Identification of hotspots on roads using continual variance analysis
Abstract
This paper presents a new statistical model for the identification of dangerous locations (subsections) on roads, also known as hotspots. The model is based on continual analysis of variance. The variance parameter has the potential for the synthesis of quantity and quality, especially regarding traffic accident frequencies and the consequences of traffic accidents within subsections and the significant comparison of the produced synthesis. The sensitivity of the suggested model can be adjusted with the level of disjunction and the length of subsections. A practical application of the new model is performed using a sample of 8442 traffic accidents, of which 6079 were Property Damage Only (PDO) accidents, 2041 resulted in injuries and 322 resulted in fatalities. The sample is for the period of 2001 to 2011 and is from an ‘I class’ two lane rural state road in the Serbia with total length of 284 kilometres. The results acquired using the continual analysis of variance were compared with previous results from four HotSpot Identification Methods (HSID) that are also based on the frequency of traffic accidents.
First published online 12 April 2017
Keyword : traffic accidents, traffic safety, hotspots on the roads, continual analysis of variance, frequency of traffic accidents, HSID methods
How to Cite
Anđelković, D., Antić, B., Lipovac, K., & Tanackov, I. (2018). Identification of hotspots on roads using continual variance analysis. Transport, 33(2), 478–488. https://doi.org/10.3846/16484142.2017.1289479
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Antić, B.; Pešić, D.; Vujanić, M.; Lipovac, K. 2013. The influence of speed bumps heights to the decrease of the vehicle speed – Belgrade experience, Safety Science 57: 303–312. https://doi.org/10.1016/j.ssci.2013.03.008
Bíl, M.; Andrášik, R.; Janoška, Z. 2013. Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation, Accident Analysis & Prevention 55: 265–273. https://doi.org/10.1016/j.aap.2013.03.003
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Cheng, W.; Washington, S. P. 2005. Experimental evaluation of hotspot identification methods, Accident Analysis & Prevention 37(5): 870–881. https://doi.org/10.1016/j.aap.2005.04.015
Connors, R. D.; Maher, M.; Wood, A.; Mountain, L.; Ropkins, K. 2013. Methodology for fitting and updating predictive accident models with trend, Accident Analysis & Prevention 56: 82–94. https://doi.org/10.1016/j.aap.2013.03.009
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El-Basyouny, K.; Sayed, T. 2010. Application of generalized link functions in developing accident prediction models, Safety Science 48(3): 410–416. https://doi.org/10.1016/j.ssci.2009.09.007
Elvik, R. 1988. Some difficulties in defining populations of “entities” for estimating the expected number of accidents, Accident Analysis & Prevention 20(4): 261–275. https://doi.org/10.1016/0001-4575(88)90054-1
Ferreira, S.; Couto, A. 2013. Traffic flow-accidents relationship for urban intersections on the basis of the translog function, Safety Science 60: 115–122. https://doi.org/10.1016/j.ssci.2013.07.007
Geedipally, S. R.; Lord, D.; Dhavala, S.S. 2014. A caution about using deviance information criterion while modeling traffic crashes, Safety Science 62: 495–498. https://doi.org/10.1016/j.ssci.2013.10.007
Gregoriades, A.; Mouskos, K. C. 2013. Black spots identification through a Bayesian networks quantification of accident risk index, Transportation Research Part C: Emerging Technologies 28: 28–43. https://doi.org/10.1016/j.trc.2012.12.008
Harwood, D. W.; Council, F. M.; Hauer, E.; Hughes, W. E.; Vogt, A. 2000. Prediction of the Expected Safety Performance of Rural Two-Lane Highways. Publication No. FHWA-RD-99-207. Federal Highway Administration (FHWA), US Department of Transportation, Washington, DC, US. 200 p. Available from Internet: https://www.fhwa.dot.gov/publications/research/safety/99207/99207.pdf
Harwood, D. W.; Torbic, D. J.; Richard, K. R.; Meyer, M. M. 2010. SafetyAnalyst: Software Tools for Safety Management of Specific Highway Sites. FHWA-HRT-10-063. Federal Highway Administration (FHWA). 305 p. Available from Internet: http://www.dot.ca.gov/newtech/researchreports/reports/2010/final_report_task_1601.pdf
Hauer, E. 1997. Observational Before-After Studies in Road Safety: Estimating the Effect of Highway and Traffic Engineering Measures on Road Safety. Pergamon. 289 p.
