Feeder Bus Timetable Design and Vehicle Size Setting in Peak Hour Demand Conditions

Dou, Xueping; Meng, Qiang

doi:10.1177/0361198119846462

Cited by 19 publications

(12 citation statements)

References 27 publications

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“…As for passenger travel cost, transfer factors are not considered in some studies on the optimization of feeder bus routes [19], and transfer factors are introduced in other studies, but some tend to reduce waiting time by optimizing walking distance, departure interval and timetable [10,20] so as to reduce transfer cost. Regarding the transfer factors, this paper considers the impact of station transfer and makes it clear that the generation of different route schemes will change the transfer choice, and the transfer at different stations will make the routes of passengers more diversified, resulting in different travel costs.…”

Section: Discussionmentioning

confidence: 99%

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Optimization for Feeder Bus Route Model Design with Station Transfer

Cao¹,

Jiang²,

Wang³

2022

Sustainability

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To fully take the advantages of conventional bus and subway, and to maximize the overall feeder efficiency of the public transport system, the topic of feeder bus route optimization is studied in this paper. Considering the origin destination demand of passenger flow between subway stations and bus stations and transfer characteristics, the objective function is established with the minimum sum of bus operation cost and passenger travel cost. Taking into account the integrity of the feeder bus route, the rationality of the route, the route capacity and the station transfer factors, the constraints of the optimization model are constructed. Based on the idea of the genetic algorithm, the solution algorithm of the optimization model is developed. The genetic algorithm and enumeration algorithm are used to solve the optimization of the feeder bus route in this case, and the accuracy and efficiency of the solution are analyzed. The influence of the number of feeder bus routes on the system in the case network is compared and discussed. We compare and analyze the differences between the original bus network and the feeder bus network in terms of bus operation cost, passenger flow demand and total passenger travel cost. The research shows that the model and algorithm can find the approximate optimal solution of the feeder bus network scheme related to the subway through fewer iterations. The number of routes in the model has little impact on the whole feeder system, and the optimization scheme using five routes is effective and reasonable in this paper. Compared with the existing bus network, the optimization scheme has obvious advantages in improving the passenger-carrying rate, reducing the per capita travel cost and improving the overall operation efficiency of the system.

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Section: Discussionmentioning

confidence: 99%

“…The satisfactory route was obtained by fuzzy programming and the TOPSIS method. Dou et al [10] proposed a solution to the feeder bus timetabling problem. An MINLP model was developed to minimize the weighted sum of passenger cost and bus operating cost.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization for Feeder Bus Route Model Design with Station Transfer

Cao¹,

Jiang²,

Wang³

2022

Sustainability

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“…Recently, several papers consider the integration of TNT and VSP, such as Wu et al (2019) and Dou and Meng (2019). Wu et al (2019) propose a bi-level model for bus schedule coordination design, allowing passengers to modify routes multiple times.…”

Section: Vehicle Scheduling Problem (Vsp)mentioning

confidence: 99%

“…They develop a heuristic algorithm to solve this model and show that a more cost-effective schedule can be obtained by taking rerouting behaviour into consideration. Dou and Meng (2019) propose a mixed-integer program for a feeder bus timetabling problem to minimize transfer waiting, transfer failure cost and bus operational cost. This model can handle both transfer passengers and local passengers whose behavior is constrained by bus capacity during peak hours.…”

Section: Vehicle Scheduling Problem (Vsp)mentioning

confidence: 99%

An Optimization-and-Simulation Framework for Redesigning University Campus Bus System with Social Distancing

Gong-yu¹,

Fei²,

Jia³

et al. 2020

Preprint

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The outbreak of coronavirus disease 2019 (COVID-19) has led to significant challenges for schools, workplaces and communities to return to operations during the pandemic, while policymakers need to balance between individuals' safety and operational efficiency. In this paper, we present a mixed-integer programming model for redesigning routes and bus schedules for the University of Michigan (UM)'s campus bus system, to prepare for students' return in the 2020 Fall semester. To ensure less than 15-minute travel time for all routes and to enforce social distancing among passengers, we propose a hub-and-spoke design and utilize real data of student activities to identify hub locations and reduce the number of bus stops used in the new routes. The new bus routes, although using only 50% or even fewer seats in each bus, can still satisfy peak-hour demand in regular semesters at UM. We sample a variety of scenarios that cover variations of peak demand, social-distancing requirements, broken-down buses or no-shows of drivers, to demonstrate the system resiliency of the new routes and schedules via simulation. Our approach can be generalized to redesign public transit systems with social distancing requirement during the pandemic, to reduce passengers' infection risk.

