Examining Safety of Electronic Signs: Using Ordinal Logistic Regression on Speeding

Examining Safety of Electronic Signs: Using Ordinal Logistic Regression on Speeding

Z. Ebrahim H. Nikraz 

Department of Civil Engineering, Curtin University, Australia

Page: 
306-314
|
DOI: 
https://doi.org/10.2495/SAFE-V4-N4-306-314
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
31 December 2014
| Citation

OPEN ACCESS

Abstract: 

Speeding continued to be of alarming concern for many countries. This paper aims to focus on highlighting speeders characteristics on 40 km/h on a busy urban road with high pedestrian movement. This case study utilised the ordinal logistic regression model to test four predictors. Three of which were age and gender of the driver and the time of day drivers were detected speeding. Whereas the fourth explanatory variable is the ‘period’ of the installation of the signs, which tests the usefulness of electronic signs. The study found that the driver’s age contributes slightly to risky speeding behaviours, and older drivers speed less. Time of the day was found to be signifi cant in the model, with a higher number of TINs being recorded in the afternoon than in the morning. Although gender was not found to be a signifi cant predictor, it was shown to produce results similar to speeding data recorded in Perth roads with males speeding slightly more than females. This difference was more pronounced when higher speeding levels were compared. The period variable in the model relating to the installation of the signs was signifi cant, with drivers slowing down after the installation of the fl ashing 40 km/h electronic signs compared with before the installations This may prove the usefulness of such signs in reducing speeding behaviour. Hence, reducing harm by reducing frequency and severity of crashes.

Keywords: 

multinomial logistic regression, ordinal logistic regression, speeding

  References

[1] Moran, A., Report lower speed limits by as much as 20km/h Toronto. http://digitaljour-nal.com/article/323583#ixzz1vPsSSIOa, 24 April 2012.

[2] Vic Roads, Offi cial web page, available at https://www.vicroads.vic.gov.au/safety-and-road-rules/driver-safety/speeding/speeding-and-safety, 2012.

[3] Gitelman, V., Balasha, D., Carmel, R., Hendel, L. & Pesahov, F., Characterization of pedestrian accidents and an examination of infrastructure measures to improve pedestrian safety in Israel. Accident Analysis and Prevention, pp. 1–11, 2010. doi: http:// dx.doi.org/10.1016/j.aap.2010.11.017

[4] Nilsson, G., Speed Accident Rates and Personal Injury Consequences for Different Road Types. Rapport 277, Swedish National Road Transport Research Institute (VTI): Sweden, 1984.

[5] Kloeden, C.N., McLean, A.J., Moore, V.M. & Ponte, G., Travelling speed and the risk of crashes involvement (CR172). Federal Offi ce of Road Safety: Canberra, 1997.

[6] O’Leary, C., Good drivers a danger. The Weekend West, May 14–15, 2011. 

[7] Ebrahim, Z. & Nikraz, H., Before and after studies to reduce the gap between road users and authorities. Nineteenth International Conference on Urban Transport and the Environment, Greece, pp. 663–672, 2013. doi: http://dx.doi.org/10.2495/ut130531

[8] Robinson, C., Safety concerns as 96 drivers caught daily in 40 km/h zones. The Sunday Time, 23 October 2010.

[9] Yan, X., Radwan, E. & Abdel-Aty. M., Characteristics of rear-end accidents at sig-nalized intersections using multiple logistic regression model. Accident Analysis and Prevention, 37(6), pp. 983–995, 2005. doi: http://dx.doi.org/10.1016/j.aap.2005.05.001

[10] Yan, X., Radwan, E. & Mannila, K.K., Analysis of truck-involved rear-end crashes using multinomial logistic regression. Advances in Transportation Studies: an International Journal, 17, pp. 39–52, 2009.

[11] Oxley, J., Charlton, J., Fildes, B., Koppel, S., Scully, J., Congiu, M. & Moore, K., Crash Risk of Older Female Drivers. Report No. 245. Monash University Accident Research Centre: Victoria, Australia, 2005.