Heydecker, B. G.; Wu, J. 2001. Identification of sites for road accident remedial work by Bayesian statistical methods: an example of uncertain inference, Advances in Engineering Software 32(10–11): 859–869. https://doi.org/10.1016/S0965-9978(01)00037-0
Hinde, J.; Demétrio, C. G. B. 1998. Overdispersion: models and estimation, Computational Statistics & Data Analysis 27(2): 151–170. https://doi.org/10.1016/S0167-9473(98)00007-3
Jiang, X.; Abdel-Aty, M.; Alamili, S. 2014. Application of Poisson random effect models for highway network screening, Accident Analysis & Prevention 63: 74–82. https://doi.org/10.1016/j.aap.2013.10.029
Jin, T. G.; Saito, M.; Eggett, D. L. 2008. Statistical comparisons of the crash characteristics on highways between construction time and non-construction time, Accident Analysis & Prevention 40(6): 2015–2023. https://doi.org/10.1016/j.aap.2008.08.024
Kwon, O. H.; Park, M.J.; Yeo, H.; Chung, K. 2013. Evaluating the performance of network screening methods for detecting high collision concentration locations on highways, Accident Analysis & Prevention 51: 141–149. https://doi.org/10.1016/j.aap.2012.10.019
Lipovac, K.; Jovanović, D.; Vuksanović, B. 2010. Uporedna analiza identifikacije opasnih mesta i rizičnih deonica na državnim putevima R Srbije, in X međunarodni simpozijum ‘Prevencija saobraćajnih nezgoda na putevima 2010’, 21–22 Oktobar 2010, Novi Sad, Srbija. (in Serbian).
Lord, D. 2008. Methodology for estimating the variance and confidence intervals for the estimate of the product of baseline models and AMFs, Accident Analysis & Prevention 40(3): 1013–1017. https://doi.org/10.1016/j.aap.2007.11.008
Lord, D.; Miranda-Moreno, L. F. 2008. Effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter of Poisson-gamma models for modeling motor vehicle crashes: a Bayesian perspective, Safety Science 46(5): 751–770. https://doi.org/10.1016/j.ssci.2007.03.005
Manner, H.; Wünsch-Ziegler, L. 2013. Analyzing the severity of accidents on the German autobahn, Accident Analysis & Prevention 57: 40–48. https://doi.org/10.1016/j.aap.2013.03.022
Miaou, S.-P.; Lord, D. 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods, Transportation Research Record: Journal of the Transportation Research Board 1840: 31–40. https://doi.org/10.3141/1840-04
Miaou, S.-P.; Song, J. J. 2005. Bayesian ranking of sites for engineering safety improvements: decision parameter, treatability concept, statistical criterion, and spatial dependence, Accident Analysis & Prevention 37(4): 699–720. https://doi.org/10.1016/j.aap.2005.03.012
Miranda-Moreno, L. F.; Labbe, A.; Fu, L. 2007. Bayesian multiple testing procedures for hotspot identification, Accident Analysis & Prevention 39(6): 1192–1201. https://doi.org/10.1016/j.aap.2007.03.008
Montella, A. 2010. A comparative analysis of hotspot identification methods, Accident Analysis & Prevention 42(2): 571–581. https://doi.org/10.1016/j.aap.2009.09.025
Oh, J.; Washington, S.; Lee, D. 2010. Property damage crash equivalency factors to solve crash frequency-severity dilemma: case study on South Korean rural roads, Transportation Research Record: Journal of the Transportation Research Board 2148: 83–92. https://doi.org/10.3141/2148-10
Okamoto, H.; Koshi, M. 1989. A method to cope with the random errors of observed accident rates in regression analysis, Accident Analysis & Prevention 21(4): 317–332. https://doi.org/10.1016/0001-4575(89)90023-7
PIARC. 2004. Road Safety Manual 2004. Recommendations from the World Road Association (PIARC).
