This study provides a comprehensive literature review to summarize all contributing factors and the logit-based models that were used to predict the severity of crashes. Using the General Estimates Systems (GES) dataset, as a subset and a branch of the National Automotive Sampling System in the US, a Generalized Ordered Logit (GLM) model is developed to predict the crash severity. The developed severity model detects the most important parameters based on characteristics of the driver, the environment, the vehicle, the road, and the type of crash. This study aims to take a more in-depth look into the distraction-related factors as one of the most important groups of contributing factors to traffic crashes. Distraction-related factors are categorized into five groups based on the generating source, including cellular phone, cognitive, passenger, outside events, and in-vehicle activities. Moreover, the effect of distraction on crashes in the presence of other factors is studied. Analyzing the severity of crashes revealed that cell phone usage and distraction caused by in-vehicle activities increase the severity of crashes, whereas other factors of distraction decrease the severity.

The use of cell phone is a significant source of driver distraction. Phone use while driving can impair a number of factors critical for safe driving which can cause serious traffic safety problems. The objective of this paper was to investigate the frequency of using cell phones while driving in Iran's roads through an observational survey with a random sample of drivers, to recognize contributing factors to cell phone usage and to understand the magnitude of the problem. A total of 1794 observations were collected from 12 sites at controlled intersections, entrance and exit points of highways. The cell phone use rate among drivers (talking or texting) was estimated at 10% which is significantly higher than that in other countries such as Australia, USA and Canada. Rate of cell phone use among younger drivers (14.15%) was higher in comparison with other groups. In order to identify factors affecting cell phone use while driving, a binary logit model is estimated. Variables which significantly contribute to the rate of using cell phone were found to be the age of driver, number of passengers, presence of kids under the age of 8, time of observation, vehicle price and type of car.

This work aims to study factors, such as driver characteristics, environmental conditions, and vehicle characteristics, that affect different crash types with a special focus on distraction parameters. For this purpose, distraction factors are divided into five groups: cellphone usage, cognitive distractions, passengers distracting the driver, outside events attracting the driver’s attention, and in-vehicle activities. Taking the crashes that occurred in the USA into account, the crash types are divided into two main groups, single-vehicle crashes and two-vehicle crashes. Since there were different crash types (alternatives) in the dataset and the probable correlation in the unobserved error term, the Nested Logit model is developed. The results of model illustrate that all of the aforementioned distraction-related factors increase the probability of run-off-road crashes, collision with a fixed object, and rear-end crashes. Cognitive distraction increases the probability of collision with a pedestrian. Distractions caused by passengers or out-of-vehicle events increase the probability of sideswipe crashes. By examining how a factor affects multiple crash type outcomes, it is possible to devise countermeasures, improvements to roadway geometry, and traffic control strategies, while minimizing unintended consequences. The results should be of value in the design of educational programs and propose road safety improvement techniques.

This research aims to study application of support vector machine algorithm, artificial neural networks and five different types of decision trees in predicting mode choice of freight transportation. Performance of these models has been compared with log it model which is one the most prevalent statistical models in the field. Effect of factors such as cargo weight, distance, type and characteristics of commodity has been studied in process of modelling mode choice which is rail and road. In this regard, data gathered in the United States, is used and similarities and advantages of the models are described in details. Results indicated that cost-sensitive support vector machine is the best method in predicting shipment mode choice. After this method, stand C5 decision tree and artificial neural network. The most important variables in determining shipment mode choice of firms are respectively weight, great-circle distance between origin and destination, commodity type, compound impedance factor of rail and truck and containerized condition of the shipment to be moved.