"Big Data" for Human Mobility
This is a spin-off of the DOE CERC-CVC project, aiming to understand human mobility dynamics and implications to transportation sustainability using "big data" and data science methods. In particular, we use large-scale, real-time taxi trajectory data for large cities (e.g., ~20,000 vehicles for 30 days) to characterize mobility dynamics at the individual level. Based on this characterization of individual mobility dynamics, we evaluate environmental implications of large-scale deployment of electric vehicles, optimize the siting of public charging infrastructure for electric vehicles, and assess the environmental benefits of ride-sharing.
- Cai, H.; Rao, R.; Xu, M. Modeling electric taxis’ charging behavior using real-world data. International Journal of Sustainable Transportation, in press.
- Cai, H.*; Zhan, X.-W.; Zhu, J.; Jia, X.-P.; Chiu, A. S. F.; Xu, M.* Understanding taxi travel patterns. Physica A: Statistical Mechanics and Its Applications 2016, 457, 590-597.
- Shahraki, N.; Cai, H.; Turkay, M.; Xu, M. Optimal locations of electric public charging stations using real world vehicle travel patterns. Transportation Research Part D: Transport and Environment 2015, 41, 165-176.
- Xu, M.*; Cai, H.; Liang, S. Big data and industrial ecology. Journal of Industrial Ecology 2015, 19 (2), 205-210.
- Cai, H.; Jia, X.-P.; Chiu, A. S. F.; Hu, X.-J.; Xu, M.* Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet. Transportation Research Part D: Transport and Environment 2014, 33, 39-46.
- Cai, H.; Xu, M.* Greenhouse gas implications of fleet electrification based on Big Data-informed individual travel patterns. Environmental Science & Technology 2013, 47 (16), 9035-9043.
