Liang Hu is a Senior Researcher at Huawei Technologies Canada. He holds Ph.D. degree in Transportation Engineering from Iowa State University and received the Research Excellence Award. His work applies operations research and data mining to product R&D for intelligent transportation systems, smart cities, future mobility, and emerging vehicle technologies. He published multiple papers in top journals and has received 200+ citations. Previous to joining Huawei, he worked as the Postdoctoral Research Associate at Iowa State University and the intern at General Motors and Oak Ridge National Laboratory. He received Bachelor degree from China Agricultural University and Master degree from Tongji University, both in Automotive Engineering. [CV] [简历]

Research Interests

• Intelligent Transportation Systems (ITS)
• Smart Cities
• Emerging vehicle technologies (electric, connected, and autonomous vehicles)
• Future mobility
• Traffic signal control
• Supply chain and logistics
• Operations research and data mining application to various transportation problems

Publications

You can also find more about my research on Google Scholar and ResearchGate.

Peer-reviewed Articles

• Hu, L. and Dong, J., 2020. An Artificial-Neural-Network-Based Model for Real-Time Dispatching of Electric Autonomous Taxis. IEEE Transactions on Intelligent Transportation Systems. link
• Dong, J., Wu, X., Liu, C., Lin, Z. and Hu, L., 2020. The impact of reliable range estimation on battery electric vehicle feasibility. International Journal of Sustainable Transportation, 14 (11), pp.833-842. link
• Hu, L., Dong, J. and Lin, Z., 2019. Modeling charging behavior of battery electric vehicle drivers: A cumulative prospect theory based approach. Transportation Research Part C: Emerging Technologies, 102, pp.474-489. link
• Lu, C., Dong, J. and Hu, L., 2019. Energy-Efficient Adaptive Cruise Control for Electric Connected and Autonomous Vehicles. IEEE Intelligent Transportation Systems Magazine, 11 (3), pp.42-55. link
• Lu, C., Dong, J., Hu, L. and Liu, C., 2019. An ecological adaptive cruise control for mixed traffic and its stabilization effect. IEEE Access, 7, pp.81246-81256. link
• Hu, L., Dong, J., Lin, Z. and Yang, J., 2018. Analyzing battery electric vehicle feasibility from taxi travel patterns: The case study of New York City, USA. Transportation Research Part C: Emerging Technologies, 87, pp.91-104. link
• Yang, J., Dong, J. and Hu, L., 2018. Design government incentive schemes for promoting electric taxis in China. Energy Policy, 115, pp.1-11. link
• Yang, J., Dong, J. and Hu, L., 2017. A data-driven optimization-based approach for siting and sizing of electric taxi charging stations. Transportation Research Part C: Emerging Technologies, 77, pp.462-477. (Highly Cited Paper) link
• Yang, J., Dong, J., Lin, Z. and Hu, L., 2016. Predicting market potential and environmental bene fits of deploying electric taxis in Nanjing, China. Transportation Research Part D: Transport and Environment, 49, pp.68-81. link
• Hu, L., Wang, M.Q. and Kong, D.Y., 2015. A Calculation Model for Greenhouse Gas Emission Impacts of Electric Vehicle Carsharing. Low-carbon City and New-type Urbanization, pp.129-138. link

Others

• Hu, L., 2019. Data-driven analyses of future electric personal mobility. Doctoral dissertation, Iowa State University. link
• Lu, C., Hu, L. and Dong, J., 2018. Ecological Adaptive Cruise Control in a Traffic Stream with Mixed Autonomous and Manually Driven Vehicles. Transportation Research Board 97th Annual Meeting, Washington, D.C., No. 18-05434. link
• Dong, J., Lu, C. and Hu, L., 2018. Estimating Energy Efficiency of Connected and Autonomous Vehicles in a Mixed Fleet (No. IHRB Project TR-708D). Center for Transportation Research and Education, Iowa State University. link
• Hu, L. and Dong, J., 2017. Investigation of the Link Between Macroscopic Traffic Flow Characteristics and Individual Vehicle Fuel Consumption. Center for Transportation Research and Education, Iowa State University. link

Talks

• 2020 INFORMS Annual Meeting. An Artificial Neural Network Based Model for Real Time Dispatching of Electric Autonomous Taxis. Seattle, Washington, October 2019.
• USDOT T3e Webinar. Energy-Efficient Adaptive Cruise Control for Electric Connected and Autonomous Vehicles. Washington, D.C., March 2018. link
• 2017 INFORMS Annual Meeting. Modeling the Operations of Electric Autonomous Taxis in New York City. Houston, Texas, October 2017. link
• Automated Vehicles Symposium (AVS) 2017. Estimating Energy Efficiency of Connected and Autonomous Vehicles in a Mixed Fleet. San Francisco, California, July 2017. link
• Transportation Research Board (TRB) 96th Annual Meeting. An Investigation of Taxi Travel Patterns and Implications on the Feasibility of Electric Taxis. Washington, D.C., January 2017. link
• The 29th World Electric Vehicle Symposium (EVS) and Exhibition. Optimal Charging Infrastructure Planning for Electric Taxi Fleet. Montreal, Quebec, June 2016. link

Professional Services

I’m the reviewer for these journals. Please email me if you’d like to appoint me as a reviewer for your journal.

• Nature Energy
• IEEE Transactions on Intelligent Vehicles
• IEEE Access
• Proceedings of the IEEE
• Transportation Research Part D: Transport and Environment
• Transport Policy
• Journal of Cleaner Production
• Knowledge and Information Systems
• International Journal of Multimedia Intelligence and Security
• IEEE Intelligent Transportation Systems Magazine
• Journal of Advanced Transportation
• Journal of Central South University
• Transportation Research Record
• IEEE Intelligent Transportation Systems Conference
• SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy
• SAE Technical Papers

Awards

• Timely Incentive Award, Huawei Technologies Canada, July 2020
• Research Excellence Award, Iowa State University, May 2019
• Outstanding Undergraduates Award, Beijing Municipal Commission of Education, June 2013
• Yihai Kerry Scholarship, October 2012
• National Scholarship, Chinese Ministry of Education, October 2010