Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda
Over the past years, bike-sharing programs have evolved and passed through various developmental stages since 1965, becoming a significant part of urban mobility worldwide. Researchers conducted numerous studies to examine the usage of bike-sharing systems. While earlier research has highlighted the...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Transport Economics and Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949899624000315 |
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| author | Jean Marie Vianney Ntamwiza Hannibal Bwire |
| author_facet | Jean Marie Vianney Ntamwiza Hannibal Bwire |
| author_sort | Jean Marie Vianney Ntamwiza |
| collection | DOAJ |
| description | Over the past years, bike-sharing programs have evolved and passed through various developmental stages since 1965, becoming a significant part of urban mobility worldwide. Researchers conducted numerous studies to examine the usage of bike-sharing systems. While earlier research has highlighted the benefits of bike-sharing, limited attention has been given to changes in docked bike-share systems and the use of machine learning algorithms to predict docked bike-sharing usage. This research investigated the effectiveness of machine learning models in predicting docked bike-sharing station usage in Kigali City. Descriptive statistics are analysed to reveal user characteristics by Gender, education, age, and occupation. The Random Forest Model effectively classified docked bike-sharing users and non-users, achieving a balanced accuracy of 84 %. With a sensitivity of 75 % and an F1 score of 82.5 %, it demonstrated strong user identification while balancing precision and recall and a positive predictive value of 91.6 %. The study also examined the factors influencing program usage. Results indicated that Gender positively affects docked bike-sharing, with a slightly higher impact from male users. Specific stations are popular among students, while others attract non-students. Corridor analysis revealed that the Central Business District positively impacts docked bike-sharing usage. Temporal and spatial trends indicate higher usage during school months, with younger riders dominating the age distribution of users. Demand also varies by season. This study provides valuable insights to support the optimisation of docked bike-sharing operations and to guide city planners in developing relevant infrastructure and policies. |
| format | Article |
| id | doaj-art-223424800dab4b91910d85485adda45e |
| institution | Kabale University |
| issn | 2949-8996 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Transport Economics and Management |
| spelling | doaj-art-223424800dab4b91910d85485adda45e2025-01-01T05:11:46ZengElsevierTransport Economics and Management2949-89962025-12-0133545Factors influencing docked bike-sharing usage in the City of Kigali, RwandaJean Marie Vianney Ntamwiza0Hannibal Bwire1Corresponding author.; University of Dar Es Salaam, Department of Transportation and Geotechnical Engineering, Dar es Salaam, TanzaniaUniversity of Dar Es Salaam, Department of Transportation and Geotechnical Engineering, Dar es Salaam, TanzaniaOver the past years, bike-sharing programs have evolved and passed through various developmental stages since 1965, becoming a significant part of urban mobility worldwide. Researchers conducted numerous studies to examine the usage of bike-sharing systems. While earlier research has highlighted the benefits of bike-sharing, limited attention has been given to changes in docked bike-share systems and the use of machine learning algorithms to predict docked bike-sharing usage. This research investigated the effectiveness of machine learning models in predicting docked bike-sharing station usage in Kigali City. Descriptive statistics are analysed to reveal user characteristics by Gender, education, age, and occupation. The Random Forest Model effectively classified docked bike-sharing users and non-users, achieving a balanced accuracy of 84 %. With a sensitivity of 75 % and an F1 score of 82.5 %, it demonstrated strong user identification while balancing precision and recall and a positive predictive value of 91.6 %. The study also examined the factors influencing program usage. Results indicated that Gender positively affects docked bike-sharing, with a slightly higher impact from male users. Specific stations are popular among students, while others attract non-students. Corridor analysis revealed that the Central Business District positively impacts docked bike-sharing usage. Temporal and spatial trends indicate higher usage during school months, with younger riders dominating the age distribution of users. Demand also varies by season. This study provides valuable insights to support the optimisation of docked bike-sharing operations and to guide city planners in developing relevant infrastructure and policies.http://www.sciencedirect.com/science/article/pii/S2949899624000315Docked bike-sharingMachine learning modelsPredictionUrban transportSpatial analysisWeather |
| spellingShingle | Jean Marie Vianney Ntamwiza Hannibal Bwire Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda Transport Economics and Management Docked bike-sharing Machine learning models Prediction Urban transport Spatial analysis Weather |
| title | Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda |
| title_full | Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda |
| title_fullStr | Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda |
| title_full_unstemmed | Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda |
| title_short | Factors influencing docked bike-sharing usage in the City of Kigali, Rwanda |
| title_sort | factors influencing docked bike sharing usage in the city of kigali rwanda |
| topic | Docked bike-sharing Machine learning models Prediction Urban transport Spatial analysis Weather |
| url | http://www.sciencedirect.com/science/article/pii/S2949899624000315 |
| work_keys_str_mv | AT jeanmarievianneyntamwiza factorsinfluencingdockedbikesharingusageinthecityofkigalirwanda AT hannibalbwire factorsinfluencingdockedbikesharingusageinthecityofkigalirwanda |