Poch, M.; Mannering, F. 1996. Negative binomial analysis of intersection-accident frequencies, Journal of Transportation Engineering 122(2): 105–113. https://doi.org/10.1061/(ASCE)0733-947X(1996)122:2(105)
Qu, X.; Yang, Y.; Liu, Z.; Jin, S.; Weng, J. 2014. Potential crash risks of expressway on-ramps and off-ramps: a case study in Beijing, China, Safety Science 70: 58–62. https://doi.org/10.1016/j.ssci.2014.04.016
Russo, F.; Biancardo S. A.; Dell’Acqua, G. 2014. Consistent approach to predictive modeling and countermeasure determination by crash type for low-volume roads, The Baltic Journal of Road and Bridge Engineering 9(2): 77–87. https://doi.org/10.3846/bjrbe.2014.10
Sadeghi, A.; Ayati, E.; Neghab, M. P. 2013. Identification and prioritization of hazardous road locations by segmentation and data envelopment analysis approach, Promet – Traffic&Transportation 25(2): 127–136.
Savolainen, P. T.; Mannering, F. L.; Lord, D.; Quddus, M. A. 2011. The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives, Accident Analysis & Prevention 43(5): 1666–1676. https://doi.org/10.1016/j.aap.2011.03.025
Shen, J.; Gan, A. 2003. Development of crash reduction factors: methods, problems, and research needs, Transportation Research Record: Journal of the Transportation Research Board 1840: 50–56. https://doi.org/10.3141/1840-06
Sokolovskij, E.; Prentkovskis, O. 2013. Investigating traffic accidents: the interaction between a motor vehicle and a pedestrian, Transport 28(3): 302–312. https://doi.org/10.3846/16484142.2013.831771
Stern, E.; Zehavi, Y. 1990. Road safety and hot weather: a study in applied transport geography, Transactions of the Institute of British Geographers 15(1): 102–111. https://doi.org/10.2307/623096
Tegge, R. A.; Jo, J.-H.; Ouyang, Y. 2010. Development and Application of Safety Performance Functions for Illinois. FHWA-ICT-10-066. Illinois Department of Transportation, Springfield, IL, US. 181 p.
Tunaru, R. 2002. Hierarchical Bayesian models for multiple count data, Austrian Journal of Statistics 31(2–3): 221–229.
Vadlamani, S.; Chen, E.; Ahn, S.; Washington, S. 2011. Identifying large truck hot spots using crash counts and PDOEs, Journal of Transportation Engineering 137(1): 11–21. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000183
Vistisen, D. 2002. Models and Methods for Hot Spot Safety Work: PhD thesis. Technical University of Denmark. 168 p.
Wang, C.; Quddus, M. A.; Ison, S. G. 2013. The effect of traffic and road characteristics on road safety: a review and future research direction, Safety Science 57: 264–275. https://doi.org/10.1016/j.ssci.2013.02.012
Washington, S.; Haque, M.; Oh, J.; Lee, D. 2014. Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots, Accident Analysis & Prevention 66: 136–146. https://doi.org/10.1016/j.aap.2014.01.007
Weiss, H. B.; Kaplan, S.; Prato, C. G. 2014. Analysis of factors associated with injury severity in crashes involving young New Zealand drivers, Accident Analysis & Prevention 65: 142–155. https://doi.org/10.1016/j.aap.2013.12.020
Yu, H.; Liu, P.; Chen, J.; Wang, H. 2014. Comparative Analysis of the spatial analysis methods for hotspot identification, Accident Analysis & Prevention 66: 80–88. https://doi.org/10.1016/j.aap.2014.01.017
Zein, S. 2004. Canadian Guide to In-Service Road Safety Reviews. Transportation Association of Canada, Ottawa, Ontario, Canada. 232 p.
