Human error including driving misbehavior contributes to over 90 percent of road vehicle accidents, and speeding is considered to be risky. Smart technologies, such as Connected Vehicle System (CVS) are among the interesting technical options to improve driving behavior, and Pay-As-You-Speed (PAYS) is an effective economic incentive to reduce speed violations. We investigated the acceptability of CVS with and without the presence of economic incentives, such as PAYS, in the context of a middle-income country: Iran. We used a Zero-Inflated Ordered Probit model (ZIOP) to estimate drivers’ willingness to pay for a CVS, and a hazard-based model for predicting the incentive level needed for accepting CVS via a PAYS scheme. ZIOP model indicated that drivers with the following characteristics were more likely to pay more for CVS: having a comprehensive insurance coverage, being younger than 60 years, owning more than one car, and having older vehicles. The hazard-based model also confirmed that drivers that speed relatively often have a lower tendency to adopt CVS, and drivers who experienced an accident in the past were more inclined to adopt CVS via PAYS. Also, drivers' opinion about CVS, vehicle characteristics, demographics, and driving experience influenced the effect of PAYS characteristics on acceptability of CVS. Finally, we offer recommendations for how to effectively implement CVS, in order to significantly reduce the high fatality and accident rates in middle-income countries such as Iran.

There are relatively few comprehensive studies on driving errors and violations in Iran, a non-Western country with a high traffic fatality rate. In this study, 712 drivers completed a questionnaire at technical inspection centres and carwashes in Tehran, Iran. Respondents were asked about their demographic characteristics, accident involvement, traffic fines, and driving aberrations in the form of the Driver Behaviour Questionnaire (DBQ). The results of a principal component analysis of the DBQ showed a distinction between errors and two types of violations: speeding and non-speeding violations. Correlation analyses showed that DBQ violations were associated with a higher driving mileage, a higher education level (for DBQ speeding violations in particular), and younger age. DBQ errors were associated with risk perception, that is, the belief that one has a high probability of becoming involved in a car accident. Regression analyses showed that the DBQ speeding violations score was predictive of the number of speeding tickets and that the DBQ non-speeding violations score was predictive of involvement in minor accidents in the past three years. A correlation analysis at the level of municipal districts showed that drivers from districts with lower education and literacy levels and lower car ownership were more likely to report driving a low-cost car and had lower DBQ violations scores. These results can be interpreted as indicating that affluence enables deviant driving. We conclude that the error-violation distinction is of relevance to road safety in Tehran, both at the level of individual drivers and at the level of districts.