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  • Yang S, Ma W, Pi X, et al. A deep learning approach to real-time parking occupancy prediction in transportation networks incorporating multiple spatio-temporal data sources[J]//Transportation Research Part C: Emerging Technologies. 2019, 107: 248-265. Link

  • Do LN, Vu HL, Vo BQ, et al. An effective spatial-temporal attention based neural network for traffic flow prediction[J]//Transportation research part C: emerging technologies. 2019, 108: 12-28. Link

  • Ma J, Chan J, Ristanoski G, et al. Bus travel time prediction with real-time traffic information[J]//Transportation Research Part C: Emerging Technologies. 2019, 105: 536-549. Link

  • Liu Y, Liu Z, Jia R. DeepPF: A deep learning based architecture for metro passenger flow prediction[J]//Transportation Research Part C: Emerging Technologies. 2019, 101: 18-34. Link

  • Dai X, Fu R, Zhao E, et al. DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending[J]//Transportation Research Part C: Emerging Technologies. 2019, 103: 142-157. Link

  • Zhang Z, Li M, Lin X, et al. Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies[J]//Transportation research part C: emerging technologies. 2019, 105: 297-322. Link

  • Hao S, Lee DH, Zhao D. Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system[J]//Transportation Research Part C: Emerging Technologies. 2019, 107: 287-300. Link

  • Gu Y, Lu W, Qin L, et al. Short-term prediction of lane-level traffic speeds: A fusion deep learning model[J]//Transportation research part C: emerging technologies. 2019, 106: 1-6. Link

  • Ermagun A, Levinson D. Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending[J]//Transportation Research Part C: Emerging Technologies. 2019, 104: 38-52. Link

  • Wang J, Chen R, He Z. Traffic speed prediction for urban transportation network: A path based deep learning approach[J]//Transportation Research Part C: Emerging Technologies. 2019, 100: 372-385. Link