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Preprints

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Potential Short- to Long-Term Impacts of On-Demand Urban Air Mobility on Transportation Demand in North America
Kexin (Sally) Chen, Ali Shamshiripour, Ravi Seshadri, Md Sami Hasnine, Lisa Yoo, Jinping Guan, Andre Romano Alho, Daniel Feldman, and Moshe Ben-Akiva
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Abstract:
This study applies an agent-based approach to investigate the potential individual-level demand for and system-wide impacts of Urban Air Mobility (UAM) in the short- to long-term in two U.S. metropolitan areas. The UAM service we envision in this research provides mobility to on-demand requests from one vertiport to another. The investigations consider the existing electric vertical take-off and landing (eVTOL) aircraft models (assuming they are piloted) and vertiport designs while accounting for the uncertainties in (i) service attributes (e.g., time-saving and service price) and (ii) demand characteristics7(e.g., perceived waiting time in various conditions). Towards this goal, the state-of-the-art agent-based simulation platform SimMobility is expanded in this research with new modules required for realistic simulation of the demand, supply, and demand-supply interactions. The expanded platform adopts a high-fidelity model system with (i) a behaviorally sound demand model to mimic the switching behavior from current non-UAM mode to UAM and to capture the individuals’ willingness to pay and plan-action dynamics in decision-making; (ii) a detailed operation model to account for not only the integration of ground and aerial transportation but also fleet rebalancing and the intra-vertiport state dynamics such as charging, gate availability, taxing, pre-landing hovering (as a result of capacity limitations), etc.; (iii) a demand-driven vertiport placement and capacity generation module. The results show that the ...

Selected Publications

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Abstract


As a special case of multitasking, travel-based multitasking typically refers to conducting a set of in-vehicle activities while traveling and has an indisputable influence on offering a pleasant travel experience to transit users during their rides. The in-vehicle activities could help the rider free up time from his/her schedule for the day (i.e., a worthwhile use of travel time). In this study, we investigate how the worthwhileness of travel-based multitasking could be under the influence of (1) the transit user’s lifestyle, (2) socio-demographics, and (3) the characteristics of the transit trip. Toward this, we conducted an intercept survey focusing on the transit trips in the Chicago metropolitan area and analyzed it using a latent class modeling approach. Two classes of transit users could be identified: (1) worthwhileness seekers, productive travelers, and (2) leisure seekers, occasional worthwhile travelers. The results also suggest travel time, waiting time and walking distance to the transit station, and the set of in-vehicle activities as significant predictors of worthwhile use of travel time. The findings provide insights to policymakers for improving public transit systems in their current form, as well as designing an autonomous mobility system as the future form of public transit.

List of Journal Publications

  1. Chauhan, R.S., Bhagat-Conway, M.W., Capasso da Silva, D., Salon, D., Shamshiripour, A., Rahimi, E., Khoeini, S., Mohammadian, A.K., Derrible, S. and Pendyala, R., 2021. “A database of travel-related behaviors and attitudes before, during, and after COVID-19 in the United States.” Scientific Data, 8(1), pp.1-7. https://doi.org/10.6084/m9.figshare.15141945

  2. Rahimi, E., Shabanpour, R., Shamshiripour, A. and Mohammadian, A.K., 2021. “Perceived risk of using shared mobility services during the COVID-19 pandemic.” Transportation Research Part F: Traffic Psychology and Behaviour, 81, pp.271-281. https://doi.org/10.1016/j.trf.2021.06.012

  3. Salon, D., Conway, M.W., da Silva, D.C., Chauhan, R.S., Derrible, S., Mohammadian, A.K., Khoeini, S., Parker, N., Mirtich, L., Shamshiripour, A. and Rahimi, E., 2021. “The potential stickiness of pandemic-induced behavior changes in the United States.” Proceedings of the National Academy of Sciences, 118(27). https://doi.org/10.1073/pnas.2106499118.

  4. Chauhan, R.S., da Silva, D.C., Salon, D., Shamshiripour, A., Rahimi, E., Sutradhar, U., Khoeini, S., Mohammadian, A.K., Derrible, S. and Pendyala, R., 2021. “COVID-19 related attitudes and risk perceptions across urban, rural, and suburban areas in the United States.” Findings, p.23714. https://doi.org/10.32866/001c.23714.

