Optimal Design of Feeder-bus Network Related to Urban Rail Line based on Transfer System

Deng, Lianbo; Wei, Gao; Zhou, Wenliang; Lai, Tianzhen

doi:10.1016/j.sbspro.2013.08.267

Cited by 19 publications

(17 citation statements)

References 13 publications

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“…Since 72.8% of GHG emissions in the transportation sector are produced by road traffic [2], it is important to increase the attractiveness of rail transport. This can be partly achieved by promoting intermodality between trains and buses [3][4][5]. Other strategies for promoting the use of rail systems are to increase service quality [6][7][8], reliability [9][10][11], and competitiveness with personal cars, by optimising service schedules and operations [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Defining Reserve Times for Metro Systems: An Analytical Approach

D’Acierno

Botte

Gallo

et al. 2018

Journal of Advanced Transportation

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The aim of this paper is to provide an analytical approach for determining operational parameters for metro systems so as to support the planning and implementation of energy-saving strategies. Indeed, one of the main targets of train operating companies is to identify and implement suitable strategies for reducing energy consumption. For this purpose, researchers and practitioners have developed energy-efficient driving profiles with the aim of optimising train motion. However, as such profiles generally entail an increase in travel times, the operating parameters in the planned timetable need to be appropriately recalibrated. Against this background, this paper develops a suitable methodology for estimating reserve times which represent the main rate of extra time needed to put ecodriving strategies in place. Our proposal is to exploit layover times (i.e., times spent by a train at the terminus waiting for the next trip) for energy-saving purposes, keeping buffer times intact in order to preserve the flexibility and robustness of the timetable in case of delays. In order to show its feasibility, the approach was applied in the case of a real metro context, whose service frequency was duly taken into account. In particular, after stochastic analysis of the parameters involved for calibrating suitable buffer times, different operating schemes were simulated by analysing the relationship between layover times, number of convoys, and feasible headway values. Finally, some operation configurations are analysed in order to quantify the amount of energy that can be saved.

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Section: Introductionmentioning

confidence: 99%

Defining Reserve Times for Metro Systems: An Analytical Approach

D’Acierno

Botte

Gallo

et al. 2018

Journal of Advanced Transportation

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“…Most previous studies on FTS route design mainly included two categories of approaches [8]: continuous analytical and discrete optimization. In the analytical approaches, optimal relationships between route spacing, stop spacing, and frequency could be found with an idealized FTS network structure based on a uniformly distributed demand in the study area [9][10][11].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Further, these approaches also optimized the stop locations and the route structures, and their headways by considering the time-dependent [12], space-dependent [13], and time-and-space-dependent demands [14][15][16][17]. However, these analytical methods have significant limitations in practical applications of FTS route design [8], because the two key model inputs were idealized FTS network structures and the demand was uniformly distributed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In such a case, an operation of integrated pedestrian guidance (from demand points to selected stops) and transit routing (from pick-up places to transportation hubs) can find an optimal relation between the walking and riding time of residents and the operation efficiency of vehicles [1,6,7,34]. (3) Such integration of stop selection, transit routing, and frequency setting was a new research problem extending from an NP-hard vehicle route problem, and a novel heuristic algorithm was needed to efficiently resolve large-scale instances [8].…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal Integrated Model for Feeder Transit Route Design and Frequency-Setting Problem with Stop Selection

Wei

Liu

Sun

et al. 2020

Journal of Advanced Transportation

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This study proposed a mathematical model for designing a feeder transit service for improving the service quality and accessibility of transportation hubs (such as airport and rail station). The proposed model featured an integrated framework, which simultaneously guided passengers to reach their nearest stops to get on and off the bus, designed routes to transport passengers from these selected pick-up stops to the transportation hubs, and calculated their departure frequencies. In particular, the maximum walking distance, the upper and lower limits of route frequencies, and the load factor rate of each route were fully accounted for in this study. The main objective of the proposed model was to simultaneously minimize the total walking, riding time, and waiting time of all passengers. As this study explored an NP-hard problem, a two-stage genetic algorithm combining the Dijkstra search method was further developed to yield metaoptimal solutions to the model within an acceptable time. Finally, a test instance in Chongqing City, China, demonstrated that the proposed model was an effective tool to generate a pedestrian, route, and operation plan; it reduced the total travel time, compared with the traditional model.

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“…Chen et al [26] proposed a two-phase mode to cope with DRT FBNDP. Deng et al [27] proposed a model to solve the multi-level cost structure (passengers' cost and operators' cost) of M-to-M DRT. Pan et al [28] presented a bi-level model to serve the maximum number of passengers in the feeder transit system in the upper level and to obtain the optimal operational cost for transit operators in the lower level.…”

Section: Literaturementioning

confidence: 99%

Personalised and Coordinated Demand-Responsive Feeder Transit Service Design: A Genetic Algorithms Approach

et al. 2018

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The purpose of this work is to create an efficient optimization framework for demand-responsive feeder transit services to assign vehicles to cover all pickup locations to transport passengers to a rail station. The proposed methodology features passengers placing a personalized travel order involving the subway schedule chosen by passengers and windows of service time, etc. Moreover, synchronous transfer between the shuttle and feeder bus is fully accounted for in the problem. A mixed-integer linear programming model is formulated to minimize the total travel time for all passengers, which consists of ride-time for vehicles from the pickup locations to the rail station and wait-time for passengers taking the subway beforehand. Different from conventional methods, the proposed model benefits from using a real distribution of passenger demand aggregated from cellular data and travel time or the distance matrix obtained from an open GIS tool. A distributed genetic algorithm is further designed to obtain meta-optimal solutions in a reasonable amount of time. When applied to design a feeder bus system in Nanjing City, China, case study results reveal that the total travel time of the proposed model was reduced by 2.46% compared to the traditional model. Sensitivity analyses were also further performed to investigate the impact of the number of vehicles on the output. Finally, the difference in results of Cplex, standard GA, and the proposed algorithm were compared to prove the validity of the algorithm.

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Optimal Design of Feeder-bus Network Related to Urban Rail Line based on Transfer System

Cited by 19 publications

References 13 publications

Defining Reserve Times for Metro Systems: An Analytical Approach

Defining Reserve Times for Metro Systems: An Analytical Approach

Optimal Integrated Model for Feeder Transit Route Design and Frequency-Setting Problem with Stop Selection

Personalised and Coordinated Demand-Responsive Feeder Transit Service Design: A Genetic Algorithms Approach

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