Identification of Hazardous Road Locations for Pedestrians

Abstract Hot zone methodology is promising in the identification of hazardous road locations. The key steps involve geo-validation of road crashes, segmentation of road network into basic spatial units (BSUs), calculation of actual crash intensity, definition of threshold value, and examination of spatial proximity of BSUs. This research applies the hot zone methodology to identify dangerous road locations for pedestrians during the study period of 2005 to 2007 in Kwun Tong District of Hong Kong. In particular, the crash intensity was calculated by a casualty-weighted method, which assigns different weights to different injury severity types. Two negative binomial regression models were employed to determine the threshold values. One could be treated as a base model which includes the length of BSU as the only explanatory variable. The other, regarded as a full model, introduces diverse environmental variables that might have influenced the distribution of pedestrian casualties.

[1]  Bhagwant Persaud,et al.  Calibration and Transferability of Accident Prediction Models for Urban Intersections , 2002 .

[2]  Wen Cheng,et al.  Experimental evaluation of hotspot identification methods. , 2005, Accident; analysis and prevention.

[3]  Piet Rietveld,et al.  The value of statistical life in road safety: a meta-analysis. , 2003, Accident; analysis and prevention.

[4]  Ted R. Miller,et al.  Variations between Countries in Values of Statistical Life , 2000 .

[5]  T. Bernhardsen Geographic Information Systems: An Introduction , 1999 .

[6]  Geert Wets,et al.  Identification and Ranking of Black Spots: Sensitivity Analysis , 2004 .

[7]  D. Graham,et al.  Spatial Variation in Road Pedestrian Casualties: The Role of Urban Scale, Density and Land-use Mix , 2003 .

[8]  Becky P.Y. Loo The Identification of Hazardous Road Locations: A Comparison of the Blacksite and Hot Zone Methodologies in Hong Kong , 2009 .

[9]  Rune Elvik,et al.  New Approach to Accident Analysis for Hazardous Road Locations , 2006 .

[10]  S. Baker,et al.  Injury research: Theories, methods, and approaches , 2012 .

[11]  Michel Mouchart,et al.  The local spatial autocorrelation and the kernel method for identifying black zones. A comparative approach. , 2003, Accident; analysis and prevention.

[12]  Haidong Zhong,et al.  Identification method of road hot zone based on GIS , 2011 .

[13]  Geert Wets,et al.  Identifying Hazardous Road Locations: Hot Spots versus Hot Zones , 2009, Trans. Comput. Sci..

[14]  Liping Fu,et al.  Bayesian multiple testing procedures for hotspot identification. , 2007, Accident; analysis and prevention.

[15]  Yingxu Wang,et al.  Transactions on Computational Science V , 2009, Lecture Notes in Computer Science.

[16]  B. Loo,et al.  Geographical Information Systems , 1997 .

[17]  N. Levine,et al.  Spatial analysis of Honolulu motor vehicle crashes: II. Zonal generators. , 1995, Accident; analysis and prevention.

[18]  F P Rivara,et al.  Urban-rural location and the risk of dying in a pedestrian-vehicle collision. , 1988, The Journal of trauma.

[19]  Becky P Y Loo,et al.  Validating crash locations for quantitative spatial analysis: a GIS-based approach. , 2006, Accident; analysis and prevention.

[20]  William R. Black,et al.  ACCIDENTS ON BELGIUM'S MOTORWAYS: A NETWORK AUTOCORRELATION ANALYSIS. , 1998 .