| Optimization models for electric vehicle service operations: A literature review ZJM Shen, B Feng, C Mao, L Ran Transportation Research Part B: Methodological 128, 462-477, 2019 | 245 | 2019 |
| Dispatch of autonomous vehicles for taxi services: A deep reinforcement learning approach C Mao, Y Liu, ZJM Shen Transportation Research Part C: Emerging Technologies 115, 102626, 2020 | 120 | 2020 |
| A reinforcement learning framework for the adaptive routing problem in stochastic time-dependent network C Mao, Z Shen Transportation Research Part C: Emerging Technologies 93, 179-197, 2018 | 71 | 2018 |
| An agent-based choice model for travel mode and departure time and its case study in Beijing M Zou, M Li, X Lin, C Xiong, C Mao, C Wan, K Zhang, J Yu Transportation Research Part C: Emerging Technologies 64, 133-147, 2016 | 57 | 2016 |
| Minimizing the total costs of urban transit systems can reduce greenhouse gas emissions: The case of San Francisco H Cheng, C Mao, S Madanat, A Horvath Transport Policy 66, 40-48, 2018 | 25 | 2018 |
| Potential greenhouse gas emission reductions from optimizing urban transit networks S Madanat, A Horvath, C Mao, H Cheng | 3 | 2016 |
| Reinforcement Learning for Operational Problems in Transportation Systems with Autonomous Vehicles C Mao UC Berkeley, 2019 | 1 | 2019 |
| Potential Greenhouse Gas Emissions Reductions from Optimizing Urban Transit Networks H Cheng, C Mao, S Madanat, A Horvath California. Dept. of Transportation. Division of Research and Innovation, 2016 | | 2016 |