[12] Lenny, M.G., Triggs, T.J. & Redman, J.R., Time of day variations in driving performance. Accident Analysis and Prevention, 29(4), pp. 431–437, 1997. doi: http://dx.doi. org/10.1016/s0001-4575(97)00022-5

[13] Wigglesworth, E., Occupational injuries by hour of day of week: a 20-year study. Australian and New Zealand Journal of Public Health, 30(6), pp. 505–508, 2006. doi: http://dx.doi.org/10.1111/j.1467-842x.2006.tb00776.x

[14] Camino Lópeza, M.A., Fontanedab, I., González Alcántarab, O.J. & Ritzel, D.O., The special severity of occupational accidents in the afternoon: “The lunch effect.” Accident Analysis & Prevention, 43(3), pp. 1104–1116, 2011. doi: http://dx.doi.org/10.1016/j. aap.2010.12.019

[15] Banwell, C., Dance, P., Quinn, C., Davies, R. & Hall, D., Alcohol, other drug use and gambling among Australian Capital Territory (ACT) workers in the building and related industries. Drugs: Education, Prevention a nd Policy, 13(2), pp.167–178, 2006. doi: http://dx.doi.org/10.1080/09687630600577550

[16] Field, A., Discovering Statistics Using SPSS, 3rd edn., SAGE Publications Ltd.: London, UK, pp. 264–315, 2009. doi: http://dx.doi.org/10.1002/bjs.7040

[17] Peng, C.Y. & Nichols, R.N., Using multinomial logistic models to predict adolescent behavioural risk. Journal of Modern Applied Statistical Methods 2(1), pp. 1–13, 2003.

[18] Menard, S. Coeffi cients of determination for multiple logistic regression analysis. The American Statistician, 54(1), pp. 17–24, 2000. doi: http://dx.doi.org/10.2307/2685605

[19] Konga, C. & Yanga, J., Logistic regression analysis of pedestrian casualty risk in passenger vehicle collisions in China. Accident Analysis and Prevention, 42, 

pp. 987–993, 2010. doi: http://dx.doi.org/10.1016/j.aap.2009.11.006

[20] Al-Ghamdi, A.S., Using logistic regression to estimate the infl uence of accident factors on accident severity. Accident Analysis and Prevention, 34, pp. 729–774, 2001. doi: http://dx.doi.org/10.1016/s0001-4575(01)00073-2

[21] Cameron, M., Newstead, S., Diamantopoulou, K. & Oxley, P., The Interaction between Speed Camera Enforcement and Speed Related Mass Media Publicity in Victoria. Report No: 201, Monash University Accident research centre: Melbourne, 2003.

[22] Washington, S.P., Karlaftis, M.G. & Mannering, F.L., Statistical and Econometric Methods for Transportation Data Analysis. Chapman & Hall/CRC, A CRC Press Company: Boca Raton, FL, pp. 257–295, 2003. doi: http://dx.doi.org/10.1201/9780203497111

[23] SPSS 18.0., SPSS Inc.: Chicago, IL 60606-6412, 2008. doi: http://dx.doi.org/10.1177/ 106480460100900407

[24] Petrucci, C.J., A primer for social worker researchers on how to conduct a multinomial logistic regression. Journal of Social Service Research, 35(2), pp. 193–205, 2009. doi: http://dx.doi.org/10.1080/01488370802678983

[25] Mountain, L.J., Hirst, W.M. & Maher, M.J., Are speed enforcement cameras more effective than other speed management measures? The impact of speed management schemes on 30 mph roads. Accident Analysis and Prevention, 37, pp. 742–754, 2005. doi: http://dx.doi.org/10.1016/j.aap.2005.03.017

[26] Corben, B., Logan, D.B., Johnston, I. & Vulcan. P., Development of a road safety strategy for Western Australia 2008–2020. Report No. 282, available at http://www.monash. edu.au/miri/research/reports/muarc282.pdf, 2008.