How to Compare Different National Databases of HGV Accidents to Identify Issues for Safety Improvements

Salvatore Cafiso, Alessandro Di Graziano, Giuseppina Pappalardo


The objective of this paper is to present a methodological approach and a case study for an international comparison of accident data coming from different national databases. Safety levels and the characteristics of severe crashes involving heavy goods vehicles in different European countries (Italy, France, Germany, Great Britain and Spain) are analysed. Considering that all the countries involved have different inventory structures for the variables reported in their national accident databases, the taxonomy theory was used in order to create a comparable structure for the database used in the analysis. The taxonomy is non-exclusive and the codes are categorical, denoting the absence or presence of a certain feature. Based on the data available in each national database the five European Union databases of accidents involving heavy goods vehicles have been referenced to only one, composed of 11 items (casualty class, injury number and severity, location, light conditions, road conditions, junction, vehicle type, driver age, driver gender, accident type and manoeuvres), which capture common features of heavy goods vehicles accidents. A statistical analysis was carried out in order to highlight significant differences in the proportions of heavy goods vehicles crash categories.


accident data; heavy goods vehicle; database; taxonomy; statistical analysis; proportion method

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Broughton, J.; Lawton, B.; Walter, L.; Hoeglinger, S.; Angermann, A.; Weiss, V.; Yannis, G.; Evgenikos, P.; Bos, N.; Reurings, M. 2008. Heavy Goods Vehicles and Buses. European Road Safety Observatory Documentation. Traffic Safety Basic Facts 2008 [cited 15 January, 2011]. Available from Internet:

Bryce, T. 2005. The Benefits of a Data Taxonomy [cited 13 July, 2009]. Available from Internet:

Cafiso, S.; Di Graziano, A.; Pappalardo, G. 2013. Using the Delphi Method to Evaluate Opinions of Public Transport Managers on Bus Safety, Safety Science 57: 254–263.

Cafiso, S.; Di Graziano, A. 2012. Evaluation of the Effectiveness of ADAS in Reducing Multi-Vehicle Collisions, International Journal of Heavy Vehicle Systems 119(2): 188–206.

Cafiso, S.; La Cava, G.; Pappalardo, G. 2012. A Comparative Analysis of Powered Two Wheelers Crash Severity among Different Urban Areas, Procedia > Social and Behavioral Sciences 53: 891–900.

Donnell, E. T.; Porter, R. J.; Shankar, V. N. 2010. A Framework for Estimating the Safety Effects of Roadway Lighting at Intersections, Safety Science 48(10): 1436–1444.

Elvik, R. 2010. Why Some Road Safety Problems are More Difficult to Solve than Others, Accident Analysis and Prevention 42(10): 1089–1096.

Gstalter, H.; Fastenmeier, W. 2010. Reliability of Drivers in Urban Intersections, Accident Analysis and Prevention 42(1): 225–234.

Geurts, K.; Wets, G.; Brijs, T.; Vanhoof, K. 2003. Profiling of High-Frequency Accident Locations by Use of Association Rules, Transportation Research Record 1840: 123–130.

Heydecker, B. J.; Wu, J. 1991. Using the Information in Road Accident Record, in Proc. of the 19th PTRC Summer Annual Meeting. London, United Kingdom.

Johnson, C.; Lewis, J.; Thew, R. 2009. Assessing and Influencing Driver Attitudes in the United Kingdom, Transportation Research Record 2138: 46–53.

Lin, T.-W.; Hwang, S.-L.; Su, J.-M.; Chen, W.-H. 2008. The Effects of In-Vehicle Tasks and Time-Gap Selection While Reclaiming Control from Adaptive Cruise Control (ACC) with Bus Simulator, Accident Analysis and Prevention 40(3): 1164–1170.

Lyon, C.; Gotts, B.; Wong, W.; Persaud, B. 2007. Comparison of Alternate Methods for Identifying Sites with a High Propensity for Specific Accident Type, Transportation Research Record 2019: 212–218.

Regan, M. A.; Hallett, C.; Gordon, C. P. 2011. Driver Distraction and Driver Inattention: Definition, Relationship and Taxonomy, Accident Analysis and Prevention 43(5): 1771–1781.

Ross, A. J.; Wallace, B.; Davies, J. B. 2004. Technical Note: Measurement Issues in Taxonomic Reliability, Safety Science 42(8): 771–778.

Stanton, N. A.; Salmon, P. M. 2009. Human Error Taxonomies Applied to Driving: a Generic Driver Error Taxonomy and Its Implications for Intelligent Transport Systems, Safety Science 47(2): 227–237.

af Wåhlberg, A. E. 2002. Characteristics of Low Speed Accidents with Buses in Public Transport, Accident Analysis and Prevention 34(5): 637–647.

Wallace, B.; Ross, A. 2007. Effective Taxonomies in Organisational Safety, in Decision Making in Complex Environments. Ed. by Cook, M.; Noues, J.; Masakowski, Y. Chapter 33: 361–372. ISBN 9780754649502.

Yeraguntla, A.; Bhat, C. R. 2005. A Classification Taxonomy and Empirical Analysis of Work Arrangements, Transportation Research Record 1926: 233–241.

DOI: 10.3846/bjrbe.2013.16


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