Human error is one of the leading causes of accidents. Distraction, fatigue, poor visibility, speeding, and other such errors made by drivers can cause accidents. With the rapid advancements in automation technologies, transportation planners have strived to use Intelligent Transportation Systems (ITS) to minimize human error. In this study, the effect of Autonomous Vehicles (AVs) on the number of potential conflicts at two unsignalized intersections is investigated by using a microsimulation model in PTV Vissim software. For human-driven cars, the factor that is considered for calibration is driver distraction mainly caused by reading or writing text messages on a cellphone while driving. This factor can be estimated using driving simulators. In this paper, five different scenarios were defined for simulation, in addition to the primary state, according to the different market penetration rates of AVs in Vissim. Safety assessment was performed by the Surrogate Safety Assessment Model (SSAM) using Time to Collision (TTC) and Deceleration Rate to Avoid Crashes (DRAC) indicators to determine the number of accidents. It was concluded that the presence of 100% of AVs could reduce the potential for accidents by up to 93%.

The serial correlation in the time series datasets should be considered to prevent biased estimates for coefficients. Nonetheless, the current models almost cannot explicitly handle autocorrelation and seasonality, and they focus mainly on the discrete nature of data. Nonetheless, the crash time series follows a normal distribution at the macro-scale. Moreover, the influential exogenous variables have been overlooked in Iran, employing univariate models. There are also contradictory results in the literature regarding the effect of average speed on crash frequency. This study is aimed to evaluate the distinct impacts of mean speed on total and fatal accident time series at the national level. Besides, the SARIMAX modeling framework is introduced as a robust multivariate method for short-term crash frequency prediction. To this end, monthly total and fatal crash counts were aggregated for all rural highways in Iran. Besides, the time trends of traffic exposure, and average speed recorded by loop detectors, were aggregated at the same level as covariates. The Box-Jenkins methodology was employed for time series analysis. The results illustrated that the seasonal autoregressive integrated moving average with explanatory variable (SARIMAX) model outperformed the univariate ARIMA and SARIMA models. Also, SARIMA was more appropriate than the simple ARIMA when seasonality existed in the time series. Besides, the average speed had a negative linear association with the total crashes. In contrast, it revealed an increasing effect on fatal crashes. Average speed has a dissimilar effect on the different traffic crash severities. Besides, the seasonal nature of data and the dynamic effects of the influential underlying factors should be considered to prevent underfitting issues and to predict future time trends accurately. The developed instruments could be employed by policymakers to evaluate the intervention's effectiveness and to forecast the future time trends of accidents in Iran.

This study aimed to introduce the random forest (RF) method as a valuable tool for short-term crash frequency prediction. Besides, the study compares the forecast efficiency of the RF model with th...

Traffic collisions are one of the most important challenges threatening the general health of the world. Iran’s crash statistics demonstrate that approximately 16,500 people lose their lives every year due to road collisions. According to the traffic police of Iran, heavy vehicles (including trailers, trucks, and panel trucks) contributed to 20.5% of the fatal road traffic collisions in the year 2013. This highlights the need for devoting special attention to heavy vehicle drivers to further explore their driving characteristics. In this research, the effect of heavy vehicle drivers’ behavior on at-fault collisions over three years has been investigated with an innovative approach of structural equation modeling (SEM) and Bayesian Network (BN). The database utilized in this research was collected using a questionnaire. For this purpose, 474 heavy vehicle drivers have been questioned in the Parviz Khan Border Market, located on the border of Iran and Iraq. The response rate of the survey was 80%. The participants answered the questions on Driver Behavior Questionnaire (DBQ) and a sleep assessing questionnaire named Global Dissatisfaction with Sleep (GSD). In this research, human factors affecting at-fault collisions of heavy vehicles were identified and their relationships with other variables were determined using the SEM approach. Then the descriptive model constructed by the SEM method was used as the basis of the BN, and the conditional probabilities of each node in the BN were calculated by the database collected by the field survey. SEM indicates that other attributes including GSD, mobile usage, daily fatigue, exposure, and education level have an indirect relation with heavy vehicle drivers’ at-fault collisions. According to the BN, if there is no information about the characteristics of a heavy vehicle driver, the driver will likely have at least one collision during the next three years with the probability of 0.17. Also, it was indicated that the minimum probability of the at-fault collision occurrence for a heavy vehicle is 0.08.

This paper aims to examine the key factors influencing driving risk perception in Iran. We conducted separate surveys for two groups of Iranian drivers, namely passenger car drivers and truck drivers. In order to assess driving risk perception, respondents were asked what they think about their Probability of Having a Road Accident (PHRA) and if they eventually have an accident as a driver, what they think about the Probability of it being Fatal or causing Severe Injury (PFSI). A Bivariate Ordered Probit model, which considers the possible correlation between PHRA and PFSI, was developed to explain the observed driving risk perception using type of vehicle, driving experience, socio-demographic information, and driving behaviour. According to the results, vehicle type, vehicle age, driving experience, sleep quality, at-fault accidents over the past three years, vehicles safety-related equipment, and education level have significant effects on driving risk perception (p-value < 0.05). In addition, this paper compares the driving risk perception of truck and passenger car drivers. The results show that truck drivers have a higher perception of PHRA and PFSI compared with passenger car drivers (p-value < 0.05). The results may convince policy-makers to consider the characteristics of the two categories of drivers when designing regulations.