- Nathan Kallus and Madeleine Udell. 2016. Revealed preference at scale: Learning personalized preferences from assortment choices. In Proceedings of the 2016 ACM Conference on Economics and Computation. 821--837.Google ScholarDigital Library
- Yuhao Kang, Song Gao, Yunlei Liang, Mingxiao Li, Jinmeng Rao, and Jake Kruse. 2020. Multiscale dynamic human mobility flow dataset in the us during the covid-19 epidemic. arXiv preprint arXiv.2008.12238 (2020).Google Scholar
- Frank S Koppelman. 1983. Predicting transit ridership in response to transit service changes. Journal of Transportation Engineering 109, 4 (1983), 548--564.Google ScholarCross Ref
- Dieter Kraft. 1988. A Software Package for Sequential Quadratic Programming. Technical Report. Institut für Dynamik der Flugsysteme Oberpfaffenhofen.Google Scholar
- Walid Krichene, Jack D Reilly, Saurabh Amin, and Alexandre M Bayen. 2018. Stackelberg routing on parallel transportation networks. Handbook of Dynamic Game Theory (2018).Google Scholar
- Rafał Kucharski and Arkadiusz Drabicki. 2017. Estimating macroscopic volume delay functions with the traffic density derived from measured speeds and flows. Journal of Advanced Transportation 2017 (2017).Google Scholar
- Daniel Lazar, Samuel Coogan, and Ramtin Pedarsani. 2020. Routing for traffic networks with mixed autonomy. IEEE Trans. Automat. Control (2020).Google ScholarCross Ref
- Daniel A Lazar, Erdem Bıyık, Dorsa Sadigh, and Ramtin Pedarsani. 2019. Learning How to Dynamically Route Autonomous Vehicles on Shared Roads. arXiv preprint arXiv:1909.03664 (2019).Google Scholar
- Daniel A Lazar, Samuel Coogan, and Ramtin Pedarsani. 2019. Optimal tolling for heterogeneous traffic networks with mixed autonomy. In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 4103--4108.Google ScholarDigital Library
- Negar Mehr and Roberto Horowitz. 2019. How will the presence of autonomous vehicles affect the equilibrium state of traffic networks? IEEE Transactions on Control of Network Systems 7, 1 (2019), 96--105.Google ScholarCross Ref
- United States. Bureau of Public Roads. 1964. Traffic assignment manual. US Department of Commerce, Bureau of Public Roads, Office of Planning, Urban Planning Division.Google Scholar
- Jordan Pascale. 2020. Here Are Four Charts That Show The Pandemic's Impact On Locals' Travel Habits. Wamu (Jul 2020). https://wamu.org/story/20/07/16/here-are-four-charts-that-show-the-pandemics-impact-on-locals-travel-habits/Google Scholar
- Neil Pedersen. 2020. Impacts of COVID-19 on Transportation and Key Considerations for the Future. http://onlinepubs.trb.org/onlinepubs/PedersenlTEAnnual%20Meeting200813.pptx Presentation at ITE 2020 Annual Meeting (Online).Google Scholar
- Republic of Turkey Governorship of Istanbul. 2020. Governor Yerlikaya Made a Press Release Regarding Gradual Working Hours Practice in Istanbul. (Sep 2020). http://en.istanbul.gov.tr/governor-yerlikaya-made-a-press-release-regarding-gradual-working-hours-practice-in-istanbulGoogle Scholar
- Tim Roughgarden and Éva Tardos. 2002. How bad is selfish routing? Journal of the ACM (JACM) 49, 2 (2002), 236--259.Google ScholarDigital Library
- Dorsa Sadigh, Anca D. Dragan, S. Shankar Sastry, and Sanjit A. Seshia. 2017. Active Preference-Based Learning of Reward Functions. In Proceedings of Robotics: Science and Systems (RSS).Google Scholar
- William H Sandholm. 2002. Evolutionary implementation and congestion pricing. The Review of Economic Studies 69, 3 (2002), 667--689.Google ScholarCross Ref
- Chaitanya Swamy. 2012. The effectiveness of Stackelberg strategies and tolls for network congestion games. ACM Transactions on Algorithms (TALG) 8, 4 (2012), 1--19.Google ScholarDigital Library
