Bus-Stop Control Strategies Based on Fuzzy Rules for the Operation of a Public Transport System

Milla, Freddy; Sáez, Doris; Cortés, Cristián E.; Cipriano, Aldo

doi:10.1109/tits.2012.2188394

Cited by 34 publications

(21 citation statements)

References 24 publications

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“…Khoat and Bernard [3], Milla et al [4], Niu [5], and Chiraphadhanakul et al [6] developed the optimization model based on the main objective of maximizing passenger welfare. Studies in later years focused on the trade-off between the costs for passengers and transit agencies.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Does Operation Scheduling Make a Difference: Tapping the Potential of Optimized Design for Skipping-Stop Strategy in Reducing Bus Emissions

Chen

Han

et al. 2017

Sustainability

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Abstract:The idea of corporate social responsibility has promoted bus operation agencies to rethink how to provide not only efficient but also environmentally friendly services for residents. A study on the potential of using an optimized design of skip-stop services, one of the essential operational strategies in practice, to reduce emissions is conducted in this paper. The underlying scheduling problem is formulated as a nonlinear programming problem with the primary objective of optimizing the total costs for both passengers and operating agencies, as well as with the secondary objective of minimizing bus emissions. A solution method is developed to solve the problem. A real-world case of Route 16 in Beijing is studied, in which the optimal scheduling strategy that maximizes the cost savings and environmental benefits is determined. The costs and emissions of the proposed scheduling strategy are compared with the optimal scheduling with skip-stop services without considering bus emissions. The results show that the proposed scheduling strategy outperforms the other operating strategy with respect to operational costs and bus emissions. A sensitivity study is then conducted to investigate the impact of the fleet size in operations and passenger demand on the effectiveness of the proposed stop-skipping strategy considering bus emissions.

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Section: Literature Reviewmentioning

confidence: 99%

“…In the binary logit model, the travel choice probability of the AB-AB passenger flow can be expressed as Equations (1)- (4).…”

Section: Od Demand Estimationmentioning

confidence: 99%

Does Operation Scheduling Make a Difference: Tapping the Potential of Optimized Design for Skipping-Stop Strategy in Reducing Bus Emissions

Chen

Han

et al. 2017

Sustainability

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“…Effective solutions to this vehicle scheduling problem are vital for bus companies to reduce their operational cost and improve the quality of their service [5], [6].…”

Section: R Minmentioning

confidence: 99%

Vehicle Scheduling of an Urban Bus Line via an Improved Multiobjective Genetic Algorithm

Zuo

Chen

Tan

et al. 2014

IEEE Trans. Intell. Transport. Syst.

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It is complex and difficult to perform the vehicle scheduling of urban bus lines, which is important to reduce the operational cost and improve the quality of public transportation services. One has to assign vehicles to cover a set of trips contained in a timetable while minimizing multiple objectives that may conflict with each other. Existing approaches combine these objectives in a weighted fashion to form a single objective and then use a single-objective optimization approach to solve it. However, they can only produce one solution, and it is not easy to assign a proper weight for each objective to obtain a superior solution that can balance different objectives. In this paper, a methodology is presented to create a set of Pareto solutions for this problem. First, a set of candidate vehicle blocks is generated. Then, multiple block subsets are selected from this candidate set by an improved multiobjective genetic algorithm combined with a departure-time adjustment procedure to obtain multiple Pareto solutions. To encode a solution, we propose a coding scheme that has a relatively short coding length and low decoding complexity. This approach is applied to a real-world vehicle scheduling problem of a bus line in Nanjing, China. Experiments show that this approach is able to quickly produce satisfactory Pareto solutions that outperform the actually used experience-based solution.

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“…These methods have been applied to ill defined industrial processes, since these methods are usually based on experienced people who usually obtain good results, regardless of whether they receive imprecise information [10][11][12][13][14]23]. The methods have also been applied to Control of a Mobile Robot using Fuzzy Bee Colony Optimization Algorithm [2,8], Particle Swarm [3, 16-18, 21-24, 26, 42-46], Genetic Algorithms [11,15,29,47], Differential evolution [30] and Ant Colony Optimization [25,30,37,41]. The origin of these impreciseness can be related to a variation of time concerning the application of a control signal and the warning of its effect [2], and nonlinearities in the dynamics of the system or sensor degradation [21].…”

Section: Introductionmentioning

confidence: 99%

Chemical Reaction Algorithm for Type-2 Fuzzy Control Optimization in Mobile Robots

O¹,

Castillo²,

Soria³

2018

JAMRIS

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In this work the optimization process of the tracking and reactive controllers for a mobile robot are presented. The Chemical Reaction Algorithm (CRA) is used to find the optimal parameter values of the membership functions and rules for the reactive and tracking controllers. In this case, we are using five membership functions in each variable of the fuzzy controllers. The main goal of the reactive controller is aimed at providing the robot with the ability to avoid obstacles in its environment. The tests are performed on a benchmark maze problem, in which the goal is not necessarily to leave the maze, but rather that the robot avoids obstacles, in this case the walls, and penalizing for unwanted trajectories, such as cycles. The tracking controller's goal is for the robot to keep into to a certain path, this in order that the robot can learn to react to unknown environments. The optimization algorithm that was used is based on an abstraction of chemical reactions. To perform the simulation we use the "SimRobot" toolbox, the results of the tests are presented in a detailed fashion, and at the end we are presenting a comparison of results among the CRA, PSO and GA methods.

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Bus-Stop Control Strategies Based on Fuzzy Rules for the Operation of a Public Transport System

Cited by 34 publications

References 24 publications

Does Operation Scheduling Make a Difference: Tapping the Potential of Optimized Design for Skipping-Stop Strategy in Reducing Bus Emissions

Does Operation Scheduling Make a Difference: Tapping the Potential of Optimized Design for Skipping-Stop Strategy in Reducing Bus Emissions

Vehicle Scheduling of an Urban Bus Line via an Improved Multiobjective Genetic Algorithm

Chemical Reaction Algorithm for Type-2 Fuzzy Control Optimization in Mobile Robots

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