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Owing to the benefits for customers (lower prices), drivers (higher revenues), aggregation companies (higher revenues) and the environment (fewer vehicles), on-demand ride pooling (e.g., Uber pool, Grab Share) has become quite popular.
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Yan Huang, Favyen Bastani, Ruoming Jin, and Xiaoyang Sean Wang · 2014
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Quantifying the benefits of vehicle pooling with shareability networks
Paolo Santi, Giovanni Resta, Michael Szell, Stanislav Sobolevsky, Steven H Strogatz, and Carlo Ratti · 2014
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Large-scale order dispatch in on-demand ride-hailing platforms: A learning and planning approach
Zhe Xu, Zhixin Li, Qingwen Guan, Dingshui Zhang, Qiang Li, Junxiao Nan, Chunyang Liu, Wei Bian, and Jieping Ye · 2018
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ZAC: A zone path construction approach for effective real-time ridesharing
Meghna Lowalekar, Pradeep Varakantham, and Patrick Jaillet · 2019
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Neural approximate dynamic programming for on-demand ride-pooling
Sanket Shah, Meghna Lowalekar, and Pradeep Varakantham · 2020
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Efficient large-scale fleet management via multi-agent deep reinforcement learning
Kaixiang Lin, Renyu Zhao, Zhe Xu, and Jiayu Zhou · 2018
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Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
T. Rashid, M. Samvelyan, C. Schroeder, G. Farquhar, J. Foerster, , and S Whiteson · 2018
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Deep implicit coordination graphs for multi-agent reinforcement learning
S. Li, J.K. Gupta, P. Morales, R. Allen, and M.J. Kochenderfer · 2021
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Zone path construction (zac) based approaches for effective real-time ridesharing
Meghna Lowalekar, Pradeep Varakantham, and Patrick Jaillet · 2021
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