Abstract
Integrating cycling within daily routines by substituting car trips could improve sustainability and increase physical activity. We estimated the quantity of single-occupancy car trips that could be cycled in Finland during snow-free times of the year and their distribution among traveller segments based on Finnish 2016 national travel survey data. Hierarchical clustering applied to a distance matrix created by a random forest model classifying regular and irregular cyclists was used for segmentation.
Approximately 7% of car trips were deemed cyclable. These were distributed unevenly across eight traveller segments extracted from the data, which differed by travel behaviour, urbanisation, age, primary activity and population size. The results suggest that replacing car use with cycling in a routinised manner while meeting existing travel needs is not viable for most people. We highlight the urgency of improving the transportation system’s ability to consistently fulfil travel needs with cycling to facilitate large-scale modal shifting.
Approximately 7% of car trips were deemed cyclable. These were distributed unevenly across eight traveller segments extracted from the data, which differed by travel behaviour, urbanisation, age, primary activity and population size. The results suggest that replacing car use with cycling in a routinised manner while meeting existing travel needs is not viable for most people. We highlight the urgency of improving the transportation system’s ability to consistently fulfil travel needs with cycling to facilitate large-scale modal shifting.
Original language | English |
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Article number | 104655 |
Journal | Transportation Research Part D: Transport and Environment |
Volume | 141 |
DOIs | |
Publication status | Published - 1 Apr 2025 |
MoE publication type | A1 Journal article-refereed |
Funding
This work was supported by the Healthy Lifestyles to Boost Sustainable Growth (STYLE) project funded by the Strategic Research Council of Academy of Finland [grant numbers 320401 and 346608]. Support of the STYLE project consortium is gratefully acknowledged.
Keywords
- Active travel
- Clustering
- Cycling
- Machine learning
- Physical activity
- Random forest