[1] ZHONG Y G, YANG T, CAO B, et al. On-demand ride-hailing platforms in competition with the taxi industry:pricing strategies and government supervision[J]. International Journal of Production Economics, 2022, 243:108301. [2] VEGA-GONZALO M, AGUILERA-GARCÍA Á, GOMEZ J, et al. Traditional taxi, e-hailing or ride-hailing?A GSEM approach to exploring service adoption patterns[J]. Transportation, 2023,51(2):3852-3866. [3] ASHKROF P, HOMEM DE ALMEIDA CORREIA G, CATS O, et al. On the relocation behavior of ride-sourcing drivers[J]. Transportation Letters, 2023,15(2):1-8. [4] GUO Y H, ZHANG Y, BOULAKSIL Y, et al. Multi-dimensional spatiotemporal demand forecasting and service vehicle dispatching for online car-hailing platforms[J]. International Journal of Production Research, 2022, 60(6):1832-1853. [5] MOREIRA-MATIAS L, GAMA J, FERREIRA M, et al. Predicting taxi-passenger demand using streaming data[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(3):1393-1402. [6] CRAWFORD F, WATLING D P, CONNORS R D. A statistical method for estimating predictable differences between daily traffic flow profiles[J]. Transportation Research Part B:Methodological, 2017, 95:196-213. [7] LI C, XU P. Application on traffic flow prediction of machine learning in intelligent transportation[J]. Neural Computing and Applications, 2021, 33(2):613-624. [8] ZHANG K, FENG Z Y, CHEN S Z, et al. A framework for passengers demand prediction and recommendation[C]//Proceedings of IEEE International Conference on Services Computing. San Francisco, USA:IEEE Press, 2016:340-347. [9] HAO S R, ZHANG M M, HOU A P. Short-term traffic flow forecast based on DE-RBF fussion model[J]. Journal of Physics, 2021, 19(1):012035. [10] YIN Z, LIU T, WANG C, et al. Reducing urban traffic congestion using deep learning and model predictive control[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(7):1257-1268. [11] AGAFONOV A A. Short-term traffic data forecasting:a deep learning approach[J]. Optical Memory and Neural Networks, 2021, 30(1):1-10. [12] LU H K, GE Z H, SONG Y Y, et al. A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting[J]. Neurocomputing, 2021, 427:169-178. [13] 黄晓辉,张雄,杨凯铭,等.基于联合Q值分解的强化学习网约车订单派送[J].计算机工程, 2022, 48(12):296-303, 311. HUANG X H, ZHANG X, YANG K M, et al. Reinforcement learning online car-hailing order dispatch based on joint Q-value decomposition[J]. Computer Engineering, 2022, 48(12):296-303, 311.(in Chinese) [14] LI M Q, GENG Z Y, WANG Y. Research on vehicle dispatch problem based on kuhn-munkres and reinforcement learning algorithm[C]//Proceedings of IEEE International Conference on Power Electronics, Computer Applications. Washington D. C.,USA:IEEE Press, 2021:158-167. [15] HOLLER J, VUORIO R, QIN Z W, et al. Deep reinforcement learning for multi-driver vehicle dispatching and repositioning problem[C]//Proceedings of IEEE International Conference on Data Mining. Washington D. C.,USA:IEEE Press, 2019:1090-1095. [16] LUO S, ZHANG L X, FAN Y S. Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning[J]. Computers&Industrial Engineering, 2021, 159:107489. [17] LIU Y, WU F Y, LYU C, et al. Deep dispatching:a deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform[J]. Transportation Research Part E:Logistics and Transportation Review, 2022, 161:102694. [18] LUNARDI W T, BIRGIN E G, RONCONI D P, et al. Metaheuristics for the online printing shop scheduling problem[J]. European Journal of Operational Research, 2021, 293(2):419-441. [19] ZAROUK Y, MAHDAVI I, REZAEIAN J, et al. A novel multi-objective green vehicle routing and scheduling model with stochastic demand, supply, and variable travel times[J]. Computers&Operations Research, 2022, 141:105698. [20] JIN G Y, XI Z X, SHA H Y, et al. Deep multi-view graph-based network for citywide ride-hailing demand prediction[J]. Neurocomputing, 2022, 510(C):79-94. [21] WU Y H, ZHANG H Y, LI C, et al. Urban ride-hailing demand prediction with multi-view information fusion deep learning framework[J]. Applied Intelligence, 2023, 53(8):8879-8897. [22] ZHOU Z H, FENG Q. Deep forest:towards an alternative to deep neural networks[EB/OL].[2023-06-01]. https://arxiv.org/pdf/1702.08835.pdf. [23] 桑磊,陆阳,俞磊.基于贪心策略的EDF调度算法优化[J].计算机工程, 2015, 41(12):96-100. SANG L, LU Y, YU L. Optimization of EDF scheduling algorithm based on greedy policy[J]. Computer Engineering, 2015, 41(12):96-100.(in Chinese) [24] XIAO Y Y, KONAK A. A genetic algorithm with exact dynamic programming for the green vehicle routing≻heduling problem[J]. Journal of Cleaner Production, 2017, 167:1450-1463. [25] KUHN H W. The Hungarian method for the assignment problem[J]. Naval Research Logistics, 2005, 52(1):7-21. [26] ZHAO Z, CHEN W H, WU X M, et al. LSTM network:a deep learning approach for short-term traffic forecast[J]. IET Intelligent Transport Systems, 2017, 11(2):68-75. [27] FISHER A, RUDIN C, DOMINICI F. All models are wrong, but many are useful:learning a variable's importance by studying an entire class of prediction models simultaneously[J]. Journal of Machine Learning Research, 2019, 20:177. [28] MOOSBAUER J, HERBINGER J, CASALICCHIO G, et al. Explaining hyperparameter optimization via partial dependence plots[C]//Proceedings of Advances in Neural Information Processing Systems. Cambridge, USA:MIT Press, 2021:2280-2291. |