| A literature review of Artificial Intelligence applications in railway systems R Tang, L De Donato, N Besinović, F Flammini, RMP Goverde, Z Lin, ... Transportation Research Part C: Emerging Technologies 140, 103679, 2022 | 125 | 2022 |
| Artificial intelligence in railway transport: Taxonomy, regulations, and applications N Bešinović, L De Donato, F Flammini, RMP Goverde, Z Lin, R Liu, ... IEEE Transactions on Intelligent Transportation Systems 23 (9), 14011-14024, 2021 | 63 | 2021 |
| Incorporating weather conditions and travel history in estimating the alighting bus stops from smart card data T Tang, R Liu, C Choudhury Sustainable Cities and Society 53, 101927, 2020 | 48 | 2020 |
| Multi-stage deep learning approaches to predict boarding behaviour of bus passengers T Tang, A Fonzone, R Liu, C Choudhury Sustainable Cities and Society 73, 103111, 2021 | 21 | 2021 |
| Predicting hourly boarding demand of bus passengers using imbalanced records from smart-cards: A deep learning approach T Tang, R Liu, C Choudhury, A Fonzone, Y Wang IEEE Transactions on Intelligent Transportation Systems 24 (5), 5105-5119, 2023 | 7 | 2023 |
| A holistic data-driven framework for developing a complete profile of bus passengers S Chen, X Liu, C Lyu, L Vlacic, T Tang, Z Liu Transportation Research Part A: Policy and Practice 173, 103692, 2023 | 5 | 2023 |
| Bus OD matrix reconstruction based on clustering Wi-Fi probe data Y Wang, W Zhang, T Tang, D Wang, Z Liu Transportmetrica B: Transport Dynamics 10 (1), 864-879, 2022 | 5 | 2022 |
| A simulation-based model for evacuation demand estimation under unconventional metro emergencies Y Wang, T Tang Journal of transportation engineering, Part A: Systems 149 (7), 04023053, 2023 | 3 | 2023 |
| A data-driven framework for natural feature profile of public transport ridership: Insights from Suzhou and Lianyungang, China T Tang, Z Gu, Y Yang, H Sun, S Chen, Y Chen Transportation Research Part A: Policy and Practice 183, 104049, 2024 | 2 | 2024 |
| MTLMetro: A Deep Multi-Task Learning Model for Metro Passenger Demands Prediction H Huang, J Mao, R Liu, W Lu, T Tang, L Liu IEEE Transactions on Intelligent Transportation Systems, 1-16, 2024 | 1 | 2024 |
| Predicting boarding and alighting behaviour of bus passengers with smart card data using machine learning techniques T Tang University of Leeds, 2020 | 1 | 2020 |
| A holistic approach to multi-depot electric bus scheduling for energy saving considering limitations in charging facilities Y Wang, J Chen, T Tang, Z Liu Energy, 131880, 2024 | | 2024 |
| On the Temporal-spatial Analysis of Estimating Urban Traffic Patterns Via GPS Trace Data of Car-hailing Vehicles J Mao, L Liu, H Huang, W Lu, K Yang, T Tang, H Shi arXiv preprint arXiv:2306.07456, 2023 | | 2023 |
| Investigating the effect of weather on bus origin-destination patterns: A case study from Changsha, China T Tang, R Liu, C Choudhury 7th Symposium of the European Association for Research in Transportation, 2018 | | 2018 |
| Enhancing Urban Traffic Sustainability: Anomaly Detection in Time Series Incorporating Spatial Correlations and Temporal Evolutions J Mao, L Liu, B Ran, H Huang, T Tang, W Lu, H Shi Available at SSRN 4737146, 0 | | |
