Severity of motorcycle crashes in Dar es Salaam, Tanzania

Traffic Inj Prev. 2019;20(2):189-195. doi: 10.1080/15389588.2018.1544706. Epub 2019 Mar 19.

Abstract

Objective: Motorcycles are a common mode of transportation in low- and middle-income countries. Tanzania, in particular, has experienced an increased use of motorcycles in the last decade. In Dar es Salaam, motorcycles provide door-to-door travel and often operate where more conventional services are uneconomical or physically impossible to maneuver. Although motorcycles play a crucial role in improving mobility in the city, they have several safety issues. This study focuses on identifying factors influencing the severity of motorcycle crashes.

Method: A multinomial logit analysis was conducted to identify factors influencing the severity of motorcycle crashes in Dar es Salaam, Tanzania. The severity categories were fatal, severe injury, minor injury, and property damage only (PDO). The analysis was based on a total of 784 motorcycle crashes that occurred from 2013 to 2016.

Results: The following factors were found to increase the probability of a fatality: Speeding, driving under the influence, head-on impact, presence of horizontal curves, reckless riding, off-peak hours, violations, and riding without a helmet. The results indicate that crashes occurring on weekdays, during peak hours, at intersections, involving a rear-end impact, in daylight, on street roads, and under clear weather conditions decrease the probability of a fatality. However, minor injury and PDO crashes were found to be associated with crashes occurring during peak hours, at intersections, and on street roads, as well as failure to yield right-of-way.

Conclusions: Several countermeasures are recommended based on the study findings. The recommended countermeasures focus on the holistic safety improvement strategies constituting the three Es of highway safety, namely, engineering, education, and enforcement.

Keywords: Motorcycle crashes; crash injury severity; developing countries; multinomial logit model.

MeSH terms

  • Accidents, Traffic / statistics & numerical data*
  • Adult
  • Aged
  • Built Environment / statistics & numerical data
  • Driving Under the Influence / statistics & numerical data
  • Female
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Motorcycles / statistics & numerical data*
  • Probability
  • Risk Factors
  • Risk-Taking
  • Tanzania / epidemiology
  • Trauma Severity Indices
  • Weather
  • Young Adult