Logistic regression models for the nearest train station choice: A comparison of captive and non-captive stations
We usually assume that each commuter is an efficient traveller, which means they maximize trip utility. From a spatial optimization perspective, a commuter might therefore choose the nearest station to reach their destination. However, based on a survey at seven train stations in Perth, Western Aust...
| Main Authors: | , , , , |
|---|---|
| Format: | Journal Article |
| Published: |
Elsevier Ltd
2014
|
| Online Access: | http://hdl.handle.net/20.500.11937/35353 |
| _version_ | 1848754474709942272 |
|---|---|
| author | Shao, C. Xia, Jianhong (Cecilia) Lin, T. Goulias, K. Chen, C. |
| author_facet | Shao, C. Xia, Jianhong (Cecilia) Lin, T. Goulias, K. Chen, C. |
| author_sort | Shao, C. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We usually assume that each commuter is an efficient traveller, which means they maximize trip utility. From a spatial optimization perspective, a commuter might therefore choose the nearest station to reach their destination. However, based on a survey at seven train stations in Perth, Western Australia, only between 30 and 80 percent of commuters choose the nearest station to their origin. Many factors could affect this travel behaviour. From a logistic regression model, five factors were found to be significant (p-value <0.05), indicating that commuters are more likely to choose the non-nearest station for longer commutes, while traveling further away from origins and destination if the chosen stations are at, or near, the end of train lines (captive stations). If the chosen stations are along the train line (non-captive stations), longer distance, longer wait times and lower costs from the chosen station to a destination were found to be significant. The results of the study are important for public transport policy makers to understand transit choice behaviours. Therefore public transport policies such as adjustments of travel fees and improving station service and facilities, could be developed. |
| first_indexed | 2025-11-14T08:40:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-35353 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:40:59Z |
| publishDate | 2014 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-353532018-07-02T00:41:25Z Logistic regression models for the nearest train station choice: A comparison of captive and non-captive stations Shao, C. Xia, Jianhong (Cecilia) Lin, T. Goulias, K. Chen, C. We usually assume that each commuter is an efficient traveller, which means they maximize trip utility. From a spatial optimization perspective, a commuter might therefore choose the nearest station to reach their destination. However, based on a survey at seven train stations in Perth, Western Australia, only between 30 and 80 percent of commuters choose the nearest station to their origin. Many factors could affect this travel behaviour. From a logistic regression model, five factors were found to be significant (p-value <0.05), indicating that commuters are more likely to choose the non-nearest station for longer commutes, while traveling further away from origins and destination if the chosen stations are at, or near, the end of train lines (captive stations). If the chosen stations are along the train line (non-captive stations), longer distance, longer wait times and lower costs from the chosen station to a destination were found to be significant. The results of the study are important for public transport policy makers to understand transit choice behaviours. Therefore public transport policies such as adjustments of travel fees and improving station service and facilities, could be developed. 2014 Journal Article http://hdl.handle.net/20.500.11937/35353 10.1016/j.cstp.2015.06.002 Elsevier Ltd fulltext |
| spellingShingle | Shao, C. Xia, Jianhong (Cecilia) Lin, T. Goulias, K. Chen, C. Logistic regression models for the nearest train station choice: A comparison of captive and non-captive stations |
| title | Logistic regression models for the nearest train station choice: A comparison of captive and non-captive stations |
| title_full | Logistic regression models for the nearest train station choice: A comparison of captive and non-captive stations |
| title_fullStr | Logistic regression models for the nearest train station choice: A comparison of captive and non-captive stations |
| title_full_unstemmed | Logistic regression models for the nearest train station choice: A comparison of captive and non-captive stations |
| title_short | Logistic regression models for the nearest train station choice: A comparison of captive and non-captive stations |
| title_sort | logistic regression models for the nearest train station choice: a comparison of captive and non-captive stations |
| url | http://hdl.handle.net/20.500.11937/35353 |