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Crash risk factor identification using association rules in Nagpur city, Maharashtra, India


Affiliations
1 Civil Engineering Department, Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur 440 010, India
 

The increase in traffic volume in urban road networks poses a significant challenge to transportation safety. It is evident that different traffic zones experience unique crash patterns and severities. The different factors that affect crash rates are caused by the various character-istics of the drivers, weather conditions, design of road-side infrastructure and driving behaviour. Although studies have shown that various factors can affect crash rates, there are insufficient studies on the exact catego-rization of these factors. Accordingly, the present study focuses on traffic crashes on streets where the risks of an accident occurrence are higher, using Nagpur city, Maharashtra, India as a case study. Three levels of risk zones were selected, i.e. zone-I (low risk), zone-II (medi-um risk) and zone-III (high risk). The risk zones are created in ArcGIS software using the kernel density esti-mator function. The association rule was then used to find out the various crash risk factors within the zone. The results of the study reveal that the risk of pedestrian fatalities is higher in areas where the speed limit is more than 40 km/h and day-to-day pedestrian activity is pre-sent. Based on the results, we propose a lower speed limit in zone-I, in addition to providing pedestrian-crossing fa-cilities such as zebra crossings or refuge islands for cross-walks. Moreover, we propose implementing an awareness campaign for road traffic safety aimed at educating road users on how to follow road discipline, especially with regard to utilizing pedestrian facilities, aggressive young motorcyclists, lane changing and overtaking mano-euvres.