Zou, Y.; Geedipally, S. R.; Lord, D. 2013. Evaluating the double Poisson generalized linear model, Accident Analysis & Prevention 59: 497–505. https://doi.org/10.1016/j.aap.2013.07.017
Abdel-Aty, M. A.; Radwan, A. E. 2000. Modeling traffic accident occurrence and involvement, Accident Analysis & Prevention 32(5): 633–642. https://doi.org/10.1016/S0001-4575(99)00094-9
Aguero-Valverde, J. 2013. Full Bayes Poisson gamma, Poisson lognormal, and zero inflated random effects models: comparing the precision of crash frequency estimates, Accident Analysis & Prevention 50: 289–297. https://doi.org/10.1016/j.aap.2012.04.019
Alver, Y.; Demirel, M. C.; Mutlu, M. M. 2014. Interaction between socio-demographic characteristics: traffic rule violations and traffic crash history for young drivers, Accident Analysis & Prevention 72: 95–104. https://doi.org/10.1016/j.aap.2014.06.015
Anastasopoulos, P. C.; Mannering, F. L. 2009. A note on modeling vehicle accident frequencies with random-parameters count models, Accident Analysis & Prevention 41(1): 153–159. https://doi.org/10.1016/j.aap.2008.10.005
Anđelković, D.; Antić, B.; Pešić, D.; Subotić, M. 2014. Polazne osnove u identifikaciji opasnih mesta na putevima [Fundamentals for identification of dangerous places on the roads], Put i saobraćaj [Journal of Road and Traffic Engineering] (2): 45–52. (in Serbian).
Antić, B.; Pešić, D.; Vujanić, M.; Lipovac, K. 2013. The influence of speed bumps heights to the decrease of the vehicle speed – Belgrade experience, Safety Science 57: 303–312. https://doi.org/10.1016/j.ssci.2013.03.008
Bíl, M.; Andrášik, R.; Janoška, Z. 2013. Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation, Accident Analysis & Prevention 55: 265–273. https://doi.org/10.1016/j.aap.2013.03.003
Cafiso, S.; Di Graziano, A.; Di Silvestro, G.; La Cava, G. 2008. Safety performance indicators for local rural roads: comprehensive procedure from low-cost data survey to accident prediction model, in TRB 87th Annual Meeting Compendium of Papers DVD, 13–17 January 2008, Washington, DC, US, 1–19.
Carey, J. 2001. Arizona Local Government Safety Project Analysis Model. Final Report 504. Arizona Department of Transportation, Phoenix, AZ, US. 136 p. Available from Internet: https://apps.azdot.gov/ADOTLibrary/publications/project_reports/PDF/AZ504.pdf
Cheng, W.; Washington, S. P. 2005. Experimental evaluation of hotspot identification methods, Accident Analysis & Prevention 37(5): 870–881. https://doi.org/10.1016/j.aap.2005.04.015
Connors, R. D.; Maher, M.; Wood, A.; Mountain, L.; Ropkins, K. 2013. Methodology for fitting and updating predictive accident models with trend, Accident Analysis & Prevention 56: 82–94. https://doi.org/10.1016/j.aap.2013.03.009
DoT. 2006. 2005 Valuation of the Benefits of Prevention of Road Accidents and Casualties. Highways Economic Note No. 1. Department for Transport (DoT), London, UK. 13 p.
El-Basyouny, K.; Sayed, T. 2010. Application of generalized link functions in developing accident prediction models, Safety Science 48(3): 410–416. https://doi.org/10.1016/j.ssci.2009.09.007
Elvik, R. 1988. Some difficulties in defining populations of “entities” for estimating the expected number of accidents, Accident Analysis & Prevention 20(4): 261–275. https://doi.org/10.1016/0001-4575(88)90054-1
Ferreira, S.; Couto, A. 2013. Traffic flow-accidents relationship for urban intersections on the basis of the translog function, Safety Science 60: 115–122. https://doi.org/10.1016/j.ssci.2013.07.007
Geedipally, S. R.; Lord, D.; Dhavala, S.S. 2014. A caution about using deviance information criterion while modeling traffic crashes, Safety Science 62: 495–498. https://doi.org/10.1016/j.ssci.2013.10.007
Gregoriades, A.; Mouskos, K. C. 2013. Black spots identification through a Bayesian networks quantification of accident risk index, Transportation Research Part C: Emerging Technologies 28: 28–43. https://doi.org/10.1016/j.trc.2012.12.008
Harwood, D. W.; Council, F. M.; Hauer, E.; Hughes, W. E.; Vogt, A. 2000. Prediction of the Expected Safety Performance of Rural Two-Lane Highways. Publication No. FHWA-RD-99-207. Federal Highway Administration (FHWA), US Department of Transportation, Washington, DC, US. 200 p. Available from Internet: https://www.fhwa.dot.gov/publications/research/safety/99207/99207.pdf
Harwood, D. W.; Torbic, D. J.; Richard, K. R.; Meyer, M. M. 2010. SafetyAnalyst: Software Tools for Safety Management of Specific Highway Sites. FHWA-HRT-10-063. Federal Highway Administration (FHWA). 305 p. Available from Internet: http://www.dot.ca.gov/newtech/researchreports/reports/2010/final_report_task_1601.pdf
Hauer, E. 1997. Observational Before-After Studies in Road Safety: Estimating the Effect of Highway and Traffic Engineering Measures on Road Safety. Pergamon. 289 p.
