Optimal Transit Route Network Design Problem with Variable Transit Demand: Genetic Algorithm Approach

Fan, Wei; Machemehl, Randy B.

doi:10.1061/(asce)0733-947x(2006)132:1(40)

Cited by 248 publications

(150 citation statements)

References 19 publications

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“…Also other objectives have been considered such as capacity maximization, minimization of the fleet size, or energy conservation. It is worth mentioning that there are also approaches following multi-objective goals (see e.g., Baaj and Mahmassani 1991;Israeli and Ceder 1995;Bielli et al 2002;Chakroborty 2003;Fan and Machemehl 2006;Mauttone and Urquharta 2009). The procedures proposed for finding a solution to the TRNDP often stem from real-world (bus) applications and contain similar elements as the line planning heuristics, e.g., route generation algorithms where in every step new vertices are inserted (e.g., Baaj and Mahmassani 1991;Mauttone and Urquharta 2009), local improvements, genetic algorithms, and other metaheuristics.…”

Section: Heuristics For Various Line Planning Problemsmentioning

confidence: 99%

Line planning in public transportation: models and methods

2011

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The problem of defining suitable lines in a public transportation system (bus, railway, tram, or underground) is an important real-world problem that has also been well researched in theory. Driven by applications, it often lacks a clear description, but is rather stated in an informal way. This leads to a variety of different published line planning models. In this paper, we introduce some of the basic line planning models, identify their characteristics, and review literature on models, mathematical approaches, and algorithms for line planning. Moreover, we point out related topics as well as current and future directions of research.Keywords Line planning · Public transportation · Mathematical programming The line planning problem in public transportationGiven the increasing demand for mobility, an efficient organization of public (passenger) transportation becomes more and more important. This is reflected not only in practice but also by an increasing number of research papers dealing with the optimization of public transport. The goal of the optimization process is on the one hand, to offer a high quality of service for the passengers while, on the other hand, the costs for setting up and running the transit system should be small.As noted by many authors (see e.g., Ceder and Wilson 1986;Liebchen and Möhring 2007;Desaulniers and Hickman 2007), the planning process in public transportation consists of several consecutive planning phases. As shown in Fig. 1, the process starts A. Schöbel (B)

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Section: Heuristics For Various Line Planning Problemsmentioning

confidence: 99%

Line planning in public transportation: models and methods

2011

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“…Dung-Ying et al [21] proposed a solution model based on the use of quantum-inspired GA for dynamic CNDP. Fan et al [22] formulated the optimal Transit Route Network Design problem with variable transit demand and developed a GA application to solve this TNDP. Other authors [23][24][25] also developed significant works on this matter, showing that Gas are efficiently in solving Transportation Network Design problems.…”

Section: Evolutionary Computation In Transportation Network Design Prmentioning

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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“…Dell'Olio et al [15] proposed a bi-level mathematical programming model to micro-locate bus stops and optimize the frequencies considering that bus routes remain fixed, and the only decision variables were the micro-locations of the bus stops and the frequency determination. Fan and Machemehl [16,17] compared different solution metaheuristics, including simulated annealing, taboo search, and genetic algorithms. They used a similar model formulation; in the first and second models, they used fixed demand, whereas they used changed demand in the third model.…”

Section: Introduction 11 Backgroundmentioning

confidence: 99%

Optimization of Trip-end Networks and Ride Price for Express Coach Systems in the High-speed Rail Era

Yang

Jiannan

2017

PROMET

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Express coach (EC) lost a considerable share of passengers after high-speed rail (HSR) was implemented. This paper proposes a door-to-door operation mode for the EC system and builds a model to design an EC trip-end network in the origin city with the aim of maximizing the EC's daily operating profit. A case study is undertaken, and the results show that the operating profit of the EC system first increases and then decreases with the growth of the trip-end routes.In the HSR era, door-to-door operation can effectively guarantee the market share and operating profits of the EC.

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Optimal Transit Route Network Design Problem with Variable Transit Demand: Genetic Algorithm Approach

Cited by 248 publications

References 19 publications

Line planning in public transportation: models and methods

Line planning in public transportation: models and methods

A new genetic approach for transport network design and optimization

Optimization of Trip-end Networks and Ride Price for Express Coach Systems in the High-speed Rail Era

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