Selection and assessment of the relevant data for reducing the number of red-light running
Abstract
Although traffic light controlled intersections separate, the traffic flows by time and space, road traffic accidents still occur, usually due to Red-Light Running (RLR). In order to define countermeasures to solve this problem, it is necessary to collect and analyze certain data that will indicate type of measures, which should be applied. In this paper, it was done on the example of one 3-leg and one 4-leg intersection where citizens provided information about frequent RLR to the City Administration of Belgrade (Serbia). The statistical significance of differences between the collected data was tested by ANOVA analysis and by PostHoc Tukey test, which showed that forecasting of second of RLR after red-light onset could effectively be conducted by Cubic distribution. In order to define the so-called RLR risk indicator for the intersection, the use of the Danger Degree (DD) indicator, that presents the rate between the number of dangerous situations caused by RLR and the total number of RLR, was proposed.
First published online 11 April 2016
Keyword : signal controlled intersections, red-light running (RLR), second after red-light onset, traffic accidents, danger degree, countermeasures
How to Cite
Vujanić, M., Pešić, D., Antić, B., & Marković, N. (2018). Selection and assessment of the relevant data for reducing the number of red-light running. Transport, 33(1), 268-279. https://doi.org/10.3846/16484142.2016.1174153
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Retting, R.; Williams, A.; Greene, M. 1998. Red-light running and sensible countermeasures: summary of research findings, Transportation Research Record: Journal of the Transportation Research Board 1640: 23–26. http://doi.org/10.3141/1640-04
Saunier, N.; Sayed, T. 2007. Automated analysis of road safety with video data, Transportation Research Record: Journal of the Transportation Research Board 2019: 57–64. http://doi.org/10.3141/2019-08
Schultz, G. G.; Peterson, R.; Eggett, D. L.; Giles, B. C. 2007. Effectiveness of blank-out overhead dynamic advance warning signals at high-speed signalized intersections, Journal of Transportation Engineering 133(10): 564–571. http://doi.org/10.1061/(ASCE)0733-947X(2007)133:10(564)
Smith, D.; McFadden, J.; Passetti, K. 2000. Automated enforcement of red light running technology and programs: a review, Transportation Research Record: Journal of the Transportation Research Board 1734: 29–37. http://doi.org/10.3141/1734-05
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Wu, C.; Yao, L.; Zhang, K. 2012. The red-light running behavior of electric bike riders and cyclists at urban intersections in China: an observational study, Accident Analysis & Prevention 49: 186–192. http://doi.org/10.1016/j.aap.2011.06.001
Yan, X.; Radwan, E.; Birriel, E. 2005. Analysis of red light running crashes based on quasi-induced exposure and multiple logistic regression method, Transportation Research Record: Journal of the Transportation Research Board 1908: 70–79. http://doi.org/10.3141/1908-09
Yan, X.; Radwan, E.; Guo, D.; Richards, S. 2009. Impact of “signal ahead” pavement marking on driver behavior at signalized intersections, Transportation Research Part F: Traffic Psychology and Behaviour 12(1): 50–67. http://doi.org/10.1016/j.trf.2008.07.002
Adams, J. S.; VanDrasek, B. J. 2009. Automated Enforcement of Red-Light Running & Speeding Laws in Minnesota: Bridging Technology and Public Policy. Research Report CTS 09-26. Center for Transportation Studies, University of Minne-sota. 101 p. Available from Internet: https://conservancy.umn.edu/handle/11299/97666
Awadallah, F. 2013. Yellow and all-red intervals: how to improve safety and reduce delay?, International Journal for Traffic and Transport Engineering 3(2): 159–172. http://doi.org/10.7708/ijtte.2013.3(2).05
