Abstract
This study illustrates the data mining of historical crash records to determine the associations between the risk of mortality or severe injury and all possible contributory factors. Information on the demographic, crash, environment, and traffic characteristics of 73, 746 pedestrian casualties in Hong Kong during the period 1991-2004 was used to establish a predictive model with binary logistic regression. Taking into consideration the impact of temporal interruption on the measures of association, the temporal confounding and interaction effects were progressively incorporated to achieve a more effective predictive model. The goodness-of-fit of the model was assessed using the Hosmer-Lemeshow statistic and a logistic regression diagnostic.
Original language | English |
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Title of host publication | Proceedings of the 11th International Conference of Hong Kong Society for Transportation Studies |
Subtitle of host publication | Sustainable Transportation |
Pages | 147-155 |
Number of pages | 9 |
Publication status | Published - 1 Dec 2006 |
Externally published | Yes |
Event | 11th International Conference of Hong Kong Society for Transportation Studies: Sustainable Transportation - Kowloon, Hong Kong Duration: 9 Dec 2006 → 11 Dec 2006 |
Conference
Conference | 11th International Conference of Hong Kong Society for Transportation Studies: Sustainable Transportation |
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Country/Territory | Hong Kong |
City | Kowloon |
Period | 9/12/06 → 11/12/06 |
ASJC Scopus subject areas
- Automotive Engineering
- Civil and Structural Engineering
- Mechanical Engineering
- Safety, Risk, Reliability and Quality
- Transportation