{"title":"A network equilibrium model for integrated shared mobility services with ride-pooling","authors":"Xu Chen , Xuan Di","doi":"10.1016/j.trc.2024.104837","DOIUrl":null,"url":null,"abstract":"<div><p>With the growing popularity of transportation network companies (TNC), there remains a gap in network equilibrium models that adequately address the emergence of ride-pooling service that pools two orders into one trip. This challenge arises from the need to enumerate all possible combinations of origin–destination (OD) pooling and sequencing. This paper proposes a network equilibrium framework that integrates ride-hailing platforms’ decision on vehicle dispatching and driver–passenger matching on congested road networks. To facilitate the representation of vehicle and passenger OD flows and ride-pooling options, a layered OD graph is created encompassing ride-sourcing and ride-pooling services over OD nodes. To capture road congestion, a connection between the layered OD graph and road networks is established where the vehicle flows on the OD graph form source demands on road networks. Numerical examples are performed on a toy example and the Sioux Fall network to demonstrate our model and algorithm. The proposed equilibrium framework can efficiently assist policymakers and urban planners to evaluate the impact of TNCs on traffic congestion.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104837"},"PeriodicalIF":7.6000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part C-Emerging Technologies","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0968090X24003589","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
With the growing popularity of transportation network companies (TNC), there remains a gap in network equilibrium models that adequately address the emergence of ride-pooling service that pools two orders into one trip. This challenge arises from the need to enumerate all possible combinations of origin–destination (OD) pooling and sequencing. This paper proposes a network equilibrium framework that integrates ride-hailing platforms’ decision on vehicle dispatching and driver–passenger matching on congested road networks. To facilitate the representation of vehicle and passenger OD flows and ride-pooling options, a layered OD graph is created encompassing ride-sourcing and ride-pooling services over OD nodes. To capture road congestion, a connection between the layered OD graph and road networks is established where the vehicle flows on the OD graph form source demands on road networks. Numerical examples are performed on a toy example and the Sioux Fall network to demonstrate our model and algorithm. The proposed equilibrium framework can efficiently assist policymakers and urban planners to evaluate the impact of TNCs on traffic congestion.
随着运输网络公司(TNC)的日益普及,在网络均衡模型方面仍存在空白,无法充分解决将两个订单合并为一个行程的拼车服务的出现。这一挑战源于需要列举所有可能的出发地-目的地(OD)拼车和排序组合。本文提出了一个网络均衡框架,将打车平台在拥堵道路网络上的车辆调度决策和司机乘客匹配决策整合在一起。为便于表示车辆和乘客的 OD 流量以及合乘选择,本文创建了一个分层 OD 图,其中包括 OD 节点上的合乘服务和合乘服务。为了捕捉道路拥堵情况,在分层 OD 图和道路网络之间建立了连接,OD 图上的车辆流构成了道路网络的源需求。我们以一个玩具示例和苏克斯瀑布网络为例,演示了我们的模型和算法。所提出的平衡框架可以有效地帮助政策制定者和城市规划者评估跨国公司对交通拥堵的影响。
期刊介绍:
Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.