I am currently a Research Scientist at Goergen Institute for Data Science, University of Rochester.
I hold a Ph.D. degree in Civil Engineering and an M.S. degree in Computer Science, both from University at Buffalo, the State University of New York. My research interests include Machine Learning and Deep Learning with their applications in Transportation, Healthcare, Energy and Finance.
MCFlow: Monte Carlo Flow Models for Data Imputation [code]
Trevor Richardson, Wencheng Wu, Lei Lin, Beilei Xu, and Edgar Bernal. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[[J15] Vehicle Trajectory Prediction Using LSTMs with Spatial-Temporal Attention Mechanisms]
Lei Lin , Weizi Li, Huikun Bi, and Lingqiao Qin. IEEE Intelligent Transportation Systems Magazine , 2020.
[J14] Intelligent Service Capacity Allocation for Cross-Border-E-Commerce Related Third-Party-Forwarding Logistics Operations: A Deep Learning Approach
Shuyun Ren, Tsan-Ming Choi, Ka-Man Lee, and Lei Lin. Transportation Research Part E: Logistics and Transportation Review, 2020.
[J13] Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing
Lei Lin, Weizi Li, Srinivas Peeta. Journal of Big Data Analytics in Transportations, 2019.
[J12] A novel dynamic pricing scheme for a large-scale electric vehicle sharing network considering vehicle relocation and vehicle-grid-integration
Shuyun Ren, Fengji Luo, Lei Lin, Shu-Chien Hsu, Xuran Ivan Li. International Journal of Production Economics, 2019.
[J11] A cohesion-based heuristic feature selection for short-term traffic forecasting
Lishan Liu, Ning Jia, Lei Lin, Zhengbing He. IEEE Access, 2019.
[J10] Predicting Station-level Hourly Demand in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach
Lei Lin, Zhengbing He, Srinivas Peeta. Transportation Research Part C: Emerging Technologies, 2018.
[J9] Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing plan development
Lei Lin, John C Handley, Yiming Gu, Lei Zhu, Xuejin Wen, Adel Sadek. Transportation Research Part C: Emerging Technologies, 2018.
[J8] Prediction of individual social-demographic role based on travel behavior variability using long-term GPS data
Lei Zhu, Jeffery Gonder, Lei Lin. Journal of Advanced Transportation, 2017.
[J7] A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations
Lei Lin, Qian Wang, Adel Sadek. Accident Analysis and Prevention, 2016.
[J6] Modeling the impacts of inclement weather on freeway traffic speed: exploratory study with social media data
Lei Lin, Ming Ni, Qing He, Jing Gao, Adel Sadek. Transportation Research Record, 2015.
[J5] A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction
Lei Lin, Qian Wang, Adel Sadek. Transportation Research Part C: Emerging Technologies, 2015.
[J4] Border crossing delay prediction using transient multi-server queueing models
Lei Lin, Qian Wang, Adel Sadek. Transportation Research Part A: Policy and Pratice, 2014.
[J3] Data Mining and Complex Networks Algorithms for Traffic Accident Analysis
Lei Lin, Qian Wang, Adel Sadek. Transportation Research Record, 2014.
[J2] On-line prediction of border crossing traffic using an enhanced Spinning Network method
Lei Lin, Qian Wang, Adel Sadek. Transportation Research Part C: Emerging Technologies, 2013.
[J1] Short-term forecasting of traffic volume: evaluating models based on multiple data sets and data diagnosis measures
Lei Lin, Qian Wang, Adel Sadek. Transportation Research Record, 2013.
December, 2015 - My paper “Modeling the Impacts of Inclement Weather on Freeway Traffic Speed: An Exploratory Study Utilizing Social Media Data” is reported by the Washington Post, the Accuweather, WBFO | Buffalo’s NPR News Station, and other over 30 Media.
June, 2014 - My paper “Android Smartphone Application for Collecting, Sharing and Predicting the Niagara Frontier Border Crossings Waiting Time” is selected as the winner of the ITS-NY 2014 Best Student ITS Paper Competition.