Awadallah, F. 2009. A legal approach to reduce red light running crashes, Transportation Research Record: Journal of the Transportation Research Board 2096: 102–107. http://doi.org/10.3141/2096-14
Çelik, A. K.; Senger, Ö. 2014. Risk factors affecting fatal versus non-fatal road traffic accidents: the case of Kars province, Turkey, International Journal for Traffic and Transport Engineering 4(3): 339–351. http://doi.org/10.7708/ijtte.2014.4(3).07
De Luca, M. 2015. A comparison between prediction power of artificial neural networks and multivariate analysis in road safety management, Transport. http://doi.org/10.3846/16484142.2014.995702
De Luca, M.; Mauro, R.; Russo, F.; Dell’Acqua, G. 2011. Before-after freeway accident analysis using cluster algorithms, Procedia – Social and Behavioral Sciences 20: 723–731. http://doi.org/10.1016/j.sbspro.2011.08.080
EC. 2008. Proposal for a Directive of the European Parliament and of the Council Facilitating Cross-Border enforcement in the Field of Road Safety (Presented by the Commission of the European Communities). COM(2008) 151 final. 35 p. Available from Internet: http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri = CELEX:52008PC0151&from = EN
Elmitiny, N.; Yan, X.; Radwan, E.; Russo, C.; Nashar, D. 2010. Classification analysis of driver’s stop/go decision and red-light running violation, Accident Analysis & Prevention 42(1): 101–111. http://doi.org/10.1016/j.aap.2009.07.007
Fitzsimmons, E. J.; Hallmark, S.; McDonald, T.; Orellana, M.; Matulac, D. 2007. The Effectiveness of Iowa’s Automated Red Light Running Enforcement Programs. Final Report. Iowa Department of Transportation. 139 p. Available from Internet: http://www.intrans.iastate.edu/reports/rlr-phase2.pdf
Fitzsimmons, E. J.; Hallmark, S. L.; Orellana, M.; McDonald, T.; Matulac, D. 2009. Investigation of Violation Reduction at intersection approaches with automated red light running enforcement cameras in Clive, Iowa, using a cross-sectional analysis, Journal of Transportation Engineering 135(12): 984–989. http://doi.org/10.1061/(ASCE)TE.1943-5436.0000079
Hallmark, S.; Oneyear, N.; McDonald, T. 2012. Toolbox of Countermeasures to Reduce Red Light Running. Final Report. Midwest Transportation Consortium. 46 p. Available from Internet: http://www.intrans.iastate.edu/research/documents/research-reports/RLR_toolbox_w_cvr.pdf
Hallmark, S.; Oneyear, N.; McDonald, T. 2011. Evaluating the Effectiveness of Red Light Running Camera Enforcement in Cedar Rapids and Developing Guidelines for Selection and Use of Red Light Running Countermeasures. Final Report. Midwest Transportation Consortium. 68 p. Available from Internet: http://www.ctre.iastate.edu/research/detail.cfm?projectID = 1284663747
Huang, H.; Chin, H. C. 2009. Disaggregate propensity study on red light running crashes using quasi-induced exposure method, Journal of Transportation Engineering 135(3): 104–111. http://doi.org/10.1061/(ASCE)0733-947X(2009)135:3(104)
Ismail, K.; Sayed, T.; Saunier, N.; Lim, C. 2009. Automated analysis of pedestrian-vehicle conflicts using video data, Transportation Research Record: Journal of the Transportation Research Board 2140: 44–54. http://doi.org/10.3141/2140-05
Johnson, M.; Newstead, S.; Charlton, J.; Oxley, J. 2011. Riding through red lights: The rate, characteristics and risk factors of non-compliant urban commuter cyclists, Accident Analysis & Prevention 43(1): 323–328. http://doi.org/10.1016/j.aap.2010.08.030
Limanond, T.; Prabjabok, P.; Tippayawong, K. 2010. Exploring impacts of countdown timers on traffic operations and driver behavior at a signalized intersection in Bangkok, Transport Policy 17(6): 420–427. http://doi.org/10.1016/j.tranpol.2010.04.009
Liu, Y.; Chang, G.-L.; Yu, J. 2012. Empirical study of driver responses during the yellow signal phase at six Maryland intersections, Journal of Transportation Engineering 138(1): 31–42. http://doi.org/10.1061/(ASCE)TE.1943-5436.0000278
