Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing

Lu, Tong; Zhou, Leishan; Liu, Jiangtao; Zhou, Xuesong

doi:10.1016/j.trc.2017.09.022

Cited by 167 publications

(106 citation statements)

References 66 publications

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“…As shown in Fig. 1, Passenger 1 should be picked up from dummy node o 1 in time window [4,7] and dropped off at dummy node d 1 in time window [11,14], while Passenger 2 should be picked up from dummy node o 2 in time window [8,10] and dropped off at dummy node d 2 in time window [13,16]. Vehicle 1 also has High-speed train speed Train scheduling Zhou et al [22] Cumulative working hours in crew scheduling, cumulative running distance in electric multiple unit (EMU) maintenance scheduling…”

Section: Modeling Methodology Based On the Spacetime-state Network Rementioning

confidence: 99%

“…Due to the complexity of variables and constraints, it is difficult to seek optimal solutions for large-scale VRPs. The generalized VRP problem also has a number of rich applications on the urban transit and other related scheduling or logistics problems [14,15]. For example, the most featured one is the transit vehicle assignment and routing problem, which is usually studied as the generalized VRP problem.…”

Section: Introductionmentioning

confidence: 99%

“…A resource-space-time network was adapted by Lu et al [23] to consider time-dependent routing applications with energy resource considerations. As an alternative representation to the high-dimension network, a decomposition approach is considered by Tong et al [14] and Ruan et al [24] with two coupled subproblems, i.e., a space-time network-based shortest path problem and a general assignment problem, for customized bus applications. By extending the space-time networkbased time-dependent shortest path algorithms, this VRPLite package aims to solve the time-dependent and state-dependent shortest path problem.…”

Section: Introductionmentioning

confidence: 99%

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Open-source VRPLite Package for Vehicle Routing with Pickup and Delivery: A Path Finding Engine for Scheduled Transportation Systems

Zhou

Mahmoudi

et al. 2018

Urban Rail Transit

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Recently, automation, shared use, and electrification are viewed as the ''three revolutions'' in the future transportation sector, and the traditional scheduled public transit system will be greatly enhanced with flexible services and autonomous vehicle scheduling capabilities. Many emerging scheduled transportation applications include the fully automatic operation system in urban rail transit, joint line planning, and timetabling for high-speed rail as well as emerging self-driving vehicle dispatching. The vehicle routing problem (VRP) holds promise for seeking an optimal set of vehicle routes and schedules to meet customers' requirements and plays a vital role in optimizing services for feature scheduled transportation systems. Due to the difficulty of finding optimal solutions for large-scale instances, enormous research efforts have been dedicated to developing efficient algorithms, while our paper presents a unique perspective based on a timedependent and state-dependent path searching framework. An open-source and light-weight VRP with pickup and delivery with time windows (VRPPDTW) modeling package, namely VRPLite, has been developed in this research to provide a high-quality and computationally efficient solution engine for transportation on demand applications. This paper describes the space-time-state

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Section: Modeling Methodology Based On the Spacetime-state Network Rementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Open-source VRPLite Package for Vehicle Routing with Pickup and Delivery: A Path Finding Engine for Scheduled Transportation Systems

Zhou

Mahmoudi

et al. 2018

Urban Rail Transit

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“…Mahmoudi and Zhou [21] built the state-space-time network to model the vehicle routing problem with pickup and drop-off and with time windows (VRPPDTW). Based on the Lagrangian decomposition and space-time windows, Tong et al [22] developed a joint optimization approach for customized bus services. Wei et al [23] developed a set of integer programming and dynamic programming models to optimize simplified multi-vehicle trajectories.…”

Section: Literature Reviewmentioning

confidence: 99%

Open-Source Public Transportation Mobility Simulation Engine DTALite-S: A Discretized Space–Time Network-Based Modeling Framework for Bridging Multi-agent Simulation and Optimization

Pan

Shang

et al. 2019

Urban Rail Transit

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Recently, an open-source light-weight dynamic traffic assignment (DTA) package, namely DTALite, has been developed to allow a rapid utilization of advanced dynamic traffic analysis capabilities. Aiming to bridge the modeling gaps between multi-agent simulation and optimization in a multimodal environment, we further design and develop DTALite-S to simplify the traffic flow dynamic representation details in DTALite for future extensions. We hope to offer a unified modeling framework with inherently consistent space-time network representations for both optimization formulation and simulation process. This paper includes three major modeling components: (1) mathematic formulations to describe traffic and public transportation simulation problem on a spacetime network; (2) transportation transition dynamics involving multiple agents in the optimization process; (3) an alternating direction method of multipliers (ADMM)based modeling structure to link different features between multi-agent simulation and optimization used in transportation. This unified framework can be embedded in a Lagrangian relaxation method and a time-oriented sequential simulation procedure to handle many general applications. We carried out a case study by using this unified framework to simulate the passenger traveling process in Beijing subway network which contains 18 urban rail transit lines, 343 stations, and 52 transfer stations. Via the ADMM-based solution approach, queue lengths at platforms, in-vehicle congestion levels and absolute deviation of travel times are obtained within 1560 seconds.The case study indicate that the open-source DTALite-S integrates simulation and optimization procedure for complex dynamic transportation systems and can efficiently generate comprehensive space-time traveling status.

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“…With the help of interactive and intelligent information platforms (via smartphone applications or websites), various types of similar on-demand transit services gained increasing popularity worldwide, such as customized/subscription buses [4], dial-a-ride [13], personalized rapid transit [14], and shuttle buses [15]. The application of advanced planning and operational approaches, including intelligent dispatching and matching, flexible routing, and dynamic pricing, further makes the DRT an increasingly attractive alternative for personal vehicles and conventional transit systems [16,17]. Considering the continually increasing cost of on-demand Sustainability 2020, 12, 298 3 of 17 services, the modeling and estimation of the performance of DRT is a hot topic aimed at increasing the efficiency of the DRT system.…”

Section: Literature Reviewmentioning

confidence: 99%

An Analytical Model for the Many-to-One Demand Responsive Transit Systems

et al. 2019

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The demand-responsive transit (DRT) service is an emerging and flexible transit mode to enhance the mobility of the urban transit system by providing personalized services. Passengers can make advanced appointments through smartphone applications. In this paper, an analytical model is proposed for the many-to-one DRT system. The agency and user costs are approximated by closed-form expressions. The agency cost, which is also the operation cost, is approximated by the continuum approximation technique. A nearest-neighbor routing strategy is applied, whereby the vehicle always collects the nearest passenger waiting in the system. The Vickrey queueing theory is adopted as the basis for approximating each component of the user cost, which is composed of the out-of-vehicle and in-vehicle waiting times and schedule deviations, which also depend on the service quality of the DRT system. The results of the numerical experiment show that (1) the agency and user costs are influenced significantly by the demand density, and (2) the DRT operator cannot further decrease the operating cost by solely deploying larger vehicles.

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Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing

Cited by 167 publications

References 66 publications

Open-source VRPLite Package for Vehicle Routing with Pickup and Delivery: A Path Finding Engine for Scheduled Transportation Systems

Open-source VRPLite Package for Vehicle Routing with Pickup and Delivery: A Path Finding Engine for Scheduled Transportation Systems

Open-Source Public Transportation Mobility Simulation Engine DTALite-S: A Discretized Space–Time Network-Based Modeling Framework for Bridging Multi-agent Simulation and Optimization

An Analytical Model for the Many-to-One Demand Responsive Transit Systems

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