Integrated Model for Timetabling and Circulation Planning on an Urban Rail Transit Line: a Coupled Network-Based Flow Formulation

Shang, Pan; Yao, Yu; Yang, Liya; Meng, Lingyun; Mo, Pengli

doi:10.1007/s11067-021-09525-w

Cited by 11 publications

(3 citation statements)

References 68 publications

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“…Carosi et al [ 35 ] constructed a multi-commodity flow model and designed a mathematical heuristic algorithm to solve the integrated model of timetable and rolling stock schedule. Shang et al [ 36 ] constructed an integrated model coupling the space-time network of passenger travel and train running to minimize passenger travel time. Ibarra-Rojas et al [ 37 ] proposed a bi-objective model to deliver collaborative optimization between the rolling stock schedule and train timetable and thus minimize operating costs and passenger travel costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated optimization of urban rail transit line planning, timetabling and rolling stock scheduling

Tang

Zhang³

et al. 2023

PLoS ONE

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Urban rail transit train operation plan is a comprehensive production plan encompassing line planning, timetabling, and rolling stock scheduling. In order to solve the problem of infeasibility of the line plan and timetable because the number of rolling stocks could be only precisely considered in the rolling stock scheduling. An integrated optimization solution is proposed which considers the line plan, timetable, and rolling stock schedule. Candidate service routes are generated according to the layout of the turn-back stations. Considering the constraints of operation and passenger flow demand, an integer nonlinear programming model is established to minimize the cost of operation and passenger waiting time. The model complexity is analyzed and based on its decomposability a deterministic search algorithm is designed. Taking Chongqing Metro Line 3 in China as an example to verify the effectiveness of the proposed model and algorithm. Compared with the train operation plan based on manual experience and compiled by stages, the integrated optimization model can better improve the quality of train operation plan.

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Section: Literature Reviewmentioning

confidence: 99%

Integrated optimization of urban rail transit line planning, timetabling and rolling stock scheduling

Tang

Zhang³

et al. 2023

PLoS ONE

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“…Wang et al calculated the line density of the rail transit network based on the population density of a city, judged the rationality of the construction scale of its network lines, classified the urban space and the layout of the rail transit network lines, and analyzed the corresponding relationship between the two [ 11 ]. Shang et al studied the vulnerability of urban rail transit systems and the optimization of network resilience [ 12 ]. Based on the analysis of the reasonable scale of network lines and its influencing factors, Li et al took the traffic demand and the service level of network lines as the main influencing factors of the reasonable scale of urban rail transit and carried out a reasonable scale calculation [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

Optimization Algorithm of Urban Rail Transit Network Route Planning Using Deep Learning Technology

2022

Computational Intelligence and Neuroscience

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Under the present background, optimizing the existing urban rail transit network is the focus of urban rail transit construction at present. Based on DL, this paper constructs the optimization algorithm of urban rail transit network route planning. According to the current urban layout and urban planning, build a suitable rail transit network line form; according to the function, the types of urban rail transit stations are divided, and the optimization of urban rail transit network lines is realized. In addition, according to the K short path algorithm, this paper calculates the effective path between any stations of rail transit and, according to the model, allocates the passenger flow to each path. Experimental results show that the accuracy of real-time traffic flow prediction by this algorithm can reach 94.98%, which is about 9% higher than other methods. This algorithm can effectively optimize the route planning of urban rail transit network. This verifies the effectiveness of the route planning optimization algorithm proposed in this paper. Using the algorithm in this paper for line planning can get good real time, rationality, and optimality.

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“…e Unified eory of Acceptance and Use of Technology (UTAUT) was utilized by Jahanshahi et al [5] to investigate travellers" opinions and identify factors that influence the adoption of bike-share systems. In the context of the movement of travellers and public transit stations, some studies focused on regional analysis [6][7][8], route analysis [9][10][11], site analysis [5,10,12,13], ticketing channel [14][15][16], mode choice [17][18][19][20], and traveller characterization [21,22]. In particular, Kim et al [12] used ridership counts of selected intervals to classify the subway stations regarding their diurnal ridership patterns associated with land use.…”

Section: Introductionmentioning

confidence: 99%

RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study

Chen

Liang

Chang

et al. 2022

Journal of Advanced Transportation

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Public transportation users increase as the population grows. In Taipei, Taiwan, this tendency is observed by analyzing historical data from the Mass Rapid Transit (MRT) and economy-shared bicycle (known as YouBike) riders. While this trend exists, the Taipei City government promotes green transportation by providing discounts to users who transfer from MRT or bus to YouBike within a particular period. Therefore, this study focuses on analyzing the patterns of users in order to identify possible clusters. Clusters of customers can be considered fundamental and competitive factors for the Ministry of Transportation to encourage the use of green transportation and promote a sustainable environment. Based on big data smart card information, this paper proposes using the RFM and K-means clustering algorithm to analyze and construct mode-switching traveller profiles on MRT and YouBike riders. As a result, three distinct clusters of MRT-YouBike riders have been identified: potential, vulnerable, and loyal. There are also suggestions regarding the most profitable groups, which customers to focus on, and to whom give special offers or promotions to foster loyalty among transit travellers.

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Integrated Model for Timetabling and Circulation Planning on an Urban Rail Transit Line: a Coupled Network-Based Flow Formulation

Cited by 11 publications

References 68 publications

Integrated optimization of urban rail transit line planning, timetabling and rolling stock scheduling

Integrated optimization of urban rail transit line planning, timetabling and rolling stock scheduling

Optimization Algorithm of Urban Rail Transit Network Route Planning Using Deep Learning Technology

RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study

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