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“…Considering the connection between bus and rail transit, Takamatsu and Taguchi [28] established an event activity network to give vehicle timetable and passenger behavior in the backward areas of public transport in Japan and explored the rationality of train and bus transfer with the constraint of passenger transfer waiting time. Dou and Meng [29], based on exploring the rationality of the transfer between the terminal bus and the railway station, taking the minimization of passenger transfer waiting time as the constraint, and considering the bus capacity and passenger queuing attitude, established an MINLP model to optimize the timetable. Zheng-Wu and Ming-Qun [30] built a two-stage coordinated optimization of the operation lines of the corresponding feeder bus system at multiple transfer points for the mixed demand including reservation demand and real-time demand.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization of Bus Line Schedule in Commuter Corridor Based on Bus IC Card Data

Han

Qiu

et al. 2022

Journal of Advanced Transportation

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The aim of this study was to explore the bus operating state of the city bus passenger corridor, taking the minimum bus operating cost and passenger travel cost as the objective function, taking passenger flow demand and operating income as the constraint, and considering the average speed change of the bus line in the bus corridor at different times. This paper proposes a dynamic optimization model of bus route schedule based on bus Integrated Circuit Card (IC Card) data. The optimization variable is the departure frequency of the candidate lines. To solve the model, a dynamic departure interval optimization method based on improved Genetic Algorithm (GA) was designed under different decision preferences. The method includes the calibration of generalized cost functions for passengers and bus companies and grasps the characteristics of bus operating speed changes and the design of departure strategies under different decision preferences. The validity and applicability of the proposed method are verified by a numerical example. We mainly carried out the following work: (1) Dynamic analysis of the time dimension of the bus departure interval takes into account the changes in passenger time characteristics during peak periods. (2) Seven schemes of weight ratio of passenger waiting time cost and bus operation cost were designed, and the departure intervals with different benefit orientations of passengers and operators were discussed, respectively, so as to select the corresponding departure schemes for decision makers under different decision preferences. The results show that (1) the total cost of the 7 different weighting schemes is lower than the actual value by 6.90% to 18.20%; (2) when decision makers need to bias the weight to the bus company, the weight ratio α : β between passengers and bus company is 0.25 : 0.75 which works best. The frequency of departures has been reduced by 6, and at the same time, the total optimized cost is reduced by 18.2%; (3) when decision makers need to bias the weight to the passengers, the weight ratio α : β between the passengers and bus company is 0.75 : 0.25 which works best. The frequency of departures has been increased by 19, and at the same time, the total optimized cost is reduced by 17.7%; and (4) when decision makers consider passengers and bus companies equally, the weight ratio α : β between passengers and bus companies is 0.5 : 0.5, the optimization cost is the closest to the actual cost, the optimization cost is reduced by 6.9%, and the frequency of departures has been increased by 5. The results show that the model in this paper provides a new idea for the information mining of bus routes in the research based on the bus IC Card data and provides an effective tool for the management of different operation decision preferences.

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Feeder Bus Timetable Design and Vehicle Size Setting in Peak Hour Demand Conditions

Cited by 19 publications

References 27 publications

Optimization for Feeder Bus Route Model Design with Station Transfer

Optimization for Feeder Bus Route Model Design with Station Transfer

An Optimization-and-Simulation Framework for Redesigning University Campus Bus System with Social Distancing

Dynamic Optimization of Bus Line Schedule in Commuter Corridor Based on Bus IC Card Data

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