Related references
Note: Only part of the references are listed.Context-Aware Taxi Dispatching at City-Scale Using Deep Reinforcement Learning
Zhidan Liu et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)
A simulation-optimization framework for a dynamic electric ride-hailing sharing problem with a novel charging strategy
Xingbin Zhan et al.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW (2022)
Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform
Yang Liu et al.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW (2022)
Ride-Hailing Service aware Electric Taxi Fleet Management using Reinforcement Learning
Paul Silva et al.
2022 THIRTEENTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS (ICUFN) (2022)
A novel order evaluation model with nested probabilistic-numerical linguistic information applied to traditional order grabbing mode
Zijing Ge et al.
APPLIED INTELLIGENCE (2021)
Decentralised cooperative cruising of autonomous ride-sourcing fleets
Linji Chen et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2021)
Optimal operations planning of electric autonomous vehicles via asynchronous learning in ride-hailing systems
Guodong Yu et al.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE (2021)
Dynamic planning for simultaneous recharging and relocation of shared electric taxies: A sequential MILP approach
Helia Jamshidi et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2021)
Mobility-Aware Charging Scheduling for Shared On-Demand Electric Vehicle Fleet Using Deep Reinforcement Learning
Yanchang Liang et al.
IEEE TRANSACTIONS ON SMART GRID (2021)
Robust matching-integrated vehicle rebalancing in ride-hailing with uncertain demand
Xiaotong Guo et al.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2021)
Real-world ride-hailing vehicle repositioning using deep reinforcement learning
Yan Jiao et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2021)
Optimizing flexible one-to-two matching in ride-hailing systems with boundedly rational users
Yuanyuan Li et al.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW (2021)
Multi-Objective Distributional Reinforcement Learning for Large-Scale Order Dispatching
Fan Zhou et al.
2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021) (2021)
Deep Reinforcement Learning for EV Charging Navigation by Coordinating Smart Grid and Intelligent Transportation System
Tao Qian et al.
IEEE TRANSACTIONS ON SMART GRID (2020)
Enhanced Coordinated Operations of Electric Power and Transportation Networks via EV Charging Services
Tao Qian et al.
IEEE TRANSACTIONS ON SMART GRID (2020)
A Mutual Selection Mechanism of Ride-Hailing Based on Hidden Points
Yi Jiang et al.
WIRELESS COMMUNICATIONS & MOBILE COMPUTING (2020)
Ride-Hailing Order Dispatching at DiDi via Reinforcement Learning
Zhiwei (Tony) Qin et al.
INFORMS JOURNAL ON APPLIED ANALYTICS (2020)
Dynamic pricing and fleet management for electric autonomous mobility on demand systems
Berkay Turan et al.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2020)
Risk-aware Energy Management of Extended Range Electric Delivery Vehicles with Implicit Quantile Network
Pengyue Wang et al.
2020 IEEE 16TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE) (2020)
Operating Electric Vehicle Fleet for Ride-Hailing Services With Reinforcement Learning
Jie Shi et al.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)
A BCMP network approach to modeling and controlling autonomous mobility-on-demand systems
Ramon Iglesias et al.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH (2019)
A Markov decision process approach to vacant taxi routing with e-hailing
Xinlian Yu et al.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2019)
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
John Holler et al.
2019 19TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2019) (2019)
A Deep Value-network Based Approach for Multi-Driver Order Dispatching
Xiaocheng Tang et al.
KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2019)
Optimizing Order Dispatch for Ride-sharing Systems
Yubin Duan et al.
2019 28TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND NETWORKS (ICCCN) (2019)
Analysis, Control, and Evaluation of Mobility-on-Demand Systems: A Queueing-Theoretical Approach
Rick Zhang et al.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS (2019)
Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms
Federico Rossi et al.
AUTONOMOUS ROBOTS (2018)
How to deal with uncertainties in electric power systems? A review
A. Rezaee Jordehi
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)
Control of robotic mobility-on-demand systems: A queueing-theoretical perspective
Rick Zhang et al.
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH (2016)
Human-level control through deep reinforcement learning
Volodymyr Mnih et al.
NATURE (2015)
A class of distortion operators for pricing financial and insurance risks
SS Wang
JOURNAL OF RISK AND INSURANCE (2000)
Share Paper
