Exploring the effects of state highway safety laws and sociocultural characteristics on fatal crashes

References

• Alver Y, Demirel MC, Mutlu MM. Interaction between socio-demographic characteristics: traffic rule violations and traffic crash history for young drivers. Accid Anal Prev. 2014;72:95–104.
   PubMed Web of Science ®Google Scholar
• Atchley P, Atwood S, Boulton A. The choice to text and drive in younger drivers: behavior may shape attitude. Accid Anal Prev. 2011;43:134–142.
   PubMed Web of Science ®Google Scholar
• Brusque C, Alauzet A. Analysis of the individual factors affecting mobile phone use while driving in France: socio-demographic characteristics, car and phone use in professional and private contexts. Accid Anal Prev. 2008;40:35–44.
   PubMed Web of Science ®Google Scholar
• Chen H, Cao L, Logan D. Analysis of risk factors affecting the severity of intersection crashes by logistic regression. Traffic Inj Prev. 2012;13:300–307.
   PubMed Web of Science ®Google Scholar
• Delaney A, Ward H, Cameron M, Williams AF. Controversies and speed cameras: lessons learnt internationally. J Public Health Policy. 2005;26:404–415.
   PubMed Web of Science ®Google Scholar
• Dobson A, Brown W, Ball J, Powers J, McFadden M. Women driver's behavior, socio-demographic characteristics and accidents. Accid Anal Prev. 1999;31:525–535.
   PubMed Web of Science ®Google Scholar
• Ehsani JP, Bingham CR, Ionides E, Childers D. The impact of Michigan's text messaging restriction on motor vehicle crashes. J Adolesc Health. 2014;54(5):S68–S74.
   PubMed Web of Science ®Google Scholar
• Fowles R, Loeb PD, Clarke WA. The cell phone effect on motor vehicle fatality rates: a Bayesian and classical econometric evaluation. Transp Res Part E Logist Transp Rev. 2010;46:1140–1147.
   Web of Science ®Google Scholar
• Goodwin AH, O'Brien NP, Foss RD. Effect of North Carolina's restriction on teenage driver cell phone use two years after implementation. Accid Anal Prev. 2012;48:363–367.
   PubMed Web of Science ®Google Scholar
• Hallett C, Lambert A, Regan MA. Text messaging amongst New Zealand drivers: prevalence and risk perception. Transp Res Part F Traffic Psychol Behav. 2012;15(3):261–271.
   Web of Science ®Google Scholar
• Harrison MA. College students' prevalence and perceptions of text messaging while driving. Accid Anal Prev. 2011;43:1516–1520.
   PubMed Web of Science ®Google Scholar
• Huisingh C, Griffin R, McGwin G. The prevalence of distraction among passenger vehicle drivers: a roadside observational approach. Traffic Inj Prev. 2015;16(2):140–146.
   PubMed Web of Science ®Google Scholar
• Jovanović D, Lipovac K, Stanojević P, Stanojević D. The effects of personality traits on driving-related anger and aggressive behavior in traffic among Serbian drivers. Transp Res Part F Traffic Psychol Behav. 2011;14:43–53.
   Web of Science ®Google Scholar
• Lee AH, Wang K, Yau KKW, Somerford PJ. Truncated negative binomial mixed regression modelling of ischaemic stroke hospitalizations. Stat Med. 2003;22:1129–1139.
   PubMed Web of Science ®Google Scholar
• Li Z, Wang W, Liu P, Bigham JM, Ragland DR. Using geographically weighted Poisson regression for county-level crash modeling in California. Saf Sci. 2013;58:89–97.
   Web of Science ®Google Scholar
• McCartt AT, Hellinga LA, Strouse LM, Farmer CM. Long-term effects of handheld cell phone laws on driver handheld cell phone use. Traffic Inj Prev. 2010;11(2):133–141.
   PubMed Web of Science ®Google Scholar
• Melinder K. Socio-cultural characteristics of high versus low risk societies regarding road traffic safety. Saf Sci. 2007;45:397–414.
   Web of Science ®Google Scholar
• Mountain LJ, Hirst WM, Maher MJ. Costing lives or saving lives: a detailed evaluation of the impact of speed cameras. Traffic Eng Control. 2004;45(8):280–287.
   Google Scholar
• Mountain LJ, Hirst WM, Maher MJ. Are speed enforcement cameras more effective than other speed management measures? The impact of speed management schemes on 30 mph roads. Accid Anal Prev. 2005;37:742–754.
   PubMed Web of Science ®Google Scholar
• Narayanamoorthy S, Paleti R, Bhat CR. On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level. Transp Res Part B Methodol. 2013;55:245–264.
   Web of Science ®Google Scholar
• NHTSA. Traffic Safety Facts: Distracted Driving 2012. Author, US Department of Transportation; 2014. Available at: http://www.distraction.gov/downloads/pdfs/812012.pdf.
   Google Scholar
• Nikolaev AG, Robbins MJ, Jacobson SH. Evaluating the impact of legislation prohibiting hand-held cell phone use while driving. Transp Res Part A Policy Pract. 2010;44(3):182–193.
   Web of Science ®Google Scholar
• Nordfjærn T, Jørgensen S, Rundmo T. Culture and socio-demographic predictors of car accident involvement in Norway, Ghana, Tanzania and Uganda. Saf Sci. 2012;50:1862–1872.
   Web of Science ®Google Scholar
• O'Neill MF, Faddy MJ. Use of binary and truncated negative binomial modelling in the analysis of recreational catch data. Fish Res. 2003;60:471–477.
   Web of Science ®Google Scholar
• Pei X, Wong SC, Sze NN. A joint-probability approach to crash prediction models. Accid Anal Prev. 2011;43:1160–1166.
   PubMed Web of Science ®Google Scholar
• Retting RA, Kyrychenko SY, McCartt AT. Evaluation of automated speed enforcement on loop 101 freeway in Scottsdale, Arizona. Accid Anal Prev. 2008;40:1506–1512.
   PubMed Web of Science ®Google Scholar
• Sampford MR. The truncated negative binomial distribution. Biometrika. 1995;42(1/2):58–69.
   Web of Science ®Google Scholar
• Shin K, Washington SP, van Schalkwyk I. Evaluation of the Scottsdale Loop 101 automated speed enforcement demonstration program. Accid Anal Prev. 2009;41:393–403.
   PubMed Web of Science ®Google Scholar
• Tay R. Speed cameras: improving safety or raising revenue? J Transp Econ Policy. 2010;44(2):247–257.
   Google Scholar