Smart Urban Transit Systems: From Integrated Framework to Interdisciplinary Perspective

Lu, Kai; Han, Baoming; Zhou, Xuesong

doi:10.1007/s40864-018-0080-x

Cited by 41 publications

(31 citation statements)

References 163 publications

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“…Both upper bound and lower bound of this problem are equal to 24, which increases by 4 compared to the optimal solution of the first example. Obviously, it is for the reason that the travel time of links (2, 3) and (4,5) and no extra vehicle or passenger waiting time is introduced.…”

Section: The Second Example With Extended Link Travel Timementioning

confidence: 99%

“…In order to test the influence of vehicle waiting time on the optimal vehicle routes and schedules, the departure time window of passenger A is delayed from [5,7] to [10,12]. Therefore, the vehicle will have to wait at node o 1 until passenger A gets ready to depart at time 10.…”

Section: The Third Example With Extra Vehicle Waiting Timementioning

confidence: 99%

“…When each transporting request is defined by determinate pickup and delivery points, the VRP becomes the vehicle routing problem with pickup and delivery (VRPPD). Practical applications of the VRPPD can be commonly found in urban rail transit management, to name a few, rail transit line planning [4,5], policy decision making [6,7], train operation management [8,9], train timetabling [10,11], and metro-based freight transportation [12]. In the emerging peer-to-peer ride-sharing service, a passenger can ask the driver to take him/her directly to the destination, and the passenger may also share this ride with one or more passengers.…”

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: The Second Example With Extended Link Travel Timementioning

confidence: 99%

Section: The Third Example With Extra Vehicle Waiting Timementioning

confidence: 99%

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

Self Cite

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“…The initiatives in the transportation system consist of various management strategies and effective restrictive measures which aim to reduce congestion and avoid accidents. The analysis of congestion in road networks includes macroscopic [1,2], medium [3,4] and microscopic [5,6] levels. The rearrangement of demand in the network once an element fails is a particular focus of some studies [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…In previous research, however, there is an overlook of the dynamic properties of traffic, such as the propagation of congestion [2]. Furthermore, quantitative research in the congestion propagation process is very limited [3,4].…”

Section: Introductionmentioning

confidence: 99%

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

Zeng

2018

Urban Rail Transit

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Simulating the congestion propagation of urban rail transit system is challenging, especially under oversaturated conditions. This paper presents a congestion propagation model based on SIR (susceptible, infected, recovered) epidemic model for capturing the congestion prorogation process through formalizing the propagation by a congestion susceptibility recovery process. In addition, as congestion propagation is the key parameter in the congestion propagation model, a model for calculating congestion propagation rate is constructed. A gray system model is also introduced to quantify the propagation rate under the joint effect of six influential factors: passenger flow, train headway, passenger transfer convenience, time of congestion occurring, initial congested station and station capacity. A numerical example is used to illustrate the congestion propagation process and to demonstrate the improvements after taking corresponding measures.

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Introduction

Schiewe¹

2020

Integrated Optimization in Public Transport Planning

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Smart Urban Transit Systems: From Integrated Framework to Interdisciplinary Perspective

Cited by 41 publications

References 163 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

Analyzing Congestion Propagation on Urban Rail Transit Oversaturated Conditions: A Framework Based on SIR Epidemic Model

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