Fetching the paper…
Reading the bibliography…
The emergence of on-demand ride pooling services allows each vehicle to serve multiple passengers at a time, thus increasing drivers' income and enabling passengers to travel at lower prices than taxi/car on-demand services (only one passenger can be assigned to a car at a time like UberX and Lyft).
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM) (Beijing, China) (CIKM ’19) . Association for Computing Machinery, New York, NY, USA, 1983–1992
Jiarui Jin, Ming Zhou, Weinan Zhang, Minne Li, Zilong Guo, Zhiwei Qin, Yan Jiao, Xiaocheng Tang, Chenxi Wang, Jun Wang, Guobin Wu, and Jieping Ye. 2019 · 1992
Earlier work this paper cites.
Graphical Models, Exponential Families, and Variational Inference
Martin J. Wainwright and Michael I. Jordan. 2008 · 2008
Earlier work this paper cites.
Branch and Cut and Price for the Pickup and Delivery Problem with Time Windows
Stefan Ropke and Jean-François Cordeau. 2009 · 2009
Earlier work this paper cites.
A Collaborative Multiagent Taxi-Dispatch System
Kiam Tian Seow, Nam Hai Dang, and Der-Horng Lee. 2010 · 2009
Earlier work this paper cites.
Partially observable Markov decision processes
Matthijs TJ Spaan. 2012 · 2012
Earlier work this paper cites.
T-share: A large-scale dynamic taxi ridesharing service. In Data Engineering (ICDE), 2013 IEEE 29th International Conference on . IEEE, 410–421
Shuo Ma, Yu Zheng, and Ouri Wolfson. 2013 · 2013
Earlier work this paper cites.
T-Finder: A Recommender System for Finding Passengers and Vacant Taxis
X. Xie, Y. Zheng, L. Zhang, and N. Yuan. 2013 · 2013
Earlier work this paper cites.
A Cost-Effective Recommender System for Taxi Drivers. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, New York, USA) (KDD ’14) . Association for Computing Machinery, New York, NY, USA, 45–54
Meng Qu, Hengshu Zhu, Junming Liu, Guannan Liu, and Hui Xiong. 2014 · 2014
Earlier work this paper cites.
Quantifying the benefits of vehicle pooling with shareability networks
Paolo Santi, Giovanni Resta, Michael Szell, Stanislav Sobolevsky, Steven H Strogatz, and Carlo Ratti. 2014 · 2014
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. 2015 · 2015
Earlier work this paper cites.
A survey on dynamic and stochastic vehicle routing problems
Ulrike Ritzinger, Jakob Puchinger, and Richard Hartl. 2016 · 2015
Earlier work this paper cites.
Inside Uber’s quest to get more people in fewer cars
Alex Heath. 2016 · 2016
Earlier work this paper cites.
New York Yellow Taxi DataSet
NYYellowTaxi. 2016 · 2016
Earlier work this paper cites.
Deep Variational Information Bottleneck. In ICLR
Alex Alemi, Ian Fischer, Josh Dillon, and Kevin Murphy. 2017 · 2017
Cited alongside, same era.
On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment
Javier Alonso-Mora, Samitha Samaranayake, Alex Wallar, Emilio Frazzoli, and Daniela Rus. 2017 · 2017
Cited alongside, same era.
OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks
Geoff Boeing. 2017 · 2017
Cited alongside, same era.
Data-Driven Distributionally Robust Vehicle Balancing Using Dynamic Region Partitions. In Proceedings of the 8th International Conference on Cyber-Physical Systems (ICCPS ’17) . Association for Computing Machinery, New York, NY, USA, 261–271
Fei Miao, Shuo Han, Abdeltawab M. Hendawi, Mohamed E Khalefa, John A. Stankovic, and George J. Pappas. 2017 · 2017
Cited alongside, same era.
Spatio-Temporal Capsule-Based Reinforcement Learning for Mobility-on-Demand Network Coordination. In The World Wide Web Conference (San Francisco, CA, USA) (WWW ’19) . Association for Computing Machinery, New York, NY, USA, 2806–2813
Suining He and Kang G. Shin. 2019 · 2019
Later among the works it cites.
Balancing efficiency and fairness in on-demand ridesourcing. In Advances in Neural Information Processing Systems . 5309–5319
Nixie S Lesmana, Xuan Zhang, and Xiaohui Bei. 2019 · 2019
Later among the works it cites.
ZAC: A Zone Path Construction Approach for Effective Real-Time Ridesharing. In ICAPS
Meghna Lowalekar, Pradeep Varakantham, and Patrick Jaillet. 2019 · 2019
Later among the works it cites.
