scholarly journals Optimal Design of the Feeder-Bus Network Based on the Transfer System

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Lianbo Deng ◽  
Wei Gao ◽  
Yanbing Fu ◽  
Wenliang Zhou
Keyword(s):  
Travel Demand ◽  
Network Design Problem ◽  
Urban Transit ◽  
Passenger Travel ◽  
Transportation Modes ◽  
Feeder Bus ◽  
Transfer Cost ◽  
Bus Network ◽  
Demand Patterns ◽  
Travel Expense

This paper studied the classic feeder-bus network design problem (FBNDP), which can be described as follows: for the passenger travel demand between rail stations and bus stops on a given urban transit network, it designs the optimal feeder bus routes and frequencies so as to minimize the passengers’ travel expense and the operator’s cost. We extended the demand pattern of M-to-1 in most existing researches to M-to-M. We comprehensively considered the passenger travel cost, which includes the waiting and riding cost on the bus, riding cost on rail, and transfer cost between these two transportation modes, and presented a new genetic algorithm that determines the optimal feeder-bus operating frequencies under strict constraint conditions. The numerical examples under different demand patterns have been experienced and analysed, which showed the robustness and efficiency of the presented algorithm. We also found that the distribution pattern of the travel demand has a significant influence on the feeder-bus network construction.

Design of Bus Routes: Methodology and the Santo Domingo Case

2002 ◽  
Vol 1791 (1) ◽  
pp. 35-43 ◽  
Author(s):  
Avishai Ceder ◽  
Oneximo Gonzalez ◽  
Hugo Gonzalez
Keyword(s):  
Network Design ◽  
Traffic Congestion ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Large Network ◽  
Santo Domingo ◽  
Urban Transit ◽  
Bus Network ◽  
Transit Routes

Growing traffic congestion, the importance of preserving the environment, and the problems of road safety are the main reasons to consider new initiatives worldwide in designing new urban transit routes. A need exists to develop a practical methodology for the construction of a new or improved network of bus routes along with intermodality considerations. An approach for the design of urban bus routes is presented with an example of designing new bus routes for the city of Santo Domingo in the Dominican Republic. Santo Domingo has major congestion, environmental, and safety problems. The presented approach involves a framework for the construction of operational objective functions for the bus-network-design problem. This framework takes into account passenger, operator, and community interests. The methodology combines the philosophy of mathematical programming approaches with decisionmaking techniques, so as to allow the user to select from a number of alternatives. The overall formulation is nonlinear and mixed-integer programming. The bus-network-design formulation used in the case study of Santo Domingo, a city with 3 million inhabitants, involved a large network of feasible bus routes subjected to the proposed method and resulted in 84 new bus routes. With other accompanied measures, the new bus routes will change the bus system image in Santo Domingo.

The Feeder-bus Network-design Problem

1989 ◽  
Vol 40 (8) ◽  
pp. 751-767 ◽  
Author(s):  
Geok Koon Kuah ◽  
Jossef Perl
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Network Design Problem ◽  
Feeder Bus ◽  
Bus Network

The Feeder-Bus Network-Design Problem

1989 ◽  
Vol 40 (8) ◽  
pp. 751
Author(s):  
Geok Koon Kuah ◽  
Jossef Perl
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Network Design Problem ◽  
Feeder Bus ◽  
Bus Network

APPLYING METAHEURISTICS TO FEEDER BUS NETWORK DESIGN PROBLEM

2004 ◽  
Vol 21 (04) ◽  
pp. 543-560 ◽  
Author(s):  
S. N. KUAN ◽  
H. L. ONG ◽  
K. M. NG
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Network Design ◽  
Computational Efficiency ◽  
Design Problem ◽  
Network Design Problem ◽  
Test Problems ◽  
Solution Quality ◽  
Feeder Bus ◽  
Bus Network

This paper proposes the design and analysis of two metaheuristics, simulated annealing (SA) and tabu search (TS), for solving the feeder bus network design problem. The results are compared to those published in the literature. A comparative study is also carried out on several test problems generated at random to evaluate the performance of these heuristics in terms of their computational efficiency and solution quality. Computational experiments have shown that TS is a more effective metaheuristic in solving the problem than SA.

Research on Optimization of Feeder Bus Network Joint for Urban Rail Transit

2013 ◽  
Vol 869-870 ◽  
pp. 256-259
Author(s):  
Ping Hong Wei ◽  
Jie Xiong ◽  
Meng Hao Zhang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Design Problem ◽  
Urban Rail Transit ◽  
Network Design Problem ◽  
Rail Transit ◽  
Urban Rail ◽  
Feeder Bus ◽  
Bus Network

The feeder bus network design problem is researched in this paper, aiming at connecting with the urban rail transit as well as possible. In solving the problem, a set of process in locating feeder bus routes is presented first, and then an optimization model of determining feeder bus network is established. The genetic algorithm (GA) is used in solving the problem and it is implemented in VC++ language. Finally, a case study is presented based on the existing rail transit line 3 in Dalian, with optimized the feeder bus routes obtained. The result also shows that the model and the algorithm are feasible in solving the problem.

Impact of Automated Vehicles on Optimal Demand-Responsive Feeder Transit Network Design

2021 ◽  
Vol 2 (1) ◽  
pp. 84-100
Author(s):  
Amirreza Nickkar ◽  
Young-Jae Lee ◽  
Seyedehsan Dadvar
Keyword(s):  
Economic Benefits ◽  
Operating Costs ◽  
Transit Systems ◽  
Travel Costs ◽  
Bus Routing ◽  
Routing Optimization ◽  
Passenger Travel ◽  
Feeder Bus ◽  
Feeder Transit ◽  
Transit Network Design

This article aims to examine the economic benefits of automating flexible demand responsive feeder transit systems using a developed feeder bus routing optimization algorithm. The objective function of the algorithm is to minimize total passengers' and operating costs of the system. The results showed that when unit operating costs decline, total operating costs, and total costs obviously decline. Furthermore, when unit operating costs decline, the average passenger travel distance and total passenger travel costs decline while the ratio of total operating costs per unit operating costs increases. That means if unit operating costs decrease, the portion of passenger travel costs in the total costs increases, and the optimization process tends to reduce passenger costs more while reducing total costs. Assuming that automation of the vehicles reduces the operating costs, it will reduce not only total operating costs and total costs, but also total passenger travel costs.

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