Jean Marie Vianney Ntamwiza
Department of Transportation and Geotechnical Engineering, University of Dar es Salaam, Dar es Salaam 35131, Tanzania
Hannibal Bwire
Department of Transportation and Geotechnical Engineering, University of Dar es Salaam, Dar es Salaam 35131, Tanzania
Alphonse Nkurunziza
Department of Civil, Environmental and Geomatics Engineering, University of Rwanda, Kigali P.O. Box 4285, Rwanda
Promoting sustainable mobility and understanding travel demand are critical for rapidly growing cities like Kigali. This research aims to address limitations of traditional transport models by integrating geospatial analysis to support multimodal planning and optimize bike-sharing infrastructure. The study combines the Four-Step Transport Model with Geographic Information Systems (GIS) to enhance spatial disaggregation and identify optimal bike-sharing station locations. It incorporates shortest-path analysis and accounts for topography, road networks, population density, and land use. A household survey of 1377 residents was conducted to validate the model output. High trip generation zones were found in Nyamirambo and Kinyinya, while Nyarugenge, Remera, and Kimironko emerged as strong trip attraction areas. Congestion hotspots were identified at the Muhima, Remera, and Nyabugogo intersections. GIS analysis revealed high biking potential in Kinyinya, Kimironko, and Gatsata, aligning with survey responses. The study proposes 187 new bike-sharing stations in high-priority congestion zones and integrates 19 existing stations to strengthen multimodal connectivity, along with a first and last mile solution. Additionally, 15 key employment and service zones covering 67 km were identified to support efficient travel routes. By reducing the need for petrol-engine vehicle rebalancing, the optimized bike-sharing network supports environmental sustainability in the city. The integration of GIS and transport modeling offers a scalable, evidence-based framework for active mobility planning in Kigali and other Sub-Saharan cities in similar conditions to Kigali city in Rwanda.
[1] Blondiau, T., Van Zeebroeck, B., Haubold, H., 2016. Economic Benefits of Increased Cycling. Transportation Research Procedia. 14, 2306–2313. DOI: https://doi.org/10.1016/j.trpro.2016.05.247
[2] Kou, Z., Wang, X., Chiu, S.F.A., et al., 2020. Quantifying greenhouse gas emissions reduction from bike share systems: a model considering real-world trips and transportation mode choice patterns. Resources, Conservation and Recycling. 153, 104534. DOI: https://doi.org/10.1016/j.resconrec.2019.104534
[3] Gao, J., Ma, S., Wang, L., et al., 2023. Does greenness bring more green travelling? Evidence from free-floating bike-sharing in Beijing. Journal of Transport Geography. 109, 103586. DOI: https://doi.org/10.1016/j.jtrangeo.2023.103586
[4] Mahajan, S., Argota Sánchez-Vaquerizo, J., 2024. Global comparison of urban bike-sharing accessibility across 40 cities. Scientific Reports. 14(1), 20493. DOI: https://doi.org/10.1038/s41598-024-70706-x
[5] Macioszek, E., Świerk, P., Kurek, A., 2020. The bike-sharing system as an element of enhancing sustainable mobility - A case study based on a city in Poland. Sustainability. 12(8), 3285. DOI: https://doi.org/10.3390/SU12083285
[6] Larouche, R., Stone, M., Buliung, R.N., et al., 2016. I would rather bike to school!: Profiling children who would prefer to cycle to school. Journal of Transport and Health. 3(3), 377–385. DOI: https://doi.org/10.1016/j.jth.2016.06.010
[7] Li, Q., Fuerst, F., Luca, D., 2023. Do shared E-bikes reduce urban carbon emissions? Journal of Transport Geography. 112, 103697. DOI: https://doi.org/10.1016/j.jtrangeo.2023.103697
[8] Nikitas, A., 2018. Understanding bike-sharing acceptability and expected usage patterns in the context of a small city novel to the concept: A story of Greek Drama. Transportation Research Part F: Traffic Psychology and Behaviour. 56, 306–321. DOI: https://doi.org/10.1016/j.trf.2018.04.022
