Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand
With the improvement of people’s living standards, people’s demand of traveling by taxi is increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their operational experience or cruise randomly to find passengers. Without macroguidance, the role of the taxi system c...
Saved in:
Main Authors: | Yongjian Yang, Xintao Wang, Yuanbo Xu, Qiuyang Huang |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2020-01-01
|
Series: | Journal of Advanced Transportation |
Online Access: | http://dx.doi.org/10.1155/2020/8674512 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data
by: Xinmin Liu, et al.
Published: (2020-01-01) -
Taxi Efficiency Measurements Based on Motorcade-Sharing Model: Evidence from GPS-Equipped Taxi Data in Sanya
by: Jiawei Gui, et al.
Published: (2018-01-01) -
The Optimal Taxi Fleet Size Structure under Various Market Regimes When Charging Taxis with Link-Based Toll
by: Jincheng Zhu, et al.
Published: (2013-01-01) -
The Sense of Community of Online Taxi Drivers
by: Ratri Arista, et al.
Published: (2022-01-01) -
TaxiInt: Predicting the Taxi Flow at Urban Traffic Hotspots Using Graph Convolutional Networks and the Trajectory Data
by: Jinmao Zhang, et al.
Published: (2021-01-01)