Differences in the risk profiles of drunk and drug drivers: Evidence from a mandatory roadside survey

A knowledge elicitation approach to traffic accident analysis in open data: comparing periods before and after the Covid-19 outbreak

Extracting knowledge from open data of traffic accidents has been attracting increasing attention to policymakers responsible for road safety. This article presents a knowledge elicitation approach to exploring the determinants of traffic accidents from open government data of an urban area in Taiwan. The collected open dataset contains 34 decisional attributes and one predictive attribute (i.e., type of injury, including head, breast, leg), and 47,974 cases. Prediction models using a classification-oriented mechanism and generated rules that considered datasets from before (B-dataset; 30,116 cases) and after (A-dataset; 17,868 cases) beginning to combat the Covid-19 pandemic in an urban area of Taiwan were compared. The findings showed that prediction accuracy was acceptable but not high, at 70.73% for B-dataset and 74.77% for A-dataset. Determinants in the human and vehicle categories revealed higher classification ranks than those in the temporal and environment categories. Traffic accidents involving motorcycles were 5.13% higher in A-dataset, whereas those involving cars were 4.11% lower. Injury on leg or foot was 3.46% higher in A-dataset, whereas other types of injury were up to 1.00% lower. The average support for rules in the A-dataset rule base and the simplicity of the A-dataset decision tree were higher than those of B-dataset. The research demonstrates the value of open government data in prediction model development and knowledge elicitation to support policymaking in the traffic safety domain.

A systematic review of factors associated with illegal drug driving

Drug driving is a serious problem worldwide that can increase the risk of road crashes. This systematic review seeks to identify factors associated with drug driving (i.e., driving after consuming drugs other than alcohol) to highlight gaps in existing knowledge and inform the design of more effective countermeasures. A search of the literature was conducted for the period January 1, 2005 to July 31, 2021 using six different databases. The search protocol followed PRISMA guidelines and was registered in PROSPERO (#CRD42021234616). Studies that met inclusion criteria compared drug drivers with either non-drug drivers, alcohol-only drivers or drug drivers from an earlier time period, to identify factors specifically associated with drug driving, rather than common to all drivers. Two hundred and nineteen publications met the inclusion criteria and were included within the review. Based on the findings, a logic model was developed that presents the factors associated with drug driving. Various sociodemographic, psychosocial and legal factors emerged as the main factors associated with illegal drug driving. At the sociodemographic and psychological levels, drug drivers were more likely to be single, young males who often drive after using cannabis and who score high on sensation-seeking and impulsivity scales. The key social factor found to be associated with drug driving was peer acceptance/disapproval of the behaviour. At the legal level, the review suggested that the effectiveness of current enforcement approaches to drug driving vary among jurisdictions around the world due to differences in the level of perceived certainty of apprehension and the chances of punishment avoidance. Future research into the anticipated and actual rewards for drug driving is needed to inform the development of more effective countermeasures.