Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services

强化学习 计算机科学 匹配(统计) 马尔可夫决策过程 服务(商务) 人气 订单(交换) 过程(计算) 马尔可夫过程 闲置 人工智能 运筹学 工程类 社会心理学 统计 数学 操作系统 经济 经济 心理学 财务
作者
Mingyue Xu,Peng Yue,Fan Yu,Can Yang,Mingda Zhang,Shangcheng Li,Hao Li
出处
期刊:International Journal of Geographical Information Science [Taylor & Francis]
卷期号:37 (2): 380-402 被引量:8
标识
DOI:10.1080/13658816.2022.2119477
摘要

The popularity of ride-hailing platforms has significantly improved travel efficiency by providing convenient and personalized transportation services. Designing an effective ride-hailing service generally needs to address two tasks: order matching that assigns orders to available vehicles and proactive vehicle repositioning that deploys idle vehicles to potentially high-demand regions. Recent studies have intensively utilized deep reinforcement learning to solve the two tasks by learning an optimal dispatching strategy. However, most of them generate actions for the two tasks independently, neglecting the interactions between the two tasks and the communications among multiple drivers. To this end, this paper provides an approach based on multi-agent deep reinforcement learning where the two tasks are modeled as a unified Markov decision process, and the colossal state space and competition among drivers are addressed. Additionally, a modifiable agent-specific state representation is proposed to facilitate knowledge transferring and improve computing efficiency. We evaluate our approach on a public taxi order dataset collected in Chengdu, China, where a variable number of simulated vehicles are tested. Experimental results show that our approach outperforms seven existing baselines, reducing passenger rejection rate, driver idle time and improving total driver income.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
热爱科研的刘完成签到,获得积分10
2秒前
无辜的醉波完成签到,获得积分10
2秒前
大模型应助干姜采纳,获得10
3秒前
852应助隐形书白采纳,获得10
3秒前
3秒前
6秒前
ljs完成签到,获得积分10
7秒前
Yen发布了新的文献求助10
7秒前
今后应助vvvaee采纳,获得10
8秒前
8秒前
许起眸给许起眸的求助进行了留言
9秒前
楠LEE发布了新的文献求助10
10秒前
10秒前
梦回与她完成签到,获得积分10
13秒前
13秒前
FashionBoy应助一一采纳,获得30
14秒前
土豆教教主完成签到 ,获得积分10
15秒前
糕糕发布了新的文献求助10
15秒前
16秒前
JIE完成签到,获得积分10
17秒前
butaishao发布了新的文献求助10
17秒前
18秒前
wjj119完成签到,获得积分10
18秒前
18秒前
你的样子完成签到,获得积分10
19秒前
19秒前
聪慧小霜应助chen采纳,获得10
20秒前
深情安青应助chaochao采纳,获得10
20秒前
21秒前
21秒前
小二郎应助浩哥要strong采纳,获得10
21秒前
22秒前
22秒前
歪歪踢完成签到 ,获得积分10
23秒前
MIDANN完成签到,获得积分10
25秒前
25秒前
25秒前
25秒前
rainlqy完成签到,获得积分10
26秒前
Spark发布了新的文献求助10
26秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance 500
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3970008
求助须知:如何正确求助?哪些是违规求助? 3514711
关于积分的说明 11175563
捐赠科研通 3250077
什么是DOI,文献DOI怎么找? 1795198
邀请新用户注册赠送积分活动 875630
科研通“疑难数据库(出版商)”最低求助积分说明 804931