Spatio-temporal pattern of vulnerable road user’s collisions hot spots and related risk factors for injury severity in Tunisia

Abstract High risk of vulnerable road users (VRUs) injuries and fatalities have received higher interest nowadays in Tunisia. By using VRUs crash record (from January 1, 2001 to December 31, 2013), we describe the spatial pattern of VRUs collisions according to different temporal scales such as (a.m. vs p.m. rush hours VRUs collisions, working days vs non-working days VRUs collisions, daytime vs nighttime VRUs collisions) and investigate the influence of personal and environmental factors for VRUs injuries severity within the Center-East region in Tunisia. The empirical results are of great variety: spatial clustering pattern of each subtype of VRUs collisions according to temporal scale were clearly observed with the exception of daytime VRUs collisions, which shows a random tendency. All time-based subtypes of VRUs collisions also were found to be clustered along the national highways and regional highways especially in the regions of Sousse and Sfax. Results from VRUs severity model suggest that the degree of injury severity is higher for male than for female victim. The Tunisian VRUs are more likely to be involved in severe collision than non-Tunisian VRUs. Among driver contributory factors, the change of direction and hazardous overtaking increase the probability of sustaining fatal accidents compared to other driver contributory factors. The season factor shows that accident severity during the summer season is higher. From a policy view point, this kind of analysis can certainly help Tunisian public authorities to develop appropriate safety measures that can possibly reduce the number of VRUs injuries and fatalities.

[1]  Jean-Claude Thill,et al.  Local Indicators of Network-Constrained Clusters in Spatial Point Patterns , 2007 .

[2]  George Yannis,et al.  Pedestrian Risk Taking While Road Crossing: A Comparison of Observed and Declared Behaviour , 2016 .

[3]  L H Nitz,et al.  Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns. , 1995, Accident; analysis and prevention.

[4]  S. Washington,et al.  Statistical and Econometric Methods for Transportation Data Analysis , 2010 .

[5]  D. Hensher,et al.  A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes. , 2008, Accident; analysis and prevention.

[6]  Craig Caulfield,et al.  Spatial and temporal visualisation techniques for crash analysis. , 2011, Accident; analysis and prevention.

[7]  H. Jafari,et al.  Spatio-Temporal Patterns of Wildlife Road Mortality in Golestan National Park-North East of Iran , 2016 .

[8]  Shakil Mohammad Rifaat,et al.  Effect of street pattern on the severity of crashes involving vulnerable road users. , 2011, Accident; analysis and prevention.

[9]  Chih Wei Pai,et al.  Motorcyclist injury severity in angle crashes at T-junctions: Identifying significant factors and analysing what made motorists fail to yield to motorcycles , 2009 .

[10]  P. Nilsen,et al.  Uncovering evidence on community-based injury prevention: A review of programme effectiveness and factors influencing effectiveness , 2007, International journal of injury control and safety promotion.

[11]  Peter T. Savolainen,et al.  Examination of Factors Affecting Driver Injury Severity in Michigan's Single-Vehicle–Deer Crashes , 2008 .

[12]  Jean-Claude Thill,et al.  Comparison of planar and network K-functions in traffic accident analysis , 2004 .

[13]  J. Vissoci,et al.  The epidemiology of road traffic injury hotspots in Kigali, Rwanda from police data , 2016, BMC Public Health.

[14]  Afshin Shariat-Mohaymany,et al.  GIS-based method for detecting high-crash-risk road segments using network kernel density estimation , 2013, Geo spatial Inf. Sci..

[15]  Luis F. Miranda-Moreno,et al.  Estimating Potential Effect of Speed Limits, Built Environment, and Other Factors on Severity of Pedestrian and Cyclist Injuries in Crashes , 2011 .

[16]  R. Tay,et al.  A Multinomial Logit Model of Pedestrian–Vehicle Crash Severity , 2011 .

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

[18]  Teferi Abegaz,et al.  Road Traffic Deaths and Injuries Are Under-Reported in Ethiopia: A Capture-Recapture Method , 2014, PloS one.

[19]  A. Moudon,et al.  The risk of pedestrian injury and fatality in collisions with motor vehicles, a social ecological study of state routes and city streets in King County, Washington. , 2011, Accident; analysis and prevention.

[20]  Liping Fu,et al.  Identification of crash hotspots using kernel density estimation and kriging methods: a comparison , 2015 .

[21]  B. Ripley The Second-Order Analysis of Stationary Point Processes , 1976 .

[22]  Margaret M. Peden,et al.  World Report on Road Traffic Injury Prevention , 2004 .

[23]  Gudmundur F. Ulfarsson,et al.  A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model. , 2010, Accident; analysis and prevention.

[24]  Gudmundur F. Ulfarsson,et al.  Bicyclist injury severities in bicycle-motor vehicle accidents. , 2007, Accident; analysis and prevention.

[25]  Peter T. Savolainen,et al.  Comparison of Severity of Motorcyclist Injury by Crash Types , 2011 .

[26]  Tessa K Anderson,et al.  Kernel density estimation and K-means clustering to profile road accident hotspots. , 2009, Accident; analysis and prevention.

[27]  S. Milne,et al.  Applying GIS and statistical analysis to assess the correlation of human behaviour and ephemeral architectural features among Palaeo-Eskimo sites on Southern Baffin Island, Nunavut , 2017 .

