Impact of Competition on Quality of Service in Demand Responsive Transit

Grootenboers, Ferdi; Weerdt, Mathijs de; Zargayouna, Mahdi

doi:10.1007/978-3-642-16178-0_12

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…The users rely on flexible services that provide almost “door‐to‐door” transportation in small vehicles, with the possibility of prebooking (see Bellini et al., ; Ferreira et al., ; Logan, ). DRT systems are nowadays mainly implemented as services for small groups of people (e.g., elderly or physically challenged; see Palmer et al., ; Cordeau and Laporte, ; Grootenboers et al., ). The literature on DRT is also very limited (for studies on the topic, see Uchimura et al., ; Brake et al., , ; Palmer et al., ; Mulley and Nelson, ; and for planning multileg journeys with fixed‐route and demand‐responsive passenger transportation services, see Horn, ).…”

Section: Introductionmentioning

confidence: 99%

A simulation study of an on‐demand transportation system

Archetti

Speranza

Weyland

2017

Int Trans Operational Res

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In this paper we present the results of a simulation study aimed at assessing an on-demand transportation system. The on-demand system uses minibuses that have neither fixed itineraries nor fixed stops. The minibuses are dynamically routed to accommodate the requests received by the users. To use the on-demand service, users communicate, close to their desired departure time, the origin and destination of the trip. They accept the service if the estimated arrival time at destination fulfills their service level threshold. In the simulation users may decide whether to walk, to use a standard bus, to call the on-demand service, and, if none of these options is satisfactory, to use a private car. We consider different scenarios to assess the potential benefits of the introduction of an on-demand service. We also analyze the scalability and responsiveness of the service. The results suggest that an on-demand system may be able to satisfy a large portion of user transportation requests and may be put beside standard transportation systems in order to provide a better transportation service to the users and substantially reduce the use of private cars.

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Section: Introductionmentioning

confidence: 99%

A simulation study of an on‐demand transportation system

Archetti

Speranza

Weyland

2017

Int Trans Operational Res

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“…[29] proposed a branch-and-cut algorithm to solve a realistic DARP with multiple trips and request types and a heterogeneous fleet of vehicles. [31] proposed an online optimization method for the dial-a-ride problem in a multi-company setting.…”

Section: Solution Methods For the Ride-sharing Problemmentioning

confidence: 99%

Space-time clustering-based method to optimize shareability in real-time ride-sharing

et al. 2022

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Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

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“…Online optimization (e.g. in [19]) provides benefits of the exact optimization techniques while also reducing response times. The principle is to discretize the processing time into time intervals.…”

Section: Centralized Dispatching Protocolmentioning

confidence: 99%

Dispatching Requests for Agent-Based Online Vehicle Routing Problems with Time Windows

Zargayouna

Zeddini

2020

CIT. J. comput. inf. technol

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Vehicle routing problems are highly complex problems. The proposals to solve them traditionally concern the optimization of conventional criteria, such as the number of mobilized vehicles and the total costs. However, in online vehicle routing problems, the optimization of the response time to the connected travelers is at least as important as the optimization of the classical criteria. Multi-agent systems on the one hand and greedy insertion heuristics on the other are among the most promising approaches to this end. In this paper, we propose a multi-agent system coupled with a regret insertion heuristic. We focus on the real-time dispatching of the travelers' requests to the vehicles and its efficiency. A dispatching protocol determines which agents perform the computation to answer the travelers' requests. We evaluate three dispatching protocols: centralized, decentralized and hybrid. We compare them experimentally based on their response time to online travelers. Two computational types are implemented: a sequential implementation and a distributed implementation. The results show the superiority of the centralized dispatching protocol in the sequential implementation (32.80% improvement in average compared to the distributed dispatching protocol) and the superiority of the hybrid dispatching protocol in the distributed implementation (59.66% improvement in average, compared with the centralized dispatching protocol).

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Impact of Competition on Quality of Service in Demand Responsive Transit

Cited by 6 publications

References 9 publications

A simulation study of an on‐demand transportation system

A simulation study of an on‐demand transportation system

Space-time clustering-based method to optimize shareability in real-time ride-sharing

Dispatching Requests for Agent-Based Online Vehicle Routing Problems with Time Windows

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