Keywords

Association rules, driver characteristics, risk factors, traffic crash, urban roads.
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  • Transport Research Wing, Road Accident in India, Transport Research Wing, Ministry of Road Transport and Highways, Government of India, New Delhi, 2018.
  • Fuller, R., The task–capability interface model of the driving pro-cess. Recherche-Transports-Sécurité, 2000, 66, 47–57.
  • Lord, D. and Mannering, F., The statistical analysis of crash-fre-quency data: A review and assessment of methodological alter-natives. Transp. Res. Part A, 2010, 44, 291–305.
  • Savolainen, P. T., Mannering, F. L., Lord, D. and Quddus, M. A., The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. Accid. Anal. Prev., 2011, 43, 1666–1676.
  • Mannering, F. L. and Bhat, C. R., Analytic methods in accident research: Methodological frontier and future directions. Anal. Methods Accid. Res., 2014, 1, 1–22.
  • Christoforou, Z., Cohen, S. and Karlaftis, M. G., Identifying crash type propensity using real-time traffic data on freeways. J. Safety Res., 2011, 42, 43–50.
  • Miranda-Moreno, L. F., Fu, L., Saccomanno, F. F. and Labbe, A., Alternative risk models for ranking locations for safety improve-ment. Transp. Res. Rec., 2005, 1908, 1–8.
  • Wu, P., Meng, X., Song, L. and Zuo, W., Crash risk evaluation and crash severity pattern analysis for different types of urban junc-tions: fault tree analysis and association rules approaches. Transp. Res. Rec., 2019, 2673, 403–416.
  • de Melo, W. A., Alarcão, A. C. J., de Oliveira, A. P. R., Pelloso, S. M. and Carvalho, M. D. D. B., Age-related risk factors with nonfatal traffic accidents in urban areas in Maringá, Paraná, Brazil. Traffic Inj. Prev., 2017, 18, 157–163.
  • Martensen, H. and Dupont, E., Comparing single vehicle and multivehicle fatal road crashes: a joint analysis of road conditions, time variables and driver characteristics. Accid. Anal. Prev., 2013, 60, 466–471.
  • Yasmin, S., Eluru, N., Bhat, C. R. and Tay, R., A latent seg-mentation based generalized ordered logit model to examine factors influencing driver injury severity. Anal. Methods Accid. Res., 2014, 1, 23–38.
  • Zhang, G., Yau, K. K. W. and Gong, X., Traffic violations in Guangdong Province of China: speeding and drunk driving. Accid. Anal. Prev., 2014, 64, 30–40.
  • Yan, X., Radwan, E. and Abdel-Aty, M., Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model. Accid. Anal. Prev., 2005, 37, 983–995.
  • Bíl, M., Bílová, M., Dobiáš, M. and Andrášik, R., Circumstances and causes of fatal cycling crashes in the Czech Republic. Traffic Inj. Prev., 2016, 17, 394–399.
  • Rezapour, M., Moomen, M. and Ksaibati, K., Ordered logistic models of influencing factors on crash injury severity of single and multiple-vehicle downgrade crashes: a case study in Wyoming. J. Safety Res., 2019, 68, 107–118.
  • Kim, J.-K., Kim, S., Ulfarsson, G. F. and Porrello, L. A., Bicyclist injury severities in bicycle–motor vehicle accidents. Accid. Anal. Prev., 2007, 39, 238–251.
  • Khasawneh, M. A., Al-Omari, A. A. and Oditallah, M., Assessing speed of passenger cars at urban channelized right-turn roadways of signalized intersections. Arab. J. Sci. Eng., 2019, 44, 5057–5073.
  • Eluru, N., Bhat, C. R. and Hensher, D. A., A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes. Accid. Anal. Prev., 2008, 40, 1033–1054.
  • Rifaat, S. M., Tay, R. and De Barros, A., Effect of street pattern on the severity of crashes involving vulnerable road users. Accid. Anal. Prev., 2011, 43, 276–283.
  • Li, Y., Liu, C. and Ding, L., Impact of pavement conditions on crash severity. Accid. Anal. Prev., 2013, 59, 399–406.
  • Hosseinpour, M., Yahaya, A. S. and Sadullah, A. F., Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: case studies from Malaysian Federal Roads. Accid. Anal. Prev., 2014, 62, 209–222.
  • Depaire, B., Wets, G. and Vanhoof, K., Traffic accident segmenta-tion by means of latent class clustering. Accid. Anal. Prev., 2008, 40, 1257–1266.
  • Clarke, D. D., Ward, P., Bartle, C. and Truman, W., Young driver accidents in the UK: The influence of age, experience, and time of day. Accid. Anal. Prev., 2006, 38, 871–878.
  • Yau, K. K. W., Lo, H.-P. and Fung, S. H. H., Multiple-vehicle traffic accidents in Hong Kong. Accid. Anal. Prev., 2006, 38, 1157–1161.
  • Khorashadi, A., Niemeier, D., Shankar, V. and Mannering, F., Differences in rural and urban driver-injury severities in accidents involving large-trucks: an exploratory analysis. Accid. Anal. Prev., 2005, 37, 910–921.
  • Keay, K. and Simmonds, I., Road accidents and rainfall in a large Australian city. Accid. Anal. Prev., 2006, 38, 445–454.
  • Loo, B. P. Y. and Yao, S., The identification of traffic crash hot zones under the link-attribute and event-based approaches in a network-constrained environment. Comput. Environ. Urban Syst., 2013, 41, 249–261.
  • Eckley, D. C. and Curtin, K. M., Evaluating the spatiotemporal clu-stering of traffic incidents. Comput. Environ. Urban Syst., 2013, 37, 70–81.
  • Gundogdu, I. B., Applying linear analysis methods to GIS-suppor-ted procedures for preventing traffic accidents: case study of Konya. Saf. Sci., 2010, 48, 763–769.
  • Truong, L. T. and Somenahalli, S. V. C., Using GIS to identify pedestrian–vehicle crash hot spots and unsafe bus stops. J. Public Transp., 2011, 14, 6.
  • Erdogan, S., A comparison of interpolation methods for producing digital elevation models at the field scale. Earth Surf. Process. Landf., 2009, 34, 366–376.
  • Vandenbulcke, G., Thomas, I. and Panis, L. I., Predicting cycling accident risk in Brussels: a spatial case-control approach. Accid. Anal. Prev., 2014, 62, 341–357.
  • Thakali, L., Kwon, T. J. and Fu, L., Identification of crash hotspots using kernel density estimation and kriging methods: a comparison. J. Mod. Transp., 2015, 23, 93–106.
  • Xie, Z. and Yan, J., Kernel density estimation of traffic accidents in a network space. Comput. Environ. Urban Syst., 2008, 32, 396–406.
  • Moons, E., Brijs, T. and Wets, G., Improving Moran’s index to identify hot spots in traffic safety. In Geocomputation and Urban Planning, Springer, 2009, pp. 117–132.
  • Cheng, W. and Washington, S. P., Experimental evaluation of hot-spot identification methods. Accid. Anal. Prev., 2005, 37, 870–881.
  • Okabe, A., Satoh, T. and Sugihara, K., A kernel density estimation method for networks, its computational method and a GIS-based tool. Int. J. Geogr. Inf. Sci., 2009, 23, 7–32.
  • Flahaut, B., Mouchart, M., San Martin, E. and Thomas, I., The local spatial autocorrelation and the kernel method for identifying black zones: a comparative approach. Accid. Anal. Prev., 2003, 35, 991–1004.
  • Anderson, T. K., Kernel density estimation and K-means clustering to profile road accident hotspots. Accid. Anal. Prev., 2009, 41, 359–364.
  • Steenberghen, T., Aerts, K. and Thomas, I., Spatial clustering of events on a network. J. Transp. Geogr., 2010, 18, 411–418.
  • Agrawal, R., Imielinski, T. and Swami, A., Database mining: a per-formance perspective. IEEE Trans. Knowl. Data Eng., 1993, 5, 914–925.
  • Kaur, M. and Kang, S., Market basket analysis: identify the chang-ing trends of market data using association rule mining. Procedia Comput. Sci., 2016, 85, 78–85.
  • Kim, J., Bang, C., Hwang, H., Kim, D., Park, C. and Park, S., IMA: Identifying disease-related genes using MeSH terms and association rules. J. Biomed. Inform., 2017, 76, 110–123.
  • Montella, A., Identifying crash contributory factors at urban round-abouts and using association rules to explore their relationships to different crash types. Accid. Anal. Prev., 2011, 43, 1451–1463.
  • Pande, A. and Abdel-Aty, M., Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool. Saf. Sci., 2009, 47, 145–154.
  • Marukatat, R., Structure-based rule selection framework for associ-ation rule mining of traffic accident data. In International Con-ference on Computational and Information Science, Springer, 2006, pp. 231–239.
  • Agrawal, R., Imielinski, T., and Swami, A., Mining association in large databases. In Proceedings of the 1993 ACM SIGMOD Inter-national Conference on Management of Data, Washington, DC, USA, 1993, pp. 207–216.
  • Yabing, J., Research of an improved a priori algorithm in data mining association rules. Int. J. Comput. Commun. Eng., 2013, 2, 25–27.
  • Schmidt, F. and Tiffin, J., Distortion of drivers’ estimates of auto-mobile speed as a function of speed adaptation. J. Appl. Psychol., 1969, 53, 536.
  • Fountas, G., Anastasopoulos, P. C. and Abdel-Aty, M., Analysis of accident injury-severities using a correlated random parameters ordered probit approach with time variant covariates. Anal. Methods Accid. Res., 2018, 18, 57–68.
  • Paleti, R., Eluru, N. and Bhat, C. R., Examining the influence of aggressive driving behavior on driver injury severity in traffic crashes. Accid. Anal. Prev., 2010, 42, 1839–1854.

