Applying Metaheuristics to Feeder Bus Network Design Problem

Kuan, S. N.; Ong, H. L.; Ng, Kien Ming

doi:10.1142/s0217595904000382

Cited by 26 publications

(14 citation statements)

References 17 publications

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“…Otherwise, v i is equal to 1. Constraints (14) and (15) define the domains of the decision variables.…”

Section: Mathematical Formulationmentioning

confidence: 99%

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A Multi-Objective Programming Approach to Design Feeder Bus Route for High-Speed Rail Stations

Guo¹,

Song²,

He³

et al. 2019

Symmetry

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The quality of route design can greatly affect the operational efficiency of feeder bus service for high-speed rail stations. A bi-objective optimization formulation is established to consider the trade-off between two conflicting optimization objectives, namely maximizing the travel demand that can be served and minimizing the feeder bus route length. The Pareto optimal solutions of the discrete mathematical formulation are generated by the exact ε-constraint method. We test the proposed approach with a numerical example on an actual size scale. The results indicate that the computational efficiency of the solution approach is encouraging, and a series of route design plans and location stop plans are generated simultaneously in a short time. A numerical example also shows that as the passengers’ maximum acceptable walking distance increases, more travel demand can be served when the route length does not change much. Benefits brought by increasing feeder bus route length are analyzed and the robustness of obtained solutions is verified. The comparison of our approach and an existing approach is also presented to demonstrate that our approach can generate better solutions.

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“…Otherwise, v i is equal to 1. Constraints (14) and (15) define the domains of the decision variables.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…In other previous studies, the number and locations of bus stops are given as inputs, and bus stops are demand points [13][14][15][16][17][18][19][20][21]. This assumption is reasonable when feeder bus routes are designed considering existing stops or the planner' hope to adjust the existing feeder bus routes.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Programming Approach to Design Feeder Bus Route for High-Speed Rail Stations

Guo¹,

Song²,

He³

et al. 2019

Symmetry

View full text Add to dashboard Cite

show abstract

“…This kind of approaches is efficient for small size networks [26]. However, when it comes to a large real-world network, there is no guarantee to find an analytic solution [30]. In addition, in most cases, the formulation from a real-world network is NPhard [31] which is not feasible for mathematical methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In other words, mathematical methods and heuristic methods lack enough capabilities to afford complexity. Fortunately, with the rapid development of computing power, metaheuristic methods have been developed to handle complicated computation, such as ant colony optimization (ACO) [18], simulated annealing (SA), tabu search (TS) (Fan et al, 2004), and genetic algorithms (GAs) [20,21,30,36]. Compared with heuristic methods, the metaheuristic methods can steadily generate high-quality solutions within an acceptable time frame.…”

Section: Literature Reviewmentioning

confidence: 99%

An Agent-Based Model for Dispatching Real-Time Demand-Responsive Feeder Bus

Wei

et al. 2018

Mathematical Problems in Engineering

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This research proposed a feeder bus dispatching tool that reduces rides' effort to reach a feeder bus. The dispatching tool takes in real-time user specific request information and optimizes total cost accordingly (passenger access time cost and transit operation cost) by choosing the best pick-up locations and feeder buses' routes. The pick-up locations are then transmitted back to passengers along with GPS guidance. The tool fits well with the Advanced Traveler Information Services (ATIS) which is one of the six highpriority dynamic mobility application bundles currently being promoted by the United State Department of Transportation. The problem is formulated into a Mixed Integer Programming (MIP) model. For small networks, out-of-the-shelf commercial solvers could be used for finding the optimal solution. For large networks, this research developed a GA-based metaheuristic solver which generates reasonably good solutions in a much shorter time. The proposed tool is evaluated on a real-world network in the vicinity of Jiandingpo metro station in Chongqing, China. The results demonstrated that the proposed ATIS tool reduces both buses operation cost and passenger walking distance. It is also able to significantly bring down computation time from more than 1 hour to about 1 min without sacrificing too much on solution optimality.

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“…-Discrete Transportation Network Design Problem (DT-NDP): changes are made to the network's physical configuration and involve binary design variables such as addition of new links [4], the selection of the optimum configuration of one-way and two-way routes [5] or the bus network design problem [6]. -Continuous Transportation Network Design Problem (CT-NDP): changes are made only to the attributes of the existing network and involves continuous design variables such as optimal capacity enhancement for a subset of existing links [7,8], transit line frequencies [9], link tolls [10], etc.…”

Section: The Transportation Network Design Problem -Bi-level Optimizamentioning

confidence: 99%

A new genetic approach for transport network design and optimization

Dinu¹,

Bordea²

2011

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. This paper presents an improved Genetic Algorithm to solve the Transportation Network Design Problem (CTNDP) with interactions among different links. The CTNDP is formulated in an optimal design as a bi-level programming model. A key factor in the present approach is the combination of diploid based complex-encoding with meiosis specific features. The novel mutation operator proposed is another improvement that leads to a better robustness and convergence stability.The computational results obtained by comparing the performance of the proposed algorithm and other Genetic Algorithms for a test network demonstrates its better local searching ability, as well as its high efficiency.Finally, suggestions for further research and extensions are given.Key words: Genetic Algorithm, bi-level programming, Network Design Problem, complex-encoding. The Transportation Network Design Problem -general descriptionThe Transportation Network Design Problem (TNDP) involves optimal decisions in determining a set of design parameters for improving an existing transportation network in response to an increasing level of traffic demand. The general increase in flow level results in traffic congestion, delays, higher fuel and maintenance costs, air pollution and accidents. The improvements of a transportation network, such as expansion of the capacities of the existing congested links, addition or deletion of links, traffic signal control adjustment, are made in accordance with a system optimum while considering the travel and route choice behavior of network users. The system optimum usually represents the minimization of the total travel time and construction costs.The network user's decisions correspond to a set of nonlinear relations that are formulated as an independent mathematical programming problem.In fact, a transportation network improvement involves the interaction of two parties with own objectives: the network planner represented by the transportation system authority and the network users that use the provided services. The traffic authority tries to optimize some overall objectives in the network, while the network users try to minimize their travel times/costs or perceived travel times/costs.The decision variables of the network planner affect the route choice behavior of network users which is based on two equilibrium principles:-the deterministic user equilibrium (DUE) condition [1] wherein network users choose the route with the shortest travel time/ the lower travel cost and equilibrium is reached where no user can unilaterally change routes to improve his/her own travel time or cost; assumptions of DUE can be somewhat unrealistic because of the variations in network conditions, variations in demand and no perfect information available for network users; -the stochastic user equilibrium (SUE) condition [2] where no user can unilaterally change routes to improve his/her own perceived travel time or cost; SUE may reflect travelers' behavior more realistically than DUE. One can consider that DUE is a special...

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Applying Metaheuristics to Feeder Bus Network Design Problem

Cited by 26 publications

References 17 publications

A Multi-Objective Programming Approach to Design Feeder Bus Route for High-Speed Rail Stations

A Multi-Objective Programming Approach to Design Feeder Bus Route for High-Speed Rail Stations

An Agent-Based Model for Dispatching Real-Time Demand-Responsive Feeder Bus

A new genetic approach for transport network design and optimization

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