Heydecker, B. G.; Wu, J. 2001. Identification of sites for road accident remedial work by Bayesian statistical methods: an example of uncertain inference, Advances in Engineering Software 32(10–11): 859–869. https://doi.org/10.1016/S0965-9978(01)00037-0
Hinde, J.; Demétrio, C. G. B. 1998. Overdispersion: models and estimation, Computational Statistics & Data Analysis 27(2): 151–170. https://doi.org/10.1016/S0167-9473(98)00007-3
Jiang, X.; Abdel-Aty, M.; Alamili, S. 2014. Application of Poisson random effect models for highway network screening, Accident Analysis & Prevention 63: 74–82. https://doi.org/10.1016/j.aap.2013.10.029
Jin, T. G.; Saito, M.; Eggett, D. L. 2008. Statistical comparisons of the crash characteristics on highways between construction time and non-construction time, Accident Analysis & Prevention 40(6): 2015–2023. https://doi.org/10.1016/j.aap.2008.08.024
Kwon, O. H.; Park, M.J.; Yeo, H.; Chung, K. 2013. Evaluating the performance of network screening methods for detecting high collision concentration locations on highways, Accident Analysis & Prevention 51: 141–149. https://doi.org/10.1016/j.aap.2012.10.019
Lipovac, K.; Jovanović, D.; Vuksanović, B. 2010. Uporedna analiza identifikacije opasnih mesta i rizičnih deonica na državnim putevima R Srbije, in X međunarodni simpozijum ‘Prevencija saobraćajnih nezgoda na putevima 2010’, 21–22 Oktobar 2010, Novi Sad, Srbija. (in Serbian).
Lord, D. 2008. Methodology for estimating the variance and confidence intervals for the estimate of the product of baseline models and AMFs, Accident Analysis & Prevention 40(3): 1013–1017. https://doi.org/10.1016/j.aap.2007.11.008
Lord, D.; Miranda-Moreno, L. F. 2008. Effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter of Poisson-gamma models for modeling motor vehicle crashes: a Bayesian perspective, Safety Science 46(5): 751–770. https://doi.org/10.1016/j.ssci.2007.03.005
Manner, H.; Wünsch-Ziegler, L. 2013. Analyzing the severity of accidents on the German autobahn, Accident Analysis & Prevention 57: 40–48. https://doi.org/10.1016/j.aap.2013.03.022
Miaou, S.-P.; Lord, D. 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods, Transportation Research Record: Journal of the Transportation Research Board 1840: 31–40. https://doi.org/10.3141/1840-04
Miaou, S.-P.; Song, J. J. 2005. Bayesian ranking of sites for engineering safety improvements: decision parameter, treatability concept, statistical criterion, and spatial dependence, Accident Analysis & Prevention 37(4): 699–720. https://doi.org/10.1016/j.aap.2005.03.012
Miranda-Moreno, L. F.; Labbe, A.; Fu, L. 2007. Bayesian multiple testing procedures for hotspot identification, Accident Analysis & Prevention 39(6): 1192–1201. https://doi.org/10.1016/j.aap.2007.03.008
Montella, A. 2010. A comparative analysis of hotspot identification methods, Accident Analysis & Prevention 42(2): 571–581. https://doi.org/10.1016/j.aap.2009.09.025
Oh, J.; Washington, S.; Lee, D. 2010. Property damage crash equivalency factors to solve crash frequency-severity dilemma: case study on South Korean rural roads, Transportation Research Record: Journal of the Transportation Research Board 2148: 83–92. https://doi.org/10.3141/2148-10
Okamoto, H.; Koshi, M. 1989. A method to cope with the random errors of observed accident rates in regression analysis, Accident Analysis & Prevention 21(4): 317–332. https://doi.org/10.1016/0001-4575(89)90023-7
PIARC. 2004. Road Safety Manual 2004. Recommendations from the World Road Association (PIARC).
