Smart Public Transport: A Bi-Objective Model for Maximizing Synchronizations and Minimizing Costs in Bus Timetables
Modern cities heavily rely on public transport systems to enhance citizen access to urban services and promote sustainability. To optimize public transport, intelligent computer-aided tools play a pivotal role in decision making. This article tackles the complex challenge of bus timetabling, specifi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/24/13032 |
| _version_ | 1797382142152409088 |
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| author | Claudio Risso Sergio Nesmachnow Diego Rossit |
| author_facet | Claudio Risso Sergio Nesmachnow Diego Rossit |
| author_sort | Claudio Risso |
| collection | DOAJ |
| description | Modern cities heavily rely on public transport systems to enhance citizen access to urban services and promote sustainability. To optimize public transport, intelligent computer-aided tools play a pivotal role in decision making. This article tackles the complex challenge of bus timetabling, specifically focusing on improving multi-leg trips or transfers. It introduces a novel multi-objective Mixed-Integer Programming Linear (MILP) model that concurrently maximizes passenger transfers and minimizes budgetary costs, while also adhering to the minimum required quality-of-service constraints for regular (non-multi-leg) trips, and an exact resolution approach based on the <i>ε</i>-constraint method to obtain a set of efficient solutions is used. The competitiveness of the model is validated via a computational experimentation performed over real-world scenarios from the public transportation system of Montevideo, Uruguay. The findings evinced that the MILP model was able to compute a set of Pareto efficient solutions that explore the tradeoff between the number of successful transfers and the cost of the system. Moreover, the best tradeoff solutions surpass the current city timetable, excelling in both the number of transfers and cost efficiency. |
| first_indexed | 2024-03-08T21:02:13Z |
| format | Article |
| id | doaj.art-08d7ccc231a34a579f58cf538f332fa9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T21:02:13Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-08d7ccc231a34a579f58cf538f332fa92023-12-22T13:50:29ZengMDPI AGApplied Sciences2076-34172023-12-0113241303210.3390/app132413032Smart Public Transport: A Bi-Objective Model for Maximizing Synchronizations and Minimizing Costs in Bus TimetablesClaudio Risso0Sergio Nesmachnow1Diego Rossit2Facultad de Ingeniería, Universidad de la República, Montevideo 11300, UruguayFacultad de Ingeniería, Universidad de la República, Montevideo 11300, UruguayINMABB, Engineering Department, Universidad Nacional del Sur-CONICET, Bahía Blanca B8000CPB, ArgentinaModern cities heavily rely on public transport systems to enhance citizen access to urban services and promote sustainability. To optimize public transport, intelligent computer-aided tools play a pivotal role in decision making. This article tackles the complex challenge of bus timetabling, specifically focusing on improving multi-leg trips or transfers. It introduces a novel multi-objective Mixed-Integer Programming Linear (MILP) model that concurrently maximizes passenger transfers and minimizes budgetary costs, while also adhering to the minimum required quality-of-service constraints for regular (non-multi-leg) trips, and an exact resolution approach based on the <i>ε</i>-constraint method to obtain a set of efficient solutions is used. The competitiveness of the model is validated via a computational experimentation performed over real-world scenarios from the public transportation system of Montevideo, Uruguay. The findings evinced that the MILP model was able to compute a set of Pareto efficient solutions that explore the tradeoff between the number of successful transfers and the cost of the system. Moreover, the best tradeoff solutions surpass the current city timetable, excelling in both the number of transfers and cost efficiency.https://www.mdpi.com/2076-3417/13/24/13032smart public transportbus timetabling problemmixed integer programmingbi-objective optimization |
| spellingShingle | Claudio Risso Sergio Nesmachnow Diego Rossit Smart Public Transport: A Bi-Objective Model for Maximizing Synchronizations and Minimizing Costs in Bus Timetables Applied Sciences smart public transport bus timetabling problem mixed integer programming bi-objective optimization |
| title | Smart Public Transport: A Bi-Objective Model for Maximizing Synchronizations and Minimizing Costs in Bus Timetables |
| title_full | Smart Public Transport: A Bi-Objective Model for Maximizing Synchronizations and Minimizing Costs in Bus Timetables |
| title_fullStr | Smart Public Transport: A Bi-Objective Model for Maximizing Synchronizations and Minimizing Costs in Bus Timetables |
| title_full_unstemmed | Smart Public Transport: A Bi-Objective Model for Maximizing Synchronizations and Minimizing Costs in Bus Timetables |
| title_short | Smart Public Transport: A Bi-Objective Model for Maximizing Synchronizations and Minimizing Costs in Bus Timetables |
| title_sort | smart public transport a bi objective model for maximizing synchronizations and minimizing costs in bus timetables |
| topic | smart public transport bus timetabling problem mixed integer programming bi-objective optimization |
| url | https://www.mdpi.com/2076-3417/13/24/13032 |
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