- Chris Teale. 2020. Transportation leaders focus on regaining trust before building anew. Smart Cities Dive (May 2020). https://www.smartcitiesdive.com/news/transportation-leaders-focus-on-regaining-trust-before-building-anew/577394Google Scholar
- Alejandro Tirachini and Oded Cats. 2020. COVID-19 and public transportation: Current assessment, prospects, and research needs. Journal of Public Transportation 22, 1 (2020), 1.Google ScholarCross Ref
- Joe Vitale, Karen Bowman, and Ryan Robinson. 2020. How the pandemic is changing the future of automotive. Deloitte (Jul 2020). https://deloitte.com/us/en/insights/industry/retail-distribution/consumer-behavior-trends-state-of-the-consumer-tracker/future-of-automotive-industry-pandemic.htmlGoogle Scholar
- Songhe Wang, Kangda Wei, Lei Lin, and Weizi Li. 2020. Spatial-temporal Analysis of COVID-19's Impact on Human Mobility: the Case of the United States. arXiv preprint arXiv:2010.03707 (2020).Google Scholar
- Lars F Westblade, Gagandeep Brar, Laura C Pinheiro, Demetrios Paidoussis, Mangala Rajan, Peter Martin, Parag Goyal, Jorge L Sepulveda, Lisa Zhang, Gary George, et al. 2020. SARS-CoV-2 viral load predicts mortality in patients with and without cancer who are hospitalized with COVID-19. Cancer cell (2020).Google Scholar
- Nils Wilde, Dana Kulic, and Stephen L Smith. 2020. Active Preference Learning using Maximum Regret. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Google ScholarDigital Library
- Wai Wong and SC Wong. 2016. Network topological effects on the macroscopic Bureau of Public Roads function. Transportmetrica A: Transport Science 12, 3 (2016), 272--296.Google ScholarCross Ref
- Morteza Zadimoghaddam and Aaron Roth. 2012. Efficiently learning from revealed preference. In International Workshop on Internet and Network Economics. Springer, 114--127.Google ScholarDigital Library
- Hongyu Zheng, Kenan Zhang, and Marco Nie. 2020. The Fall and Rise of the Taxi Industry in the COVID-19 Pandemic: A Case Study. Available at SSRN 3674241 (2020).Google Scholar
Index Terms
- Incentivizing routing choices for safe and efficient transportation in the face of the COVID-19 pandemic
Recommendations
Enhancing mass transit passenger safety during a pandemic via in-vehicle time minimization
AbstractThe risk of infection in a pandemic increases with duration of close contact with an infected person. Since the application of social distancing in public transit vehicles is challenging, minimization of in-vehicle time can help to ...
Highlights- Sustainable operation for public transit to enhance passenger safety in a pandemic.
Telehealth utilization during the Covid-19 pandemic: A systematic review
AbstractDuring the coronavirus disease (COVID-19) pandemic, different technologies, including telehealth, are maximised to mitigate the risks and consequences of the disease. Telehealth has been widely utilised because of its usability and safety in ...
Highlights- State-of-the-art Literature Categorization for Telehealth utilization during COVID-19.
- Challenges, motivations and recommended solutions are identified for Telehealth during COVID-19.
- Different Applications of Telehealth during the ...
Covid-19 in Transportation: A Comprehensive Bibliometric Analysis and Systematic Review with a Reappraisal
HCI International 2023 – Late Breaking PapersAbstractThis paper explores the impact of COVID-19 on public transportation systems through a bibliometric analysis, systematic review, and reappraisal. A systematic review analyzes literature, identifies trends, and provides comprehensive insights. ...
Comments