Modelling the impact of road infrastructure on cycling moving speed
Cycling for transport is a sustainable alternative to using motorised vehicles for daily trips and is a key form of micromobility. Travel time is a critical factor influencing cycling route choice behaviour and uptake. Thus, it is important to understand the factors affecting cycling travel time and...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Journal of Cycling and Micromobility Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950105924000408 |
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| author | Afshin Jafari Dhirendra Singh Lucy Gunn Alan Both Billie Giles-Corti |
| author_facet | Afshin Jafari Dhirendra Singh Lucy Gunn Alan Both Billie Giles-Corti |
| author_sort | Afshin Jafari |
| collection | DOAJ |
| description | Cycling for transport is a sustainable alternative to using motorised vehicles for daily trips and is a key form of micromobility. Travel time is a critical factor influencing cycling route choice behaviour and uptake. Thus, it is important to understand the factors affecting cycling travel time and speed and their impact on cycling behaviour. In this study, an agent-based transport simulation model with heterogeneous cycling speeds was developed and used for Melbourne to study the impact of a hypothetical traffic signal optimisation intervention along six key cycling corridors. Linear regression and random forest models were used to identify factors affecting cycling speed, which informed the parameters of the agent-based model. Simulation outputs showed, on average, an increase of 4.1 % in the number of cyclists on the corridors, as existing cyclists chose to use these corridors, and an average reduction in cyclists’ moving travel time of 6.2 % for those using the intervention corridors (excluding time spent waiting at traffic signals). The findings provide insights into the effects of road attributes on cycling speed and behaviour, as well as the effectiveness of interventions aimed at reducing cycling delays. These insights are valuable for developing solutions to optimise urban infrastructure for micromobility, enhancing the efficiency and appeal of cycling as a viable transport option. |
| format | Article |
| id | doaj-art-d2eb42b881fb49a69a44611e9853bf74 |
| institution | DOAJ |
| issn | 2950-1059 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Cycling and Micromobility Research |
| spelling | doaj-art-d2eb42b881fb49a69a44611e9853bf742025-08-20T02:56:56ZengElsevierJournal of Cycling and Micromobility Research2950-10592025-03-01310004910.1016/j.jcmr.2024.100049Modelling the impact of road infrastructure on cycling moving speedAfshin Jafari0Dhirendra Singh1Lucy Gunn2Alan Both3Billie Giles-Corti4Centre for Urban Research, RMIT University, Melbourne, Australia; Correspondence to: RMIT University, GPO Box 2476, Melbourne, VIC 3001, AustraliaSchool of Computing Technologies, RMIT University, Melbourne, Australia; Data61, CSIRO, Melbourne, AustraliaCentre for Urban Research, RMIT University, Melbourne, AustraliaSchool of Geospatial science, RMIT University, Melbourne, AustraliaCentre for Urban Research, RMIT University, Melbourne, Australia; The University of Western Australia, Crawley, Western AustraliaCycling for transport is a sustainable alternative to using motorised vehicles for daily trips and is a key form of micromobility. Travel time is a critical factor influencing cycling route choice behaviour and uptake. Thus, it is important to understand the factors affecting cycling travel time and speed and their impact on cycling behaviour. In this study, an agent-based transport simulation model with heterogeneous cycling speeds was developed and used for Melbourne to study the impact of a hypothetical traffic signal optimisation intervention along six key cycling corridors. Linear regression and random forest models were used to identify factors affecting cycling speed, which informed the parameters of the agent-based model. Simulation outputs showed, on average, an increase of 4.1 % in the number of cyclists on the corridors, as existing cyclists chose to use these corridors, and an average reduction in cyclists’ moving travel time of 6.2 % for those using the intervention corridors (excluding time spent waiting at traffic signals). The findings provide insights into the effects of road attributes on cycling speed and behaviour, as well as the effectiveness of interventions aimed at reducing cycling delays. These insights are valuable for developing solutions to optimise urban infrastructure for micromobility, enhancing the efficiency and appeal of cycling as a viable transport option.http://www.sciencedirect.com/science/article/pii/S2950105924000408Cycling infrastructure optimizationMicromobility solutionsAgent-based simulationBicycle speedTraffic signals |
| spellingShingle | Afshin Jafari Dhirendra Singh Lucy Gunn Alan Both Billie Giles-Corti Modelling the impact of road infrastructure on cycling moving speed Journal of Cycling and Micromobility Research Cycling infrastructure optimization Micromobility solutions Agent-based simulation Bicycle speed Traffic signals |
| title | Modelling the impact of road infrastructure on cycling moving speed |
| title_full | Modelling the impact of road infrastructure on cycling moving speed |
| title_fullStr | Modelling the impact of road infrastructure on cycling moving speed |
| title_full_unstemmed | Modelling the impact of road infrastructure on cycling moving speed |
| title_short | Modelling the impact of road infrastructure on cycling moving speed |
| title_sort | modelling the impact of road infrastructure on cycling moving speed |
| topic | Cycling infrastructure optimization Micromobility solutions Agent-based simulation Bicycle speed Traffic signals |
| url | http://www.sciencedirect.com/science/article/pii/S2950105924000408 |
| work_keys_str_mv | AT afshinjafari modellingtheimpactofroadinfrastructureoncyclingmovingspeed AT dhirendrasingh modellingtheimpactofroadinfrastructureoncyclingmovingspeed AT lucygunn modellingtheimpactofroadinfrastructureoncyclingmovingspeed AT alanboth modellingtheimpactofroadinfrastructureoncyclingmovingspeed AT billiegilescorti modellingtheimpactofroadinfrastructureoncyclingmovingspeed |