[9] Laa, B., Emberger, G., 2020. Bike sharing: Regulatory options for conflicting interests – Case study Vienna. Transport Policy. 98, 148–157. DOI: https://doi.org/10.1016/j.tranpol.2020.03.009
[10] Donovan, J., 2019. The Four-Step Model: Step 4 – The Later Stages of Couple Treatment: Long Term Personality Development. In: Donovan, J. (ed.). Working with Attachment in Couples Therapy. Routledge: New York, NY, USA. pp. 124–143. DOI: https://doi.org/10.4324/9780429297816-7
[11] Howari, F.M., Ghrefat, H., 2021. Chapter 4-Geographic Information System: Spatial Data Structures, Models, and Case Studies. In: Mohamed, A.M.O., Paleologos, E.K., Howari, F. (eds.). Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering. Elsevier Inc.: Amsterdam, Netherlands. pp. 165–198. DOI: http://dx.doi.org/10.1016/B978-0-12-809582-9.00004-9
[12] Song, M., Wang, K., Zhang, Y., et al., 2020. Impact evaluation of bike-sharing on bicycling accessibility. Sustainability. 12(15), 6124. DOI: https://doi.org/10.3390/su12156124
[13] Desjardins, E., Higgins, C.D., Páez, A., 2022. Examining equity in accessibility to bike share: A balanced floating catchment area approach. Transportation Research Part D: Transport and Environment. 102, 103091. DOI: https://doi.org/10.1016/j.trd.2021.103091
[14] Jin, S.T., Sui, D.Z., 2024. Bikesharing and equity: A nationwide study of bikesharing accessibility in the U.S. Transportation Research Part A: Policy and Practice. 181, 103983. DOI: https://doi.org/10.1016/j.tra.2024.103983
[15] Qian, X., Jaller, M., 2021. Bikeshare destination choices and accessibility among disadvantaged communities. Transportation Research Part D: Transport and Environment. 91, 102686. DOI: https://doi.org/10.1016/j.trd.2020.102686
[16] Mix, R., Hurtubia, R., Raveau, S., 2022. Optimal location of bike-sharing stations: A built environment and accessibility approach. Transportation Research Part A: Policy and Practice. 160, 126–142. DOI: https://doi.org/10.1016/j.tra.2022.03.022
[17] Ou, Y., Bao, Z., Thomas Ng, S., et al., 2023. Estimating the effect of air quality on Bike-Sharing usage in Shanghai, China: An instrumental variable approach. Travel Behaviour and Society. 33, 100626. DOI: https://doi.org/10.1016/j.tbs.2023.100626
[18] Mendiate, C.J., Nkurunziza, A., Soria-Lara, J.A., et al., 2023. Exploring cyclists' travel behaviour in different attitudinal market segments: case study of Quelimane, Mozambique. Urban, Planning and Transport Research. 11(1), 2221088. DOI: https://doi.org/10.1080/21650020.2023.2221088
[19] Abdellaoui Alaoui, E.A., Koumetio Tekouabou, S.C., 2021. Intelligent management of bike sharing in smart cities using machine learning and Internet of Things. Sustainable Cities and Society. 67, 102702. DOI: https://doi.org/10.1016/j.scs.2020.102702
[20] Lu, W., Scott, D.M., Dalumpines, R., 2018. Understanding bike share cyclist route choice using GPS data: Comparing dominant routes and shortest paths. Journal of Transport Geography. 71, 172–181. DOI: https://doi.org/10.1016/j.jtrangeo.2018.07.012
[21] Chen, Q., Fu, C., Zhu, N., et al., 2023. A target-based optimization model for bike-sharing systems: From the perspective of service efficiency and equity. Transportation Research Part B: Methodological. 167, 235–260. DOI: https://doi.org/10.1016/j.trb.2022.12.002
[22] Hu, R., Zhang, Z., Ma, X., et al., 2021. Dynamic Rebalancing Optimization for Bike-Sharing System Using Priority-Based MOEA/D Algorithm. IEEE Access. 9, 27067–27084. DOI: https://doi.org/10.1109/ACCESS.2021.3058013
[23] Zhang, H., Song, X., Xia, T., et al., 2018. MAAs in bike-sharing: Smart phone GPS data-based layout optimization and emission reduction potential analysis. Energy Procedia. 152, 649–654. DOI: https://doi.org/10.1016/j.egypro.2018.09.225
[24] National Institute of Statistics of Rwanda, 2022. MAIN INDICATORS: 5th Rwanda Population and Housing Census (PHC). Available from: https://www.statistics.gov.rw/data-sources/censuses/Population-and-Housing-Census/fifth-population-and-housing-census-2022/main-indicators-5th-rwanda-population-and-housing-census-phc (cited 15 June 2024).
