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      Factors affecting motorcycle crash casualty severity at signalized and non-signalized intersections in Ghana: Insights from a data mining and binary logit regression approach.

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          Abstract

          Despite the countless benefits derived from motorcycle usage, it has become a significant public health concern, particularly in developing countries, due to the plateauing number of fatal/serious injuries associated with them. Although it has been well documented that the frequency and fatality rates of intersection-related motorcycle crashes are high, little research efforts have been made to explore the contributory factors influencing motorcycle-involved crashes at these locations. Interestingly, no study has investigated the latent patterns and chains of factors that simultaneously contribute to the injury severity sustained by motorcycle crash casualties at intersections under different traffic control conditions in developing countries. Since motorcycles are mostly used as taxis in developing countries, it is imperative to consider the injury severity sustained by all crash casualties in the motorcycle safety analysis. This study bridges the research gap by employing a plausible data mining tool to explore hidden rules associated with motorcycle crash casualty injury severity outcomes at both signalized and non-signalized intersections in Ghana's most densely populated region, Accra, using three-year crash data spanning 2016-2018. Besides, a binary logit regression model was also employed to explore the impact of crash factors on casualty severity outcomes using the same dataset. The results from both analysis techniques were consistent; however, the data mining technique provided chains of factors which provided additional insights into the groups of factors that collectively influence the casualty injury severity outcomes. From the rule discovery results, while full license status, daytime/daylight, and shoulder presence increased the risk of fatal injuries at signalized intersections, factors such as inattentiveness, good road surface, nighttime, shoulder absence, and young rider were highly likely to increase casualty fatalities at non-signalized intersections. By controlling all or some of these risk factors, the level of injury severity on the roadways could be reduced. Based on the findings, we provide enforcement, education, and engineering-based recommendations to help improve motorcycle safety.

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          Author and article information

          Journal
          Accid Anal Prev
          Accident; analysis and prevention
          Elsevier BV
          1879-2057
          0001-4575
          Feb 2022
          : 165
          Affiliations
          [1 ] Department of Transportation Engineering, The University of Seoul, 163 Seoulsiripdae-ro Dongdaemun-gu, Seoul 02504, South Korea. Electronic address: drtamakloe@uos.ac.kr.
          [2 ] Texas A&M Transportation Institute, College Station, TX 77843, USA. Electronic address: s-das@tti.tamu.edu.
          [3 ] Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. Electronic address: en.aidoo@yahoo.com.
          [4 ] Department of Transportation Engineering & Department of Urban Big Data Convergence, The University of Seoul, 163 Seoulsiripdae-ro Dongdaemun-gu, Seoul 02504, South Korea. Electronic address: djpark@uos.ac.kr.
          Article
          S0001-4575(21)00548-0
          10.1016/j.aap.2021.106517
          34896907
          fd5c5a85-00e6-4743-9838-2e7d8d7d809b
          Copyright © 2021 Elsevier Ltd. All rights reserved.
          History

          Intersection,Injury severity,Developing countries,Data mining,Safety,Motorcycle

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