Long, K.; Han, L. D.; Yang, Q. 2011. Effects of countdown timers on driver behavior after the yellow onset at Chinese intersections, Traffic Injury Prevention 12(5): 538–544. http://doi.org/10.1080/15389588.2011.593010
Ma, W.; Liu, Y.; Yang, X. 2010. Investigating the impacts of green signal countdown devices: empirical approach and case study in China, Journal of Transportation Engineering 136(11): 1049–1055. http://doi.org/10.1061/(ASCE)TE.1943-5436.0000181
Palat, B.; Delhomme, P. 2012. What factors can predict why drivers go through yellow traffic lights? An approach based on an extended theory of planned behavior, Safety Science 50(3): 408–417. http://doi.org/10.1016/j.ssci.2011.09.020
Pesic, D.; Vujanic, M.; Lipovac, K.; Antic, B. 2011. Analysis of possibility for traffic safety improvement based on Serbian traffic violation database analysis, Scientific Research and Essays 6(29): 6140–6151. http://doi.org/10.5897/SRE11.1272
Phillips, R. O.; Bjørnskau, T.; Hagman, R.; Sagberg, F. 2011. Reduction in car–bicycle conflict at a road–cycle path intersection: evidence of road user adaptation?, Transportation Research Part F: Traffic Psychology and Behaviour 14(2): 87–95. http://doi.org/10.1016/j.trf.2010.11.003
Porter, B. E.; England, K. J. 2000. Predicting red-light running behavior: a traffic safety study in three urban settings, Journal of Safety Research 31(1): 1–8. http://doi.org/10.1016/S0022-4375(99)00024-9
Retting, R. A.; Chapline, J. F.; Williams, A. F. 2002. Changes in crash risk following re-timing of traffic signal change intervals, Accident Analysis & Prevention 34(2): 215–220. http://doi.org/10.1016/S0001-4575(01)00016-1
Retting, R. A.; Williams, A. F.; Farmer, C. M.; Feldman, A. F. 1999a. Evaluation of red light camera enforcement in Oxnard, California, Accident Analysis & Prevention 31(3): 169–174. http://doi.org/10.1016/S0001-4575(98)00059-1
Retting, R. A.; Williams, A. F.; Farmer, C. M.; Feldman, A. F. 1999b. Evaluation of red light camera enforcement in Fairfax, VA., USA, ITE Journal 69(8): 30–34.
Retting, R.; Williams, A.; Greene, M. 1998. Red-light running and sensible countermeasures: summary of research findings, Transportation Research Record: Journal of the Transportation Research Board 1640: 23–26. http://doi.org/10.3141/1640-04
Saunier, N.; Sayed, T. 2007. Automated analysis of road safety with video data, Transportation Research Record: Journal of the Transportation Research Board 2019: 57–64. http://doi.org/10.3141/2019-08
Schultz, G. G.; Peterson, R.; Eggett, D. L.; Giles, B. C. 2007. Effectiveness of blank-out overhead dynamic advance warning signals at high-speed signalized intersections, Journal of Transportation Engineering 133(10): 564–571. http://doi.org/10.1061/(ASCE)0733-947X(2007)133:10(564)
Smith, D.; McFadden, J.; Passetti, K. 2000. Automated enforcement of red light running technology and programs: a review, Transportation Research Record: Journal of the Transportation Research Board 1734: 29–37. http://doi.org/10.3141/1734-05
Tiwari, G.; Bangdiwala, S.; Saraswat, A.; Gaurav, S. 2007. Survival analysis: pedestrian risk exposure at signalized intersections, Transportation Research Part F: Traffic Psychology and Behaviour 10(2): 77–89. http://doi.org/10.1016/j.trf.2006.06.002
Wu, C.; Yao, L.; Zhang, K. 2012. The red-light running behavior of electric bike riders and cyclists at urban intersections in China: an observational study, Accident Analysis & Prevention 49: 186–192. http://doi.org/10.1016/j.aap.2011.06.001
Yan, X.; Radwan, E.; Birriel, E. 2005. Analysis of red light running crashes based on quasi-induced exposure and multiple logistic regression method, Transportation Research Record: Journal of the Transportation Research Board 1908: 70–79. http://doi.org/10.3141/1908-09
Yan, X.; Radwan, E.; Guo, D.; Richards, S. 2009. Impact of “signal ahead” pavement marking on driver behavior at signalized intersections, Transportation Research Part F: Traffic Psychology and Behaviour 12(1): 50–67. http://doi.org/10.1016/j.trf.2008.07.002