New Mobility Solutions

About

The landscape of urban transportation has been reshaped by the evolution of Information and Communication Technologies (ICT) and the emergence of the Internet of Things (IoT). This transformative wave has given rise to a spectrum of New Mobility Options (NMOs), ranging from Connected and Automated Vehicles (CAVs) to transportation network companies (TNCs), shared mobility, micro-mobility, and micro-transit. Within this evolving context, the concept of Mobility-as-a-Service (MaaS) has ascended as a pivotal component in urban transportation, endowing travelers with versatile and adaptable mobility choices. This paradigm shift is particularly discernible in bustling metropolises like New York, Tokyo, and London, where these NMOs have become integral to the urban fabric. The benefits of NMOs attracted more users and impacted travelers’ travel behaviors and associated travel demand. As these NMOs proliferate and become one of the essential components of urban transportation systems, researchers and decision-makers need to comprehensively understand the impacts of those NMOs on urban transportation systems and harness positive and mitigate negative impacts.

My research proposed new mobility solutions through the following research topics: Multi-modal (MaaS), Public Transit, Shared Mobility, and Connected and Automated Vehicles.

Multimodal Mobility

Mobility-as-a-Service (MaaS) is a novel and promising mobility paradigm which integrates multiple transportation modes (transit, shared mobility, micro-mobility, etc.) to provide sustainable, affordable, and equitable mobility service to the public. My research contributes to the development of MaaS in smart cities by leveraging big data from various sources, and machine-learning informed physics modeling (human decision and mobility simulation) approach.

  • He, Y. B., Zhou, J., Ma, Z., Wang, D., Sha, D., Lee, M., ... & Ozbay, K. (2021). A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City. Transport Policy, 101, 145-161. doi: 10.1016/j.tranpol.2020.12.011.

    He, Y. B., Cai, K., Li, Y., & Xiao, M. (2014). An improved cellular-automaton-based algorithm for real-time aircraft landing scheduling. 2014 Seventh International Symposium on Computational Intelligence and Design (ISCID). doi: 10.1109/iscid.2014.243.

Public Transit

The transit ridership went down remarkably during the COVID-19 pandemic in the US, resulting in severer traffic congestion and emissions before the pandemic. My research on public transit focuses on improving the operation efficiency and reliability of the aging transit system and developing emerging transit services, such as aerial rapid services, street car services, etc.

  • Wang, D., He, Y. B., Gao, J., Chow, J. Y., Ozbay, K., & Iyer, S. (2021). Impact of COVID-19 behavioral inertia on reopening strategies for New York City transit. International Journal of Transportation Science and Technology, 10(2), 197-211. doi: 10.1016/j.ijtst.2021.01.003.

    Chow, J.Y.J., Ozbay, K., He, Y. B., Zhou, J., Ma, Z., Lee, M., Wang, D., and Sha, D. (2020). Multi-agent simulation-based virtual test bed ecosystem: MATSim-NYC. C2SMART Project Report.

Shared Mobility

Shared mobility changes people’s travel behaviors and benefits society as a sustainable mobility option. Maximizing the social and environmental benefits, my research provides innovative solutions for the operation and management of shared mobility in the context of smart cities.

  • He, Y. B., Zhou, J., Ma, Z., Chow, J. Y., & Ozbay, K. (2020). Evaluation of city-scale built environment policies in New York City with an emerging-mobility-accessible synthetic population. Transportation Research Part A: Policy and Practice, 141, 444-467. doi: 10.1016/j.tra.2020.10.006.

    Lee, M., Chow, J. Y., Yoon, G., & He, Y. B. (2021). Forecasting e-scooter substitution of direct and access trips by mode and distance. Transportation Research Part D: Transport and Environment, 96, 102892. doi: 10.1016/j.trd.2021.102892.

Connected Automated Vehicle

As CAV technologies would significantly transform transportation systems, research on the operation and impacts of CAV technologies on society is needed more than ever. My research leverages the activity-based model and computational simulation to model the impacts of CAV on both travel demand and supply sides and search for the system equilibrium. 

  • He, Y. B., Jiang, Q., & Ma, J. (2022). Impact evaluation of connected and automated vehicles in Southern California with an activity-based approach part I: Travel behavior and demand analysis. Transportation Research Part D: Transport and Environment. doi: 10.1016/j.trd.2022.103329.

    Jiang, Q., He, Y. B., & Ma, J. (2022). Impact evaluation of connected and automated vehicles in Southern California with an activity-based approach part II: Environment and equity analysis and policy implication. Transportation Research Part D: Transport and Environment. doi: 10.1016/j.trd.2022.103381.