Selected Publications

Note: (co-)advised students underlined, corresponding author*

26.  He, Y. B.*, Ma, H., Jiang, Q., & Ma, J. (2024). Multi-Agent Multimodal Mobility Simulation for Mega-cities: Case of Los Angeles. Agent-Based Mobility Traffic and Transportation Models Workshop-2024. 

25. X. Han, et al., (2024). Foundation Intelligence for Smart Infrastructure Services in Transportation 5.0. IEEE Transactions on Intelligent Vehicles. doi: 10.1109/TIV.2023.3349324.

24. Yu, Q., He, Y. B., Zhu, Y., & Ma, J. (2023). Environmental justice implications of distributive equity and near-roadway air quality from zero-emission vehicle adoption in California. Nature Communications. doi: 10.1038/s41467-023-43309-9

23. Jiang, Q., Zhang, N., He, Y. B., & Ma, J. (2023). Public charging demand prediction for electric vehicles in large-scale transportation systems with a scenario- and activity-based approach. Transportation Research Part A: Policy and Practice. doi: 10.1016/j.tra.2023.103935

22. Tsukiji, T., Zhang, N., Jiang, Q., He, Y. B., & Ma, J. (2023). A Multifaceted Equity Metric System for Transportation Electrification. IEEE Open Journal of Intelligent Transportation Systems. doi: 10.1109/OJITS.2023.3311689

21. Anderson, K., et al., (2023). LA100 Equity Strategies: Executive Summary. United States Department of Energy, Office of Scientific and Technical Information. https://doi.org/10.2172/2221830

20. 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.

19. 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.

18. Ma, J., He, B. Y., Jiang, Q., & Center, M. T. (2022). Evaluating Policies and Incentives to Reduce Vehicle-Miles-Traveled and Air Pollutant Emissions through the Promotion of Telework and Remote Services (No. PSR-21-50). METRANS Transportation Center (Calif.).

17. He, Y. B., Jiang, Q., & Ma, J. (2022). Connected and Automated Vehicle Impacts in Southern California: Travel Behavior, Demand, and Transportation System Perspectives. UCLA Institute of Transportation Studies. doi:10.17610/T6NW2P

16. 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.

15. Wang, D., Tayarani, M., He, Y. B., Gao, J., Chow, J. Y., Gao, H. O., & Ozbay, K. (2021). Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit. Transportation Research Part A: Policy and Practice. doi: 10.1016/j.tra.2021.09.005.

14. 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.

13. 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.

12. He, Y. B., & Chow, J. Y. (2021). Gravity model of passenger and mobility fleet origin–destination patterns with partially observed service data. Transportation Research Record. doi: 10.1177/0361198121992074.

11. 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.

10. Wang, D., Zuo, F., Gao, J., He, Y. B., Bian, Z., Bernardes, S. D., ... & Ban, X. J. (2020). Agent-based simulation model and deep learning techniques to evaluate and predict transportation trends around COVID-19. C2SMART White Paper.

9. 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.

8. Wang, D., Ozbay, K., He, Y. B., Shen, Y., Chow, J. Y. J. (2019). Traffic flow model calibration for an agent-based traffic simulation model applied in New York City. 8th International Symposium on Dynamic Traffic Assignment. [Extended abstract accepted]

7. He, Y. B., & Chow, J. Y. (2019). Optimal privacy control for transport network data sharing. Transportation Research Part C: Emerging Technologies. doi: 10.1016/j.trc.2019.07.010. (Also accepted by the 23rd International Symposium on Transportation and Traffic Theory, 13.2% acceptance rate).

6. He, Y. B., Chow, J. Y. J. & Nourinejad, M. (2017). A privacy design problem for sharing transport service tour data. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). doi: 10.1109/itsc.2017.8317692

5. 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.

4. Wen, Y., Yu, Q., He, Y. B., Ma, J., Zhang, S., Wu, Y., & Zhu, Y. (2024). Persistent environmental injustice due to brake and tire wear emissions and heavy-duty trucks in future California zero-emission fleets. Nature Sustainability. [Under review]

3. Zhang, N., Chu, Y., Jiang, Q., He, Y. B., & Ma, J. (2023). Multi-scale Vulnerability Analysis for Transportation Electrification under Extreme Weather Events. International Journal of Disaster Risk Reduction. [Under review]

2. Jiang, Q., He, Y. B., & Ma, J. (2023). Large-scale network speed prediction during hurricane evacuation with limited data: a deep learning approach. IEEE Intelligent Transportation Systems Magazine. [Under review]

1. Jiang, Q., He, Y. B. *, Qiao, Y., Zhu, Y., & Ma, J. (2023). Is Tele-X the solution for reducing traffic congestion and emissions? Findings from a comprehensive activity-based modeling analysis. [Under review]