  5. da Silva, D.C., Khoeini, S., Salon, D., Conway, M.W., Chauhan, R.S., Pendyala, R.M., Shamshiripour, A., Rahimi, E., Magassy, T., Mohammadian, A.K. and Derrible, S., 2021. “How are Attitudes Toward COVID-19 Associated with Traveler Behavior During the Pandemic?.” Findings, p.24389. https://doi.org/10.32866/001c.24389

  6. Shamshiripour, A., Rahimi, E., Mohammadian, A.K. and Auld, J., 2020. “Investigating the influence of latent lifestyles on productive travels: Insights into designing autonomous transit system.” Transportation Research Part A: Policy and Practice, 141, pp.469-484. https://doi.org/10.1016/j.tra.2020.10.001

  7. Shamshiripour, A., Rahimi, E., Shabanpour, R. and Mohammadian, A.K., 2020. “How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago.” Transportation Research Interdisciplinary Perspectives, 7, p.100216. https://doi.org/10.1016/j.trip.2020.100216

  8. Shamshiripour, A., Rahimi, E., Shabanpour, R. and Mohammadian, A.K., 2020. “Dynamics of travelers’ modality style in the presence of mobility-on-demand services.” Transportation Research Part C: Emerging Technologies, 117, p.102668. https://doi.org/10.1016/j.trc.2020.102668

  9. Rahimi, E., Shamshiripour, A., Shabanpour, R., Mohammadian, A. K. and Auld, J., 2020. “Analysis of transit users’ response behavior in case of unplanned service disruptions.” Transportation Research Record, 2674(3), pp.258-271. https://doi.org/10.1177/0361198120911921

  10. Rahimi, E., Shamshiripour, A., Samimi, A. and Mohammadian, A.K., 2020. “Investigating the injury severity of single-vehicle truck crashes in a developing country.” Accident Analysis & Prevention, 137, p.105444. https://doi.org/10.1016/j.aap.2020.105444

  11. Shamshiripour, A., Shabanpour, R., Golshani, N., Mohammadian, A.K. and Shamshiripour, P., 2020. “Analyzing the impact of neighborhood safety on active school travels.” International journal of sustainable transportation, 14(10), pp.788-805. https://doi.org/10.1080/15568318.2019.1628327

  12. Ermagun, A., Shamshiripour, A. and Stathopoulos, A., 2020. “Performance analysis of crowd-shipping in urban and suburban areas.” Transportation, 47(4), pp.1955-1985. https://doi.org/10.1007/s11116-019-10033-7

  13. Rahimi, E., Shamshiripour, A., Shabanpour, R., Mohammadian, A.K. and Auld, J., 2019. “Analysis of transit users’ waiting tolerance in response to unplanned service disruptions.” Transportation Research Part D: Transport and Environment, 77, pp.639-653. https://doi.org/10.1016/j.trd.2019.10.011

  14. Shamshiripour, A., Golshani, N., Shabanpour, R. and Mohammadian, A. K., 2019. “Week-long mode choice behavior: dynamic random effects logit model.” Transportation research record, 2673(10), pp.736-744. https://doi.org/10.1177/0361198119851746

  15. Shamshiripour, A. and Samimi, A., 2019. “Estimating a mixed-profile MDCEV: case of daily activity type and duration.” Transportation Letters, 11(6), pp.289-302. https://doi.org/10.1080/19427867.2017.1337266.

  16. Shabanpour, R., Shamshiripour, A. and Mohammadian, A. K., 2018. “Modeling adoption timing of autonomous vehicles: innovation diffusion approach.” Transportation, 45(6), pp.1607-1621. https://doi.org/10.1007/s11116-018-9947-7

  17. Shabanpour, R., Golshani, N., Shamshiripour, A. and Mohammadian, A.K., 2018. “Eliciting preferences for adoption of fully automated vehicles using best-worst analysis.” Transportation research part C: emerging technologies, 93, pp.463-478. https://doi.org/10.1016/j.trc.2018.06.014

List of Book Chapters

  1. Shamshiripour A., Shabanpour R., Golshani N., Auld J., Mohammadian A. K., 2020. “A Flexible Activity Scheduling Conflict Resolution Framework” in: Goulias K.G., Davis A.W (eds) Mapping the Travel Behavior Genome: The Role of Disruptive Technologies, Automation, and Experimentation. 299-322, 2020. Elsevier. https://doi.org/10.1016/B978-0-12-817340-4.00016-4

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