A Timetable Optimization Model for Urban Rail Transit with Express/Local Mode

Zhang, Renjie; Yin, Shisong; Ye, Mao; Yang, Zhiqiang; He, Shanglu

doi:10.1155/2021/5589185

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Regarding time minimization, Bai et al (2019) focus on optimizing train headways at each station to reduce passenger waiting time and platform crowding using a genetic algorithm. Similarly, Zhang et al (2021) research a timetable optimization model for express/local modes to minimize total passenger waiting time at platforms, resulting in a 9.3% savings. Other related research includes a demand-driven timetable assisted by smart card-based automated fare collection systems (Sun et al , 2014) and train scheduling to minimize passenger waiting time (Niu et al , 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

A conceptual non-stop operation strategy for urban rail transit: a case study of São Paulo’s monorail line

Oliveira,

Zhang

2024

SRT

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Purpose The purpose of this study is to address the extended travel time caused by dwelling time at stations for passengers on traditional rail transit lines. To mitigate this issue, the authors propose the “Non-stop” design, which involves trains comprised of modular vehicles that can couple and uncouple from each other during operation, thereby eliminating dwelling time at stations.. Design/methodology/approach The main contributions of this paper are threefold: first, to introduce the concept of non-stop rail transit lines, which, to the best of the authors’ knowledge, has not been researched in the literature; second, to develop a framework for the operation schedule of such a line; and third, the author evaluate the potential of its implementation in terms of total passenger travel time. Findings The total travel time was reduced by 6% to 32.91%. The results show that the savings were more significant for long commutes and low train occupancy rates. Research limitations/implications The non-stop system can improve existing lines without the need for the construction of additional facilities, but it requires technological advances for rolling stock. Originality/value To eliminate dwelling time at stations, the authors present the “Non-stop” design, which is based on trains composed of locomotives that couple and uncouple from each other during operation, which to the best of the authors’ knowledge has not been researched in the literature.

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Section: Literature Reviewmentioning

confidence: 99%

A conceptual non-stop operation strategy for urban rail transit: a case study of São Paulo’s monorail line

Oliveira,

Zhang

2024

SRT

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“…While conducting the research about bus scheduling optimization, some researchers mentioned that the significance of scheduling optimization or the purpose of adding some optimization objectives is to improve passengers' travel satisfaction (Andrade-Michel et al, 2021;Zhang et al, 2021). However, what is the specific relationship between travel satisfaction and service attributes?…”

Section: Vehicle Scheduling In the Post Covid-19 Pandemicmentioning

confidence: 99%

Urban bus scheduling considering maximizing TWB under the fixed departure times: A case of Beijing

Peng,

Wang,

Yang

et al. 2023

Preprint

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Urban buses play a crucial role in urban transportation, and optimizing bus scheduling has emerged as a significant research area in transportation. However, existing studies have largely overlooked factors like travel satisfaction and subjective well-being when designing bus schedules. To bridge this gap and enhance passengers' well-being while promoting human-centric traffic management, this study employed a multiple linear regression model to establish a connection between passengers' perceived well-being and objective scheduling service attributes, such as perceived in-vehicle congestion, in-vehicle travel time, and waiting time at the station. By deriving a Travel Well-Being (TWB) index from these scheduling attributes, the study aimed to use it as a comprehensive optimization objective for bus timetable optimization. To validate the proposed optimization approach, a bus line in Beijing, China, was selected, and the results obtained using the Differential Evolution (DE) algorithm demonstrated that the scheduling model, incorporating well-being considerations, effectively increased the estimated well-being rate by 29.80% compared to the original timetable. This study contributes to establishing a human-centric and inclusive transportation system by integrating perceived subjective well-being into the optimization of bus timetables.

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“…Lee et al [8] classifed passengers according to starting and ending points and transfer choices, set up a coordination model of fast and local train modes with the goal of the shortest passenger travel time, designed a genetic algorithm, and obtained the optimal combination scheme of the fast and local train. Zhang et al [9] proposed a timetable optimization model aiming at minimizing the total waiting time of passengers and solved it by genetic algorithm. Tang and Xu [10] established a double-layer programming model for the train operation scheme of suburban rail transit lines and designed a hybrid algorithm combining G-SA and MSA.…”

Section: Introductionmentioning

confidence: 99%

Metro Train Stopping Scheme Decision Based on Multisource Data in Express-Local Train Mode

Li,

Wang,

Zhang

et al. 2024

Journal of Advanced Transportation

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The urban rail transit network has gradually realized grid operation with the increase in the coverage rate. Therefore, the stopping schemes in accordance with the trend of the passenger flow are more conducive to improving the attractiveness of the rail transit and improving the sharing rate of the urban public transit. Traditional data from a single source may not be sufficient to describe the overall trend of the passenger flow in a period of time, and the error is possible in the case of insufficient data. Based on the multisource data, the spatial weight function is introduced to fuse the point of interest data and real estate information data, from which one obtains the residential index and office index, and the cluster analysis is conducted to obtain the potential stop scheme. Then, the optimization model of the train operation plan is established, aiming at minimizing the passenger travel time and the generalized system cost, and is constrained by a series of driving conditions. Compared with the single data source, multisource data can better reflect passenger flow trends and land use characteristics. Compared with the traditional all-station stopping scheme, a reasonable setting of crossing stations and running express-local trains can better satisfy the demands of the passenger flow. Finally, the optimization of Changchun rail Transit Line 1 shows that the model can reduce the travel time of passengers and the operating cost of the rail transit company and improve the quality of service, so as to achieve a win-win situation between passengers and the rail transit company.

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A Timetable Optimization Model for Urban Rail Transit with Express/Local Mode

Cited by 7 publications

References 20 publications

A conceptual non-stop operation strategy for urban rail transit: a case study of São Paulo’s monorail line

A conceptual non-stop operation strategy for urban rail transit: a case study of São Paulo’s monorail line

Urban bus scheduling considering maximizing TWB under the fixed departure times: A case of Beijing

Metro Train Stopping Scheme Decision Based on Multisource Data in Express-Local Train Mode

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