Factors motivating bicycling in Sydney: analysing crowdsourced data
Devising smarter strategic plans for more efficient modes of transport is fast becoming a priority for city planners and transport agencies. Having Sydney, Australia as case study, we analysed 6,932 GPS tracked cycling routes acquired from the RiderLog smart phone application to better understand in...
| Main Authors: | , , , |
|---|---|
| Format: | Conference Paper |
| Published: |
2017
|
| Online Access: | https://plea2017.net/ http://hdl.handle.net/20.500.11937/75823 |
| _version_ | 1848763575209820160 |
|---|---|
| author | Izadpanahi, Parisa Leao Z, Simone Lieske, Scott N Pettit, Chris J |
| author_facet | Izadpanahi, Parisa Leao Z, Simone Lieske, Scott N Pettit, Chris J |
| author_sort | Izadpanahi, Parisa |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Devising smarter strategic plans for more efficient modes of transport is fast becoming a priority for city planners and transport agencies. Having Sydney, Australia as case study, we analysed 6,932 GPS tracked cycling routes acquired from the RiderLog smart phone application to better understand interactions between bicyclists and the urban environment that encourage bicycling behaviour. Our approach used regression methods to identify a set of variables that can best predict the distance that cyclists ride. Gender, distance of the cycling track along parks and coastal areas, distance of the cycling track along commercial areas, percentage of the slope of the cycling track, and percentage of the type of cycling infrastructure (separate, shared, mixed, and no cycling lane) were considered as the potential predictor variables. Results indicate that although most of these variables could significantly predict the distance that cyclists ride, the distance of the cycling paths along parks and coastal areas and along commercial areas had the greatest contribution to the total R square. The findings of this paper provide important metrics which can inform city planners on how to improve attributes of the urban environment associated with bicycle tracks to motivate cyclists to ride longer distances. |
| first_indexed | 2025-11-14T11:05:38Z |
| format | Conference Paper |
| id | curtin-20.500.11937-75823 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:05:38Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-758232019-07-12T03:20:20Z Factors motivating bicycling in Sydney: analysing crowdsourced data Izadpanahi, Parisa Leao Z, Simone Lieske, Scott N Pettit, Chris J Devising smarter strategic plans for more efficient modes of transport is fast becoming a priority for city planners and transport agencies. Having Sydney, Australia as case study, we analysed 6,932 GPS tracked cycling routes acquired from the RiderLog smart phone application to better understand interactions between bicyclists and the urban environment that encourage bicycling behaviour. Our approach used regression methods to identify a set of variables that can best predict the distance that cyclists ride. Gender, distance of the cycling track along parks and coastal areas, distance of the cycling track along commercial areas, percentage of the slope of the cycling track, and percentage of the type of cycling infrastructure (separate, shared, mixed, and no cycling lane) were considered as the potential predictor variables. Results indicate that although most of these variables could significantly predict the distance that cyclists ride, the distance of the cycling paths along parks and coastal areas and along commercial areas had the greatest contribution to the total R square. The findings of this paper provide important metrics which can inform city planners on how to improve attributes of the urban environment associated with bicycle tracks to motivate cyclists to ride longer distances. 2017 Conference Paper http://hdl.handle.net/20.500.11937/75823 https://plea2017.net/ https://plea2017.net/#programmes-container unknown |
| spellingShingle | Izadpanahi, Parisa Leao Z, Simone Lieske, Scott N Pettit, Chris J Factors motivating bicycling in Sydney: analysing crowdsourced data |
| title | Factors motivating bicycling in Sydney: analysing crowdsourced data |
| title_full | Factors motivating bicycling in Sydney: analysing crowdsourced data |
| title_fullStr | Factors motivating bicycling in Sydney: analysing crowdsourced data |
| title_full_unstemmed | Factors motivating bicycling in Sydney: analysing crowdsourced data |
| title_short | Factors motivating bicycling in Sydney: analysing crowdsourced data |
| title_sort | factors motivating bicycling in sydney: analysing crowdsourced data |
| url | https://plea2017.net/ https://plea2017.net/ http://hdl.handle.net/20.500.11937/75823 |