[28]  N. Schuurman,et al.  Pedestrian Injury and Human Behaviour: Observing Road-Rule Violations at High-Incident Intersections , 2011, PloS one.

[29]  Richard Andrášik,et al.  Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation. , 2013, Accident; analysis and prevention.

[30]  Tomaž Tollazzi,et al.  Traffic safety analysis of powered two-wheelers (PTWs) in Slovenia. , 2012, Accident; analysis and prevention.

[31]  T. Miller,et al.  The employer costs of motor vehicle crashes , 2006, International journal of injury control and safety promotion.

[32]  Gholamali Shafabakhsh,et al.  GIS-based spatial analysis of urban traffic accidents: case study in Mashhad, Iran , 2017 .

[33]  Samiul Hasan,et al.  Exploring the determinants of pedestrian-vehicle crash severity in New York City. , 2013, Accident; analysis and prevention.

[34]  David W. S. Wong,et al.  A surface-based approach to measuring spatial segregation , 2007 .

[35]  D Chisholm,et al.  Distribution of road traffic deaths by road user group: a global comparison , 2009, Injury Prevention.

[36]  Bernard W. Silverman,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[37]  Silviu Marian Ciobanu,et al.  Hotspots and social background of urban traffic crashes: A case study in Cluj-Napoca (Romania). , 2016, Accident; analysis and prevention.

[38]  Gudmundur F. Ulfarsson,et al.  Analyzing fault in pedestrian-motor vehicle crashes in North Carolina. , 2010, Accident; analysis and prevention.

[39]  Peter T. Savolainen,et al.  Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations. , 2011, Accident; analysis and prevention.

[40]  Naveen Eluru,et al.  Evaluating alternate discrete outcome frameworks for modeling crash injury severity. , 2013, Accident; analysis and prevention.

[41]  Joanne M Wood,et al.  Using reflective clothing to enhance the conspicuity of bicyclists at night. , 2012, Accident; analysis and prevention.

[42]  N N Sze,et al.  Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes. , 2007, Accident; analysis and prevention.

[43]  Peter T. Savolainen,et al.  Driver Injury Severity Resulting from Single-Vehicle Crashes along Horizontal Curves on Rural Two-Lane Highways , 2009 .

[44]  T. Vos,et al.  Health transition in Iran toward chronic diseases based on results of Global Burden of Disease 2010. , 2014, Archives of Iranian medicine.

[45]  Shenjun Yao,et al.  The identification of traffic crash hot zones under the link-attribute and event-based approaches in a network-constrained environment , 2013, Comput. Environ. Urban Syst..

[46]  T. Anderson,et al.  Comparison of spatial methods for measuring road accident ‘hotspots’: a case study of London , 2007 .

[47]  Sekhar Somenahalli,et al.  Using GIS to Identify Pedestrian-Vehicle Crash Hot Spots and Unsafe Bus Stops , 2011 .

[48]  Samath D Dharmaratne,et al.  Under reporting of road traffic injuries in the district of Kandy, Sri Lanka , 2013, BMJ Open.

[49]  Monika Sester,et al.  Integration of GPS traces with road map , 2010, IWCTS '10.

[50]  Hoong Chor Chin,et al.  An analysis of motorcycle injury and vehicle damage severity using ordered probit models. , 2002, Journal of safety research.

[51]  Minho Park,et al.  Random Parameter Negative Binomial Model of Signalized Intersections , 2016 .

[52]  Jianping Wu,et al.  Spatial point analysis of road crashes in Shanghai: A GIS-based network kernel density method , 2011, 2011 19th International Conference on Geoinformatics.

[53]  Patricia Delhomme,et al.  Risk of crashing with a tram: Perceptions of pedestrians, cyclists, and motorists , 2012 .

[54]  Li Zhu,et al.  A GIS-based Bayesian approach for analyzing spatial–temporal patterns of intra-city motor vehicle crashes , 2007 .

[55]  P. Nilsen,et al.  What makes community based injury prevention work? In search of evidence of effectiveness , 2004, Injury Prevention.

[56]  B. Agbelie A comparative empirical analysis of statistical models for evaluating highway segment crash frequency , 2016 .

[57]  A P Jones,et al.  The application of K-function analysis to the geographical distribution of road traffic accident outcomes in Norfolk, England. , 1996, Social science & medicine.

[58]  F. Aloulou,et al.  Analyse microéconométrique des accidents routiers en Tunisie , 2016 .

[59]  David O'Sullivan,et al.  Geographic Information Analysis , 2002 .

[60]  Carolina Burnier,et al.  Severity of injury resulting from pedestrian-vehicle crashes: What can we learn from examining the built environment? , 2009 .

[61]  P. Lejeune,et al.  Spatio-temporal patterns of wildlife-vehicle collisions in a region with a high-density road network , 2013 .

[62]  Ibrahim Yilmaz,et al.  Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar. , 2008, Accident; analysis and prevention.

[63]  M. Raschke,et al.  Bicycle accidents - do we only see the tip of the iceberg? A prospective multi-centre study in a large German city combining medical and police data. , 2012, Injury.

[64]  John Steward,et al.  The Impact of Built Environment on Pedestrian Crashes and the Identification of Crash Clusters on an Urban University Campus , 2010, The western journal of emergency medicine.