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  • Crash risk factor identification using association rules in Nagpur city, Maharashtra, India

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Authors

Bahuguna Dalai
Civil Engineering Department, Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur 440 010, India
Vishrut S. Landge
Civil Engineering Department, Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur 440 010, India

Abstract


The increase in traffic volume in urban road networks poses a significant challenge to transportation safety. It is evident that different traffic zones experience unique crash patterns and severities. The different factors that affect crash rates are caused by the various character-istics of the drivers, weather conditions, design of road-side infrastructure and driving behaviour. Although studies have shown that various factors can affect crash rates, there are insufficient studies on the exact catego-rization of these factors. Accordingly, the present study focuses on traffic crashes on streets where the risks of an accident occurrence are higher, using Nagpur city, Maharashtra, India as a case study. Three levels of risk zones were selected, i.e. zone-I (low risk), zone-II (medi-um risk) and zone-III (high risk). The risk zones are created in ArcGIS software using the kernel density esti-mator function. The association rule was then used to find out the various crash risk factors within the zone. The results of the study reveal that the risk of pedestrian fatalities is higher in areas where the speed limit is more than 40 km/h and day-to-day pedestrian activity is pre-sent. Based on the results, we propose a lower speed limit in zone-I, in addition to providing pedestrian-crossing fa-cilities such as zebra crossings or refuge islands for cross-walks. Moreover, we propose implementing an awareness campaign for road traffic safety aimed at educating road users on how to follow road discipline, especially with regard to utilizing pedestrian facilities, aggressive young motorcyclists, lane changing and overtaking mano-euvres.

Keywords


Association rules, driver characteristics, risk factors, traffic crash, urban roads.

References





DOI: https://doi.org/10.18520/cs%2Fv123%2Fi6%2F781-790