A Particle Swarm Optimization Algorithm for the Solution of the Transit Network Design Problem

Cipriani, Ernesto; Fusco, Gaetano; Patella, Sergio Maria; Petrelli, Marco

doi:10.3390/smartcities3020029

Cited by 14 publications

(7 citation statements)

References 37 publications

(34 reference statements)

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“…In terms of solving algorithms, metaheuristic algorithms are widely applied concerning that the transit network design problems are considered to be NP-hard given the nonconvex solution space [21]. Commonly used algorithms are genetic algorithms, artifcial bee colony algorithms, particle swarm optimization algorithms, and hybrid methods [27][28][29]. Metaheuristic algorithms combine the advantages of random search and neighborhood search and are characterized by fast convergence and wide application.…”

Section: Literature Reviewmentioning

confidence: 99%

Bus Route Design Problem for Rural Tourism Objectives: A Multiobjective Approach

Guo

et al. 2022

Journal of Advanced Transportation

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Rural tourism bus routes are an essential component of rural public transport systems, intending to serve tourist trips with passengers moving between critical regional transport nodes and tourist attractions. This paper presents a methodology for the optimal design of rural tourism bus routes by minimizing total travel costs for tourists and maximizing the total quality of tourism bus services. Road scenery, road design attributes, and route popularity elements are integrated into the evaluation of tourism bus service quality. The constraints for the bus route planning and tourism demand are taken into account to guarantee the rational design of rural tourism bus routes. A solution approach is put forward based on the initial solution set generation procedure and strengthens the elitist genetic algorithm. Finally, the bus network in a rural tourism destination of Nanjing is taken as the case study to validate the feasibility and efficiency of the proposed model.

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Section: Literature Reviewmentioning

confidence: 99%

Bus Route Design Problem for Rural Tourism Objectives: A Multiobjective Approach

Guo

et al. 2022

Journal of Advanced Transportation

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“…In essence, the core idea of particle swarm optimization is that the particles in the swarm complete the decision under the combined action of the information of themselves and their peers [4].…”

Section: Principle Of Particle Swarm Optimizationmentioning

confidence: 99%

English Classroom Teaching Evaluation System Based on Particle Swarm Optimization Algorithm

Hui¹,

Zhang²

2022

Proceedings of the 2022 2nd International Conference on Education, Information Management and Service Science (EIMSS 2022)

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Teaching evaluation system is complicated and affected by various factors. Teaching quality assessment is an important measure to ensure the quality of school teaching and meet the needs of society. The purpose of this paper is to design an English school evaluation system based on optimal particle swarm optimization. The computer has become an invaluable teaching tool, which combined with teaching evaluation can directly promote the various aspects of the educational course or programme. According to the current situation of teaching in colleges and universities, in order to meet the specific requirements of teaching quality and promote high-quality talents, the particle swarm optimization algorithm theory is introduced into the teaching evaluation system, and the evaluation index system is also established. Moreover, the optimization learning model of particle swarm optimization is tested. A total of 3000 teacher evaluation data files are used as training data for testing. Each item includes 10 ratings. The results show that expert and peer test results are within 0.2 error on 8 datasets. Among the teaching evaluation models, the evaluation system based on particle swarm optimization algorithm can better evaluate the teacher training model when it comes to the teaching evaluation and judgment of English teachers.

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“…Moreover, many researchers claimed that PSO is a viable method and its computational efficiency is better than GA and DE [14][15][16][17]. Also, there is a cluster of studies that used PSO in solving the BRT route optimization problem and the Transportation Network Design Problems (TNDP) in high-density urban areas [18][19][20][21]. Many studies focused on the bus stations' spacing in urban areas.…”

Section: Generating the New Population And Go Up For Evaluatingmentioning

confidence: 99%

Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

et al. 2021

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In this research, a feasible mechanism is developed to determine the optimum number of bus rapid transit (BRT) stations as well as their respective locations along the service corridor. To accomplish this, a mathematical model is developed and optimized by using three different evolutionary algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), and the results are compared. The total cost function is composed of two main costs namely the operator’s cost, i.e., related to costs on service provider’s end, and the user’s cost, i.e., related to costs on commuters’ end. A functional numerical example with the commuters’ demand is worked out by minimizing the cost function, which demonstrates the applicability of the framework. In our case study, PSO outclassed GA and DE on the basis of convergence rate. Since our work has proved the robustness of PSO as compared to GA and DE, we conducted our sensitivity analysis keeping PSO as our benchmark algorithm to study the influence of various parameters on the optimal cost. The computational experiments reveal that the optimal cost is substantially affected by the variations in the commuters’ demand, commuters’ walking speed, and value of the users’ access and in-vehicle time. On the contrary, the acceleration/deceleration delays at a bus station, bus operating cost, and headway have an inconsiderable impact on the optimal cost.

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A Particle Swarm Optimization Algorithm for the Solution of the Transit Network Design Problem

Cited by 14 publications

References 37 publications

Bus Route Design Problem for Rural Tourism Objectives: A Multiobjective Approach

Bus Route Design Problem for Rural Tourism Objectives: A Multiobjective Approach

English Classroom Teaching Evaluation System Based on Particle Swarm Optimization Algorithm

Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

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