Multimodal Feeder Network Design Problem: Ant Colony Optimization Approach

Mohaymany, Afshin Shariat; Gholami, Ali

doi:10.1061/(asce)te.1943-5436.0000110

Cited by 62 publications

(38 citation statements)

References 16 publications

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“…The literature review reveals that in some of the studies, it was assumed that this cost is calculated based only on the fleet size [14,19]. As explained in Section 3.2.2, the dwell time and bus slack time also have important roles in the time spent by personnel.…”

Section: Train Dwell User Cost (Cduit)mentioning

confidence: 99%

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Optimization of an Improved Intermodal Transit Model Equipped with Feeder Bus and Railway Systems Using Metaheuristics Approaches

et al. 2016

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Abstract:One of the serious concerns in network design is creating an efficient and appropriate network capable of efficiently migrating the passenger's mode of transportation from private to public. The main goal of this study is to present an improved model for combining the feeder bus network design system and the railway transit system while minimizing total cost. In this study, the imperialist competitive algorithm (ICA) and the water cycle algorithm (WCA) were employed to optimize feeder bus and railway services. The case study and input data were based on a real transit network in Petaling Jaya, Kuala Lumpur, Malaysia. Numerical results for the proposed model, including the optimal solution, statistical optimization results and the convergence rate, as well as comparisons are discussed in detail.

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Section: Train Dwell User Cost (Cduit)mentioning

confidence: 99%

“…It is assumed that there is an interrelationship among some of the costs, such as "social cost" and "feeder operating in-vehicle cost". Thus, based on previous studies [19,22,23], in order to simplify the proposed model, the social cost is assumed to be 20% of the "feeder operating in-vehicle cost" in this study.…”

Section: Social Costs (C S )mentioning

confidence: 99%

Optimization of an Improved Intermodal Transit Model Equipped with Feeder Bus and Railway Systems Using Metaheuristics Approaches

et al. 2016

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“…Personnel cost which includes the drivers and administrative costs is dependent on the fleet size, hourly pay, and insurance rate. These cost data come from Mohaymany and Gholami [13]. The cost is formulated as the product of the number of feeders and trains per round trip per each unit of time.…”

Section: Defining the Objective Functionmentioning

confidence: 99%

Optimal Coordination Strategy for an Integrated Multimodal Transit Feeder Network Design Considering Multiple Objectives

Almasi

Sadollah

et al. 2018

Sustainability

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Public transportation can have an efficient role ingainingtraveler satisfaction while decreasing operation costs through establishing an integrated public transit system. The main objective of this research is to propose an integrated multimodal transit model to design the best combination of both railway and feeder bus mode transit systems, while minimizing total cost. In this paper, we have proposed a strategy for designing transit networks that provide multimodal services at each stop, and for consecutively assigning optimum demand to the different feeder modes. Optimum transit networks have been achieved using single and multi-objective approaches via metaheuristic optimization algorithms, such as simulated annealing, genetic algorithms, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The used input data and study area were based on the real transit network of Petaling Jaya, located in Kuala Lumpur, Malaysia. Numerical results of the presented model, containing the statistical results, the optimum demand ratio, optimal solution, convergence rate, and comparisons among best solutions have been discussed in detail.

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“…In the seminal book of Box and Jenkins (77) a still widely used structured methodology is put forward to determine these terms and their weights from data. An early overview of ARIMA models applied to traffic forecasting can be found in (78), and since then many examples are available in the literature (4,43,48,(78)(79)(80)(81)(82). Additionally, many variations or additions on ARIMA have been proposed in literature, such as seasonal ARIMA (SARIMA) (4,83,84) in which periodic terms are added (which typically pertain to time of day, day of week, or other trends in traffic data); subset ARIMA models, which partition the time series in subsets with corresponding terms and components (81); variations on exponential smoothing (49,85); models in which more sophisticated unsupervised techniques are used in determining the weights and terms such as Kohonen ARIMA (9); models in which also exogenous inputs (from other time series) are incorporated (ARIMAX) (86).…”

Section: Linear Time Series and Regression Approachesmentioning

confidence: 99%

Artificial Intelligence Applications to Critical Transportation Issues

2012

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Forewords part of its mission, the Transportation Research Board's Artificial Intelligence and Advanced Computing Committee (ABJ70) serves as a technical forum on the application of artificial intelligence (AI) to transportation problems and disseminates information about AI applications that is credible and potentially useful to the transportation community. This is the second circular published by the committee. The first, Circular E-C113: Artificial Intelligence in Transportation: Information for Application, was published in 2007.This circular, authored by members of the committee, encompasses three parts. The first part describes AI applications in transportation, including the advantages and limitations of AI. The second part encompasses 12 articles in five chapters describing five general AI areas: traffic operations, travel demand modeling, transportation safety and security, public transportation, and infrastructure design and construction. The third part presents a few of the committee members' thoughts on the future of AI research in transportation applications.This circular is intended as an informational resource for transportation practitioners and managers about AI tools within these general areas. Each article details the types of problems to which the AI paradigm is best suited, its strengths and weaknesses, example applications, and guidelines for its application. -Shinya Kikuchi Chair, TRB Artificial Intelligence and Advanced Computing Committee PUBLISHER'S NOTEThis circular celebrates the contributions to TRB by Shinya Kikuchi, Charles E. Via Jr. Professor of Civil and Environmental Engineering and Director, Department of Civil and Environmental Engineering, Virginia Tech National Capital Region, who after six years is completing his term as Chair of the Artificial Intelligence and Advanced Computing Applications Committee.* This circular is the second in the series on artificial intelligence (AI) applications in transportation prepared under Prof. Kikuchi's leadership.During his chairmanship, he has spearheaded the dissemination of AI knowledge through these circulars as well as through regular workshops at TRB Annual Meetings. Prof. Kikuchi has served on nine TRB committees, always making his mark by empowering his committee members to act and to push new ideas out to the TRB community and beyond. He has consistently drawn a high level of participation in TRB activities from researchers and practitioners around the globe. The subcommittee was assisted by Kakan Chandra Dey, graduate student at Clemson University, South Carolina. The image on the cover was designed by Parth Bhavsar, graduate student at Clemson University; a portion of the cover image was taken from the National Institute of Neurological Disorders and Stroke website (http://www.ninds.nih.gov/disorders/brain_basics/know_your_brain.htm). Contents Difference Between Artificial Intelligence and Traditional MethodsHENK VAN ZUYLEN Delft University of Technologyhe domain of artificial intelligence (AI) is wide. Originally AI wa...

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Multimodal Feeder Network Design Problem: Ant Colony Optimization Approach

Cited by 62 publications

References 16 publications

Optimization of an Improved Intermodal Transit Model Equipped with Feeder Bus and Railway Systems Using Metaheuristics Approaches

Optimization of an Improved Intermodal Transit Model Equipped with Feeder Bus and Railway Systems Using Metaheuristics Approaches

Optimal Coordination Strategy for an Integrated Multimodal Transit Feeder Network Design Considering Multiple Objectives

Artificial Intelligence Applications to Critical Transportation Issues

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