A Deep Value-Network Based Approach for Multi-Driver Order Dispatching. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19) . Association for Computing Machinery, New York, NY, USA, 1780–1790
Xiaocheng Tang, Zhiwei (Tony) Qin, Fan Zhang, Zhaodong Wang, Zhe Xu, Yintai Ma, Hongtu Zhu, and Jieping Ye. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A Taxi Order Dispatch Model Based On Combinatorial Optimization. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Halifax, NS, Canada) (KDD ’17) . Association for Computing Machinery, New York, NY, USA, 2151–2159
Lingyu Zhang, Tao Hu, Yue Min, Guobin Wu, Junying Zhang, Pengcheng Feng, Pinghua Gong, and Jieping Ye. 2017 · 2017
Cited alongside, same era.
Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (London, United Kingdom) (KDD ’18) . Association for Computing Machinery, New York, NY, USA, 1774–1783
Kaixiang Lin, Renyu Zhao, Zhe Xu, and Jiayu Zhou. 2018 · 2018
Cited alongside, same era.
Credit Assignment For Collective Multiagent RL With Global Rewards. In Advances in Neural Information Processing Systems , Vol. 31. Curran Associates, Inc
Duc Thien Nguyen, Akshat Kumar, and Hoong Chuin Lau. 2018 · 2018
Cited alongside, same era.
MOVI: A Model-Free Approach to Dynamic Fleet Management. In IEEE INFOCOM 2018 - IEEE Conference on Computer Communications (Honolulu, HI, USA). IEEE Press, 2708–2716
Takuma Oda and Carlee Joe-Wong. 2018 · 2018
Cited alongside, same era.
PrivateHunt: Multi-Source Data-Driven Dispatching in For-Hire Vehicle Systems
Xiaoyang Xie, Fan Zhang, and Desheng Zhang. 2018 · 2018
Cited alongside, same era.
Taxi Dispatch Planning via Demand and Destination Modeling. In 2018 IEEE 43rd Conference on Local Computer Networks (LCN) . IEEE Computer Society, Los Alamitos, CA, USA, 377–384
J. Xu, R. Rahmatizadeh, L. Boloni, and D. Turgut. 2018b · 2018
Cited alongside, same era.
Mean Field Multi-Agent Reinforcement Learning. In Proceedings of the 35th International Conference on Machine Learning (ICML) , Vol. 80. 5571–5580
Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang. 2018 · 2018
Cited alongside, same era.
Uber vs. Lyft: Who’s tops in the battle of U.S. rideshare companies
Kathryn Gessner. 2019 · 2019
Cited alongside, same era.
Multi-Agent Reinforcement Learning for Order-Dispatching via Order-Vehicle Distribution Matching. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM) (Beijing, China) (CIKM ’19) . Association for Computing Machinery, New York, NY, USA, 2645–2653
Ming Zhou, Jiarui Jin, Weinan Zhang, Zhiwei Qin, Yan Jiao, Chenxi Wang, Guobin Wu, Yong Yu, and Jieping Ye. 2019 · 2019
Later among the works it cites.
Stable training of bellman error in reinforcement learning. In Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings, Part V 27 . Springer, 439–448
Chen Gong, Yunpeng Bai, Xinwen Hou, and Xiaohui Ji. 2020 · 2020
Later among the works it cites.
Neural Approximate Dynamic Programming for On-Demand Ride-Pooling. In The Thirty-Fourth AAAI Conference on Artificial Intelligence . AAAI Press, 507–515
Sanket Shah, Meghna Lowalekar, and Pradeep Varakantham. 2020 · 2020
Later among the works it cites.
Structural relational inference actor-critic for multi-agent reinforcement learning
Xianjie Zhang, Yu Liu, Xiujuan Xu, Qiong Huang, Hangyu Mao, and Anil Carie. 2021 · 2021
Later among the works it cites.
Mind your data! hiding backdoors in offline reinforcement learning datasets
Chen Gong, Zhou Yang, Yunpeng Bai, Junda He, Jieke Shi, Arunesh Sinha, Bowen Xu, Xinwen Hou, Guoliang Fan, and David Lo. 2022a · 2022
Later among the works it cites.
Pruning the Communication Bandwidth between Reinforcement Learning Agents through Causal Inference: An Innovative Approach to Designing a Smart Grid Power System
Xianjie Zhang, Yu Liu, Wenjun Li, and Chen Gong. 2022a · 2022
Later among the works it cites.
Common belief multi-agent reinforcement learning based on variational recurrent models
Xianjie Zhang, Yu Liu, Hangyu Mao, and Chao Yu. 2022b · 2022
Later among the works it cites.
Future Aware Pricing and Matching for Sustainable On-Demand Ride Pooling. In Thirty-Seventh AAAI Conference on Artificial Intelligence, (AAAI) . 14628–14636
Xianjie Zhang, Pradeep Varakantham, and Hao Jiang. 2023 · 2023
Closest in time.