Car/Motorbike Drivers’ Willingness to Use and to Pay for Alcohol Interlock in Taiwan

This study explored the important factors affecting drunk car/motorbike drivers’ willingness to use and pay for alcohol interlocks. Data were obtained through a survey upon choice-based sampling conducted in central Taiwan. Questionnaires were distributed to the participants of drunk driving and road safety education courses from 17 August to 26 October 2020. All drunk drivers whose driver’s licenses are revoked for drunk driving are mandated to participate in this course. Prior to the survey, the researchers explained the questionnaires, instructed the participants to complete the questionnaires, and then collected all the questionnaires. The socioeconomic characteristics of drunk drivers, awareness of alcohol interlocks and drunk driving, drinking patterns and health self-assessment before and after drunk driving ban enforcement, and changes in the number of trips were investigated. This study applied the double-hurdle model for data analysis to estimate the variables affecting drunk car/motorbike drivers. Results indicate that the respondents who were classified by the Alcohol Use Disorders Identification Test as high-risk drinkers before and after drunk driving ban enforcement were more willing to use alcohol interlocks and to pay higher prices. Additionally, the respondents with declined health self-assessments were also more willing to use alcohol interlocks and pay higher prices. This study suggests offering subsidies for alcohol interlocks to families with financial difficulties, in order to increase the alcohol interlock installation rate. Moreover, since the current duration of license suspension and withdrawal is considerably long, drunk drivers avoid using and installing alcohol interlocks by reducing the number of trips. In other words, the willingness to install alcohol interlocks may be increased by reducing the duration of license suspension and withdrawal.

Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach

Background: Alcohol-related road-traffic injury is the leading cause of premature death in middle- and lower-income countries, including Thailand. Applying machine-learning algorithms can improve the effectiveness of driver-impairment screening strategies by legal limits. Methods: Using 4794 RTI drivers from secondary cross-sectional data from the Thai Governmental Road Safety Evaluation project in 2002–2004, the machine-learning models (Gradient Boosting Classifier: GBC, Multi-Layers Perceptrons: MLP, Random Forest: RF, K-Nearest Neighbor: KNN) and a parsimonious logistic regression (Logit) were developed for predicting the mortality risk from road-traffic injury in drunk drivers. The predictors included alcohol concentration level in blood or breath, driver characteristics and environmental factors. Results: Of 4974 drivers in the derived dataset, 4365 (92%) were surviving drivers and 429 (8%) were dead drivers. The class imbalance was rebalanced by the Synthetic Minority Oversampling Technique (SMOTE) into a 1:1 ratio. All models obtained good-to-excellent discrimination performance. The AUC of GBC, RF, KNN, MLP, and Logit models were 0.95 (95% CI 0.90 to 1.00), 0.92 (95% CI 0.87 to 0.97), 0.86 (95% CI 0.83 to 0.89), 0.83 (95% CI 0.78 to 0.88), and 0.81 (95% CI 0.75 to 0.87), respectively. MLP and GBC also had a good model calibration, visualized by the calibration plot. Conclusions: Our machine-learning models can predict road-traffic mortality risk with good model discrimination and calibration. External validation using current data is recommended for future implementation.

Factors Affecting Recidivism of Drunk Driving for Car and Motorbike Users

This study explored the important factors affecting the recidivism rate of drunk driving for car and motorbike users. The respondents were students of Taiwan’s road safety training course, which was required for all drunk drivers who were suspended from driving due to the violation of regulations. The characteristics of the drunk car and motorbike drivers, such as socioeconomic variables, alcohol consumption changes, family life cycle, and changes in the number of trips, were investigated. This study estimated the models affecting the recidivism rate of drunk driving for car and motorbike users with the logistic regression model. The main variables included drivers with a university degree or above who tend not to be recidivists compared to the drivers without one. Such respondents are more willing to avoid the risk of becoming drunk driving recidivists. Moreover, the variables of alcohol use disorders’ identification test (AUDIT), breath alcohol concentration, and frequency of drunk driving all significantly affect the possibility of recidivism. In terms of family life cycle, married respondents with children aged between 1 and 5 are less likely to become drunk driving recidivists. Those who take motorbikes as an alternative vehicle after being suspended from driving cars are more likely to become drunk driving recidivists. This study suggests the measures of suspending or withdrawing car and motorbike driver’s licenses at the same time, using alcolocks to restrict the right to drive, and increasing the frequency of drunk driving crackdowns. In addition, in terms of alcohol consumption behaviors, drinkers with high risks and drunk drivers with high breath alcohol concentrations should be regarded as the key targets for future tracking in order to avoid drunk driving recidivism.

Drunk Drivers’ Willingness to Use and to Pay for Designated Drivers

While drunk driving accidents, which are a serious problem in Taiwan, have decreased in recent years, cases of drunk driving continue to emerge endlessly, and are a source of traffic risks even when the accidents cause no injuries. In order to prevent drunk driving and reduce car accidents, the government has made laws stricter, and has vigorously promoted “designated drivers”. As the concept of designated drivers is not common in Taiwan, this study mainly explores drunk drivers’ understanding of designated drivers in Nantou County and Taichung City, and investigates the willingness of drunk drivers to use and to pay for designated driving services. This study conducted a questionnaire survey on the drunk drivers of the drunk driving and traffic safety training course held at the Motor Vehicles Office. Double-hurdle and tobit models were applied to investigate the issues mentioned above. According to the test results, the tobit model was superior to the double-hurdle model. The estimation results indicated that distance, age, income, family conditions, and drinking habits influence the willingness to use and to pay for designated drivers. Gender, age, family background, and experience in designated driving cause differences in the willingness to use designated drivers in the two regions. It is expected that the conclusion of this study could provide a direction and reference for the future improvement of designated driving services.

State or market? How to effectively decrease alcohol-related crash fatalities and injuries

BackgroundIt is estimated that more than 270 000 people die yearly in alcohol-related crashes globally. To tackle this burden, government interventions, such as laws which restrict blood alcohol concentration (BAC) levels and increase penalties for drunk drivers, have been implemented. The introduction of private-sector measures, such as ridesharing, is regarded as alternatives to reduce drunk driving and related sequelae. However, it is unclear whether state and private efforts complement each other to reduce this public health challenge.MethodsWe conducted interrupted time-series analyses using weekly alcohol-related traffic fatalities and injuries per 1 000 000 population in three urban conglomerates (Santiago, Valparaíso and Concepción) in Chile for the period 2010–2017. We selected cities in which two state interventions—the ‘zero tolerance law’ (ZTL), which decreased BAC, and the ‘Emilia law’ (EL), which increased penalties for drunk drivers—were implemented to decrease alcohol-related crashes, and where Uber ridesharing was launched.ResultsIn Santiago, the ZTL was associated with a 29.1% decrease (95% CI 1.2 to 70.2), the EL with a 41.0% decrease (95% CI 5.5 to 93.2) and Uber with a non-significant 28.0% decrease (95% CI −6.4 to 78.5) in the level of weekly alcohol-related traffic fatalities and injuries per 1 000 000 population series. In Concepción, the EL was associated with a 28.9% reduction (95% CI 4.3 to 62.7) in the level of the same outcome. In Valparaíso, the ZTL had a −0.01 decrease (95% CI −0.02 to −0.00) in the trend of weekly alcohol-related crashes per 1 000 000 population series.ConclusionIn Chile, concomitant decreases of alcohol-related crashes were observed after two state interventions were implemented but not with the introduction of Uber. Relationships between public policy interventions, ridesharing and motor vehicle alcohol-related crashes differ between cities and over time, which might reflect differences in specific local characteristics.