| A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources S Yang, W Ma, X Pi, S Qian Transportation Research Part C: Emerging Technologies 107, 248-265, 2019 | 193 | 2019 |
| iLOCuS: Incentivizing vehicle mobility to optimize sensing distribution in crowd sensing S Xu, X Chen, X Pi, C Joe-Wong, P Zhang, HY Noh IEEE Transactions on Mobile Computing, 2019 | 95 | 2019 |
| Pas: Prediction-based actuation system for city-scale ridesharing vehicular mobile crowdsensing X Chen, S Xu, J Han, H Fu, X Pi, C Joe-Wong, Y Li, L Zhang, HY Noh, ... IEEE Internet of Things Journal 7 (5), 3719-3734, 2020 | 66 | 2020 |
| A general formulation for multi-modal dynamic traffic assignment considering multi-class vehicles, public transit and parking X Pi, W Ma, ZS Qian Transportation Research Part C: Emerging Technologies 104, 369-389, 2019 | 56 | 2019 |
| Estimating multi-class dynamic origin-destination demand through a forward-backward algorithm on computational graphs W Ma, X Pi, S Qian Transportation Research Part C: Emerging Technologies 119, 102747, 2020 | 51 | 2020 |
| Understanding transit system performance using avl-apc data: An analytics platform with case studies for the pittsburgh region X Pi, M Egge, J Whitmore, ZS Qian, A Silbermann Journal of Public Transportation 21 (2), 19-40, 2018 | 32 | 2018 |
| A stochastic optimal control approach for real-time traffic routing considering demand uncertainties and travelers’ choice heterogeneity X Pi, ZS Qian Transportation Research Part B: Methodological 104, 710-732, 2017 | 32 | 2017 |
| Vehicle dispatching for sensing coverage optimization in mobile crowdsensing systems S Xu, X Chen, X Pi, C Joe-Wong, P Zhang, HY Noh 2019 18th ACM/IEEE International Conference on Information Processing in …, 2019 | 24 | 2019 |
| Incentivizing vehicular crowdsensing system for large scale smart city applications S Xu, X Chen, X Pi, C Joe-Wong, P Zhang, HY Noh Sensors and Smart Structures Technologies for Civil, Mechanical, and …, 2019 | 15 | 2019 |
| Spatiotemporal arbitrage of large-scale portable energy storage for grid congestion relief G He, D Zhang, X Pi, Q Chen, S Kar, J Whitacre 2019 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2019 | 10 | 2019 |
| A deep learning approach to real-time parking occupancy prediction in spatio-temporal networks incorporating multiple spatio-temporal data sources S Yang, W Ma, X Pi, S Qian arXiv preprint arXiv:1901.06758, 2019 | 8 | 2019 |
| Large-scale mesoscopic network modeling with cars and trucks: a case study in Pittsburgh X Pi, W Ma, ZS Qian Transportation Research Board 98th Annual MeetingTransportation Research Board, 2019 | 3 | 2019 |
| Understanding human perception of bus fullness: An empirical study of crowdsourced fullness ratings and automatic passenger count data X Pi, Z Qian, A Steinfeld, Y Huang Transportation research record 2672 (8), 475-484, 2018 | 3 | 2018 |
| Mobility Data Analytics Center S Qian, X Pi, Z Hu | 2 | 2016 |
| Predicting Occupancy of Parking Spaces in Transportation Networks: A Deep Learning Approach with Multi-Source Spatio-Temporal Data S Yang, W Ma, X Pi, ZS Qian Transportation Research Board 98th Annual MeetingTransportation Research Board, 2019 | 1 | 2019 |
| Publication Submission Form A Steinfeld, RD Aziz, L Von Dehsen, SY Park, J Maisel, E Steinfeld Journal Article 26 (3), 45-64, 2019 | | 2019 |
| Multi-modal Mesoscopic Transportation System Modeling and Management with Mobility Data X Pi Carnegie Mellon University, 2019 | | 2019 |
| Traffic Impact Study of CSX Pittsburgh Intermodal Rail Terminal and Mitigation Plans for McKees Rocks S Qian, X Pi, W Ma Mobility21, Carnegie Mellon University, 2019 | | 2019 |
| Data-Driven Network Models for Analyzing Multi-Modal Transportation Systems ZS Qian, X Pi, W Ma | | 2018 |
Sean QianProfessor, Carnegie Mellon University在 cmu.edu 的电子邮件经过验证
