Public Transportation Network Design and Frequency Setting: Pareto Optimality through Alternating-Objective Genetic Algorithms

Vlachopanagiotis, Theocharis; Grizos, Konstandinos; Georgiadis, Georgios; Politis, Ioannis

doi:10.3390/futuretransp1020015

Cited by 5 publications

(8 citation statements)

References 46 publications

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“…Since we are confronting a bi-objective optimization problem and these two objective functions are mutually conflicting, it is necessary to strike a balance between these two objectives. The Pareto-based GA is capable of illustrating the trade-off and compromise between different objectives [31,32]. Additionally, the Pareto-based GA possesses a strong global search capability, enabling us to discover a set of non-dominated solutions, presenting multiple feasible optimization schemes for selection, with flexibility [33][34][35].…”

Section: Algorithm Applicabilitymentioning

confidence: 99%

“…We compare the Pareto front generated by the two algorithms in Figure 16. In Figure 16, Pareto-based GA generates more Pareto solutions (50) than ECM (32) with about a 56% improvement. The number of delayed passengers (1687 pax) in the optimization obtained by ECM is larger than that by Pareto-based GA (1607 pax).…”

Section: Comparison Between Pareto-based Ga and Woamentioning

confidence: 99%

See 1 more Smart Citation

Timetable Rescheduling Using Skip-Stop Strategy for Sustainable Urban Rail Transit

Cao,

Wang,

Yang

et al. 2023

Sustainability

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Unanticipated events inevitably occur in daily urban rail transit operations, disturbing the scheduled timetable. Despite the mild delay, the busy operation system probably tends to worsen a larger disturbance and even lead to a knock-on disruption if no rescheduling is timely carried out. We propose a bi-objective mixed-integer linear programming model (MILP) that employs the skip-stop operation strategy to eliminate unscheduled delays. This model addresses two distinct, yet interconnected objectives. Firstly, it aims to minimize the difference between the plan and the actual operation. Secondly, it strives to minimize the number of left-behind passengers. In order to resolve this MILP problem, we devised a Pareto-based genetic algorithm (GA). Based on the case study, we certify the superior effectiveness with comparisons to the whale optimization algorithm and the epsilon constraint method. The outcomes affirm that our model has the potential to reduce the total delay time of the line by 44.52% at most compared with the traditional all-stop running adjustment model. The optimal scheme saved 6.08% of the total costs based on a trade-off between operators’ interests and passenger satisfaction.

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Section: Algorithm Applicabilitymentioning

confidence: 99%

Section: Comparison Between Pareto-based Ga and Woamentioning

confidence: 99%

Timetable Rescheduling Using Skip-Stop Strategy for Sustainable Urban Rail Transit

Cao,

Wang,

Yang

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Numerous optimization approaches are utilized in the existing works to enhance the transportation framework. Some of the existing optimization approaches are, genetic algorithm (GA) (Vlachopanagiotis (2021), ), simulated annealing (SA), Tabu search (TS) and Swarm intelligence (SI). These optimization approaches are proved their capability, effectiveness in optimal route generation in transportation network.…”

Section: Introductionmentioning

confidence: 99%

An Effective Demand based Optimal Route Generation in Transport System using DFCM and ABSO Approaches

Nayak

Chaubey

2023

Preprint

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The transportation network’s service quality is generally depends on providing demand based routing. Different existing approaches are focused to enhance the service quality of the transportation but them fails to satisfy the demand. This work presents an effective demand based objectives for optimal route generation in public transport network system. The proposed demand based optimal route generation process is described in subsequent stages. Initially, the passengers in each route are clustered using Distance based adaptive Fuzzy C-means clustering approach (DFCM) for collecting the passengers count in each stop. Here, the number of cluster members in each cluster is equivalent to the passenger’s count of each stop. After the clustering process, adaptive objectives based beetle swarm optimization (ABSO) approach based routing is performed based on the clustered data. Further, re-routing is performed based on the demand based objectives such as passenger’s count, comfort level of passengers, route distance and average travel time using ABSO approach. This ABSO approach provides the optimal routing based on these demand based objectives. The presented methodology is implemented in the MATLAB working platform. The dataset used for the analysis is Surat city transport historical data. The experimental results of the presented work is examined with the different existing approaches in terms of root mean square error (9.5%), mean error (0.254%), mean absolute error (0.3007%), correlation coefficient (0.8993), vehicle occupancy (85%) and accuracy (99.57%).

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“…(GA) [19,20], simulated annealing (SA), Tabu search (TS) and Swarm intelligence (SI) [21]. These optimization approaches are proved their capability, effectiveness in optimal route generation in transportation network.…”

Section: Introductionmentioning

confidence: 99%

“…. The speed update of the optimization approach is described in the subsequent condition (12) 18) and (19).…”

mentioning

confidence: 99%

An Effective Demand based Optimal Route Generation in Transport System using DFCM and ABSO Approaches

Nayak,

Chaubey

2022

IJACSA

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The transportation network service quality is generally depends on providing demand based routing. Different existing approaches are focused to enhance the service quality of the transportation but them fails to satisfy the demand. This work presents an effective demand based objectives for optimal route generation in public transport system. The importance of this work is providing demand based optimal routing for large city transportation. The proposed demand based optimal route generation process is described in subsequent stages. Initially the passengers in each route are clustered using Distance based adaptive Fuzzy C-means clustering approach (DFCM) for collecting the passengers count in each stop. Here the number of cluster members in each cluster is equivalent to the passenger count of each stop. After the clustering process, adaptive objectives based beetle swarm optimization (ABSO) approach based routing is performed with the clustered data. Then rerouting is performed based on the demand based objectives such as passenger's count, comfort level of passengers, route distance and average travel time using ABSO approach. This ABSO approach provides the optimal routing based on these demand based objectives. The presented methodology is implemented in the MATLAB working platform. The dataset used for the analysis is Surat city transport historical data. The experimental results of the presented work is examined with the different existing approaches in terms of root mean square error (9.5%), mean error (0.254%), mean absolute error (0.3007%), correlation coefficient (0.8993), vehicle occupancy (85%) and accuracy (99.57%).

show abstract

Public Transportation Network Design and Frequency Setting: Pareto Optimality through Alternating-Objective Genetic Algorithms

Cited by 5 publications

References 46 publications

Timetable Rescheduling Using Skip-Stop Strategy for Sustainable Urban Rail Transit

Timetable Rescheduling Using Skip-Stop Strategy for Sustainable Urban Rail Transit

An Effective Demand based Optimal Route Generation in Transport System using DFCM and ABSO Approaches

An Effective Demand based Optimal Route Generation in Transport System using DFCM and ABSO Approaches

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