Characterizing Situations of Dock Overload in Bicycle Sharing Stations
Bicycle sharing systems are becoming increasingly popular in cities around the world as they are an inexpensive and sustainable means of transportation. Promoting the use of these systems substantially improves the quality of life in cities by reducing pollutant emissions and traffic congestion. In...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-12-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/8/12/2521 |
| _version_ | 1818313890104082432 |
|---|---|
| author | Luca Cagliero Tania Cerquitelli Silvia Chiusano Paolo Garza Giuseppe Ricupero Elena Baralis |
| author_facet | Luca Cagliero Tania Cerquitelli Silvia Chiusano Paolo Garza Giuseppe Ricupero Elena Baralis |
| author_sort | Luca Cagliero |
| collection | DOAJ |
| description | Bicycle sharing systems are becoming increasingly popular in cities around the world as they are an inexpensive and sustainable means of transportation. Promoting the use of these systems substantially improves the quality of life in cities by reducing pollutant emissions and traffic congestion. In these systems, bikes are made available for shared use to individuals on a short-term basis. They allow people to borrow a bike in one dock and return it to any other station with free docks belonging to the same system. The occupancy level of the stations can be constantly monitored. However, to achieve a satisfactory user experience, all the stations in the system must be neither overloaded nor empty when the user needs to access the station. The aim of this paper is to analyze occupancy level data acquired from real systems to determine situations of dock overload in multiple stations which could lead to service disruption. The proposed methodology relies on a pattern mining approach. A new pattern type called Occupancy Monitoring Pattern is proposed here to detect situations of dock overload in multiple stations. Since stations are geo-referenced and their occupancy levels are periodically monitored, occupancy patterns can be filtered and evaluated by taking into consideration both the spatial and temporal correlation of the acquired measurements. The results achieved on real data highlight the potential of the proposed methodology in supporting domain experts in their maintenance activities, such as periodic re-balancing of the occupancy levels of the stations, as well as in improving user experience by suggesting alternative stations in the nearby area. |
| first_indexed | 2024-12-13T08:40:56Z |
| format | Article |
| id | doaj.art-89eecf4464c740de984e831f493d728a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-13T08:40:56Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-89eecf4464c740de984e831f493d728a2022-12-21T23:53:32ZengMDPI AGApplied Sciences2076-34172018-12-01812252110.3390/app8122521app8122521Characterizing Situations of Dock Overload in Bicycle Sharing StationsLuca Cagliero0Tania Cerquitelli1Silvia Chiusano2Paolo Garza3Giuseppe Ricupero4Elena Baralis5Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, ItalyDipartimento Interateneo di Scienze, Progetto e Politiche del Territorio, Politecnico di Torino, Viale Pier Andrea Mattioli, 39, 10125 Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, ItalyDipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, ItalyBicycle sharing systems are becoming increasingly popular in cities around the world as they are an inexpensive and sustainable means of transportation. Promoting the use of these systems substantially improves the quality of life in cities by reducing pollutant emissions and traffic congestion. In these systems, bikes are made available for shared use to individuals on a short-term basis. They allow people to borrow a bike in one dock and return it to any other station with free docks belonging to the same system. The occupancy level of the stations can be constantly monitored. However, to achieve a satisfactory user experience, all the stations in the system must be neither overloaded nor empty when the user needs to access the station. The aim of this paper is to analyze occupancy level data acquired from real systems to determine situations of dock overload in multiple stations which could lead to service disruption. The proposed methodology relies on a pattern mining approach. A new pattern type called Occupancy Monitoring Pattern is proposed here to detect situations of dock overload in multiple stations. Since stations are geo-referenced and their occupancy levels are periodically monitored, occupancy patterns can be filtered and evaluated by taking into consideration both the spatial and temporal correlation of the acquired measurements. The results achieved on real data highlight the potential of the proposed methodology in supporting domain experts in their maintenance activities, such as periodic re-balancing of the occupancy levels of the stations, as well as in improving user experience by suggesting alternative stations in the nearby area.https://www.mdpi.com/2076-3417/8/12/2521bicycle sharing systemsmachine learningassociation rule mining |
| spellingShingle | Luca Cagliero Tania Cerquitelli Silvia Chiusano Paolo Garza Giuseppe Ricupero Elena Baralis Characterizing Situations of Dock Overload in Bicycle Sharing Stations Applied Sciences bicycle sharing systems machine learning association rule mining |
| title | Characterizing Situations of Dock Overload in Bicycle Sharing Stations |
| title_full | Characterizing Situations of Dock Overload in Bicycle Sharing Stations |
| title_fullStr | Characterizing Situations of Dock Overload in Bicycle Sharing Stations |
| title_full_unstemmed | Characterizing Situations of Dock Overload in Bicycle Sharing Stations |
| title_short | Characterizing Situations of Dock Overload in Bicycle Sharing Stations |
| title_sort | characterizing situations of dock overload in bicycle sharing stations |
| topic | bicycle sharing systems machine learning association rule mining |
| url | https://www.mdpi.com/2076-3417/8/12/2521 |
| work_keys_str_mv | AT lucacagliero characterizingsituationsofdockoverloadinbicyclesharingstations AT taniacerquitelli characterizingsituationsofdockoverloadinbicyclesharingstations AT silviachiusano characterizingsituationsofdockoverloadinbicyclesharingstations AT paologarza characterizingsituationsofdockoverloadinbicyclesharingstations AT giuseppericupero characterizingsituationsofdockoverloadinbicyclesharingstations AT elenabaralis characterizingsituationsofdockoverloadinbicyclesharingstations |