[25] Kigali City, 2022. About Kigali City. Available from: https://www.kigalicity.gov.rw/about/overview (cited 15 June 2024).
[26] The New Times, 2019. Is Kigali ready for more car-free zones? Available from: https://www.newtimes.co.rw/article/165164/News/is-kigali-ready-for-more-car-free-zones (cited 25 June 2021).
[27] Ministry of Infrastructure, Rwanda, 2020. Non-Motorized Transport Strategy. Available from: https://sustmob.org/STR/NMTStrategy_Rwanda_200402.pdf (cited 15 June 2024).
[28] Aifadopoulou, G., Tsaples, G., Salanova Grau, J.M., et al., 2020. Management of resource allocation on vehicle-sharing schemes: the case of Thessaloniki's bike-sharing system. Operational Research. 22(2), 1001–1016.
[29] Alzaman, C., Aljuneidi, T., Li, Z., 2023. Predicting Bike Usage and Optimising Operations at Repair Shops in Bike Sharing Systems. IEEE Access. 11, 32534–32547. DOI: https://doi.org/10.1109/ACCESS.2023.3250230
[30] Chen, J., Zhang, Y., Zhang, R., et al., 2019. Analyzing users' attitudes and behavior of free-floating bike sharing: An investigating of Nanjing. Transportation Research Procedia. 39, 634–645. DOI: https://doi.org/10.1016/j.trpro.2019.06.065
[31] Passmore, R., Watkins, K., Guensler, R., 2024. Using shortest path routing to assess cycling networks. Journal of Transport Geography. 117, 103864. DOI: https://doi.org/10.1016/j.jtrangeo.2024.103864
[32] Dai, S., Zhao, W., Wang, Y., et al., 2023. Assessing spatiotemporal bikeability using multi-source geospatial big data: A case study of Xiamen, China. International Journal of Applied Earth Observation and Geoinformation. 125, 103539. DOI: https://doi.org/10.1016/j.jag.2023.103539
[33] Molina-García, J., Castillo, I., Queralt, A., et al., 2012. Implementation of a bicycle-sharing program: An effective way of introducing cycling as mode of transport. Journal of Science and Medicine in Sport. 15(1), S72. DOI: https://doi.org/10.1016/j.jsams.2012.11.172
[34] Yu, L., Feng, T., Li, T., et al., 2023. Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations. Urban Rail Transit. 9(1), 57–71. DOI: https://doi.org/10.1007/s40864-022-00183-w
[35] Giner Fabregat, G., Fonseca i Casas, P., Rivero Martínez, A., 2025. Intelligent Optimization of Bike-Sharing Systems: Predictive Models and Algorithms for Equitable Bicycle Distribution in Barcelona. Sustainability. 17(10), 4316. DOI: https://doi.org/10.3390/su17104316
[36] Sa, K., Seo, J., Lee, S., 2020. Analysis of Origin-Destination Characteristics and the Shortest Travel Path of Public Bike Users in the Morning Peak-Hour Period - Focused on the 2017 Travel OD Data of Public Bike Sharing in Seoul, Korea. Journal of the Urban Design Institute of Korea Urban Design. 21(6), 105–120. DOI: https://doi.org/10.38195/judik.2020.12.21.6.105
[37] Mo, X., Liu, X., Chan, W.K.V., 2021. Modeling and optimization in resource sharing systems: Application to bike-sharing with unequal demands. Algorithms. 14(2), 47. DOI: https://doi.org/10.3390/a14020047
[38] Guo, T., Yang, J., He, L., et al., 2020. Emerging Technologies and Methods in Shared Mobility Systems Layout Optimization of Campus Bike-Sharing Parking Spots. Journal of Advanced Transportation. 2020(1), 8894119. DOI: https://doi.org/10.1155/2020/8894119
[39] Song, J., Li, B., Szeto, W.Y., et al., 2024. A station design problem in a bike-sharing system with both conventional and electric shared bikes considering bike users' roaming delay costs. Transportation Research Part E: Logistics and Transportation Review. 181, 103350. DOI: https://doi.org/10.1016/j.tre.2023.103350
[40] Leichenko, R., Taylor, C., 2024. Promoting rural sustainability transformations: Insights from U.S. bicycle route and trail studies. Journal of Rural Studies. 105, 103205. DOI: https://doi.org/10.1016/j.jrurstud.2024.103205
Copyright © 2025 Jean Marie Vianney Ntamwiza, Hannibal Bwire, Alphonse Nkurunziza
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
