Coordination of Feeder Bus Schedule with Train Service at Integrated Transport Hubs

Dou, Xueping; Gong, Xiaolin; Guo, Xiucheng; Tao, Tao

doi:10.3141/2648-12

Cited by 6 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

Optimization for Feeder Bus Route Model Design with Station Transfer

Cao¹,

Jiang²,

Wang³

2022

Sustainability

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The time of travel and transfer is shown in Equation (19). The total travel time of passengers is shown in Equation (20).…”

Section: Cost Analysismentioning

confidence: 99%

Optimization for Feeder Bus Route Model Design with Station Transfer

Cao¹,

Jiang²,

Wang³

2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…The issue of integrating trunk service and its feeder bus service has been considered in many studies [38,[40][41][42]. Kuah and Perl [43] conducted the first study in this regard by considering metro rail as a trunk service; however, they did not consider the coordination of feeder bus and mass transit systems.…”

Section: Coordinated Feeder Bus Transit Systems (Cfbt)mentioning

confidence: 99%

Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line

Nickkar,

Lee

2023

Algorithms

View full text Add to dashboard Cite

Recent advancements in technology have increased the potential for demand-responsive feeder transit services to enhance mobility in areas with limited public transit access. For long rail headways, feeder bus network algorithms are straightforward, as the maximum feeder service cycle time is determined by rail headway, and bus–train matching is unnecessary. However, for short rail headways, the algorithm must address both passenger–feeder-bus and feeder-bus–train matching. This study presents a simulated annealing (SA) algorithm for flexible feeder bus routing, accommodating short headway trunk lines and multiple bus relocations for various stations and trains. A 5 min headway rail trunk line example was utilized to test the algorithm. The algorithm effectively managed bus relocations when optimal routes were infeasible at specific stations. Additionally, the algorithm minimized total costs, accounting for vehicle operating expenses and passenger in-vehicle travel time costs, while considering multiple vehicle relocations.

show abstract

“…Given the demand for rail-to-bus transfer and considering the limited bus capacity, Dou et al ( 21 ) proposed a timetable design model to search for optimal terminal departure times of feeder buses which minimized waiting times for transfer passengers and operating costs for bus companies. This study extends the model proposed by Dou et al ( 21 ) by further incorporating vehicle-size effects on passenger and operator costs and explicitly considering passenger attitudes and behaviors toward waiting in a queue, as well as catering to both train and local passengers. The effects of the time-dependent demand on timetable coordination have also been discussed in recent studies.…”

Section: Relevant Studiesmentioning

confidence: 99%

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

Dou

Meng

2019

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

This study proposes a solution to the feeder bus timetabling problem, in which the terminal departure times and vehicle sizes are simultaneously determined based on the given transfer passengers and their arrival times at a bus terminal. The problem is formulated as a mixed integer non-linear programming (MINLP) model with the objective of minimizing the transfer waiting time of served passengers, the transfer failure cost of non-served passengers, and the operating costs of bus companies. In addition to train passengers who plan to transfer to buses, local passengers who intend to board buses are considered and treated as passengers from virtual trains in the proposed model. Passenger attitudes and behaviors toward the waiting queue caused by bus capacity constraints in peak hour demand conditions are explicitly embedded in the MINLP model. A hybrid artificial bee colony (ABC) algorithm is developed to solve the MINLP model. Various experiments are set up to account for the performance of the proposed model and solution algorithm.

show abstract

Coordination of Feeder Bus Schedule with Train Service at Integrated Transport Hubs

Cited by 6 publications

References 19 publications

Optimization for Feeder Bus Route Model Design with Station Transfer

Optimization for Feeder Bus Route Model Design with Station Transfer

Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line

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

Contact Info

Product

Resources

About