Poch, M.; Mannering, F. 1996. Negative binomial analysis of intersection-accident frequencies, Journal of Transportation Engineering 122(2): 105–113. https://doi.org/10.1061/(ASCE)0733-947X(1996)122:2(105)
Qu, X.; Yang, Y.; Liu, Z.; Jin, S.; Weng, J. 2014. Potential crash risks of expressway on-ramps and off-ramps: a case study in Beijing, China, Safety Science 70: 58–62. https://doi.org/10.1016/j.ssci.2014.04.016
Russo, F.; Biancardo S. A.; Dell’Acqua, G. 2014. Consistent approach to predictive modeling and countermeasure determination by crash type for low-volume roads, The Baltic Journal of Road and Bridge Engineering 9(2): 77–87. https://doi.org/10.3846/bjrbe.2014.10
Sadeghi, A.; Ayati, E.; Neghab, M. P. 2013. Identification and prioritization of hazardous road locations by segmentation and data envelopment analysis approach, Promet – Traffic&Transportation 25(2): 127–136.
Savolainen, P. T.; Mannering, F. L.; Lord, D.; Quddus, M. A. 2011. The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives, Accident Analysis & Prevention 43(5): 1666–1676. https://doi.org/10.1016/j.aap.2011.03.025
Shen, J.; Gan, A. 2003. Development of crash reduction factors: methods, problems, and research needs, Transportation Research Record: Journal of the Transportation Research Board 1840: 50–56. https://doi.org/10.3141/1840-06
Sokolovskij, E.; Prentkovskis, O. 2013. Investigating traffic accidents: the interaction between a motor vehicle and a pedestrian, Transport 28(3): 302–312. https://doi.org/10.3846/16484142.2013.831771
Stern, E.; Zehavi, Y. 1990. Road safety and hot weather: a study in applied transport geography, Transactions of the Institute of British Geographers 15(1): 102–111. https://doi.org/10.2307/623096
Tegge, R. A.; Jo, J.-H.; Ouyang, Y. 2010. Development and Application of Safety Performance Functions for Illinois. FHWA-ICT-10-066. Illinois Department of Transportation, Springfield, IL, US. 181 p.
Tunaru, R. 2002. Hierarchical Bayesian models for multiple count data, Austrian Journal of Statistics 31(2–3): 221–229.
Vadlamani, S.; Chen, E.; Ahn, S.; Washington, S. 2011. Identifying large truck hot spots using crash counts and PDOEs, Journal of Transportation Engineering 137(1): 11–21. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000183
Vistisen, D. 2002. Models and Methods for Hot Spot Safety Work: PhD thesis. Technical University of Denmark. 168 p.
Wang, C.; Quddus, M. A.; Ison, S. G. 2013. The effect of traffic and road characteristics on road safety: a review and future research direction, Safety Science 57: 264–275. https://doi.org/10.1016/j.ssci.2013.02.012
Washington, S.; Haque, M.; Oh, J.; Lee, D. 2014. Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots, Accident Analysis & Prevention 66: 136–146. https://doi.org/10.1016/j.aap.2014.01.007
Weiss, H. B.; Kaplan, S.; Prato, C. G. 2014. Analysis of factors associated with injury severity in crashes involving young New Zealand drivers, Accident Analysis & Prevention 65: 142–155. https://doi.org/10.1016/j.aap.2013.12.020
Yu, H.; Liu, P.; Chen, J.; Wang, H. 2014. Comparative Analysis of the spatial analysis methods for hotspot identification, Accident Analysis & Prevention 66: 80–88. https://doi.org/10.1016/j.aap.2014.01.017
Zein, S. 2004. Canadian Guide to In-Service Road Safety Reviews. Transportation Association of Canada, Ottawa, Ontario, Canada. 232 p.
Zou, Y.; Geedipally, S. R.; Lord, D. 2013. Evaluating the double Poisson generalized linear model, Accident Analysis & Prevention 59: 497–505. https://doi.org/10.1016/j.aap.2013.07.017