Logistic regression model of pedestrian injury risk

Nang Ngai Sze

Logistic regression model of pedestrian injury risk

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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
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
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

Cite this

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ER -

Logistic regression model of pedestrian injury risk

Abstract

Conference

ASJC Scopus subject areas

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