Monitoring and controlling real-time bus services: a reinforcement learning procedure for eliminating bus bunching
Bus bunching is one of the main issues encountered in bus service operations. The headway (i.e., time between two successive buses) of high-frequency services operating on congested routes can be subject to significant variability with two or more buses arriving at the same bus stop and at the same...
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| Published in | Transportation research procedia (Online) Vol. 62; pp. 302 - 309 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2352-1465 2352-1457 2352-1465 |
| DOI | 10.1016/j.trpro.2022.02.038 |
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| Abstract | Bus bunching is one of the main issues encountered in bus service operations. The headway (i.e., time between two successive buses) of high-frequency services operating on congested routes can be subject to significant variability with two or more buses arriving at the same bus stop and at the same time (bus bunching). This affects the reliability of bus services and causes user frustration with the travel experience. Although public transport is encouraged as the most environment-friendly mass transit solution, bus bunching is undesirable given that it creates inefficiencies and can push passengers to use private transport.
The advances in information communication technologies (ICTs) offer new opportunities for transit operators to limit this effect, ensuring a more reliable service. The paper, taking advantage of the innovations in ICTs, proposes a machine learning-based procedure addressing the issues causing bus bunching. The procedure is applied to a real test case with encouraging results. It opens the possibility that it may be incorporated in a decision support system to assist operators (drivers) in taking corrective actions throughout the day, improving bus service operations. |
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| AbstractList | Bus bunching is one of the main issues encountered in bus service operations. The headway (i.e., time between two successive buses) of high-frequency services operating on congested routes can be subject to significant variability with two or more buses arriving at the same bus stop and at the same time (bus bunching). This affects the reliability of bus services and causes user frustration with the travel experience. Although public transport is encouraged as the most environment-friendly mass transit solution, bus bunching is undesirable given that it creates inefficiencies and can push passengers to use private transport.
The advances in information communication technologies (ICTs) offer new opportunities for transit operators to limit this effect, ensuring a more reliable service. The paper, taking advantage of the innovations in ICTs, proposes a machine learning-based procedure addressing the issues causing bus bunching. The procedure is applied to a real test case with encouraging results. It opens the possibility that it may be incorporated in a decision support system to assist operators (drivers) in taking corrective actions throughout the day, improving bus service operations. |
| Author | Sassano, Mario Valentini, Alessio Comi, Antonio |
| Author_xml | – sequence: 1 givenname: Antonio surname: Comi fullname: Comi, Antonio organization: Department of Enterprise Engineering, University of Rome Tor Vergata, Rome, Italy – sequence: 2 givenname: Mario surname: Sassano fullname: Sassano, Mario organization: Dipartimento di Ingegneria Civile ed Ingegneria Informatica, University of Rome Tor Vergata, Rome, Italy – sequence: 3 givenname: Alessio surname: Valentini fullname: Valentini, Alessio email: alessio0valentini@gmail.com organization: Dipartimento di Ingegneria Civile ed Ingegneria Informatica, University of Rome Tor Vergata, Rome, Italy |
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| Cites_doi | 10.1109/ITSC.2016.7795538 10.1016/j.treng.2020.100025 10.1016/j.trpro.2020.02.109 10.3141/2647-04 10.1016/j.tranpol.2014.09.002 10.1016/j.ijtm.2005.02.001 10.1016/j.trb.2015.03.002 10.1016/j.trpro.2017.12.072 10.1016/j.trc.2017.11.023 10.1016/j.trc.2021.103087 10.3141/2216-06 10.3390/forecast2030017 10.1287/trsc.35.1.1.10143 10.1016/j.trc.2020.102661 |
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| Keywords | bus services operations transit services bus bunching operations control automated vehicle monitoring intelligent transport system machine learning service reliability |
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| SubjectTerms | automated vehicle monitoring bus bunching bus services operations intelligent transport system machine learning operations control service reliability transit services |
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| Title | Monitoring and controlling real-time bus services: a reinforcement learning procedure for eliminating bus bunching |
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