<p> Aggressive driving behavior is the cause of a large percentage of accidents and fatalities, and it is growing every year. In several cases some drivers perceive their driving as non-aggressive when in fact they drive aggressively. To investigate factors affecting perceived (self-reported) and observed (based on the data from a driving simulation experiment) aggressive driving behavior, four fixed effect bivariate ordered probit models for three categories of aggressive driving behavior (i.e., observed and perceived non-aggressive, somewhat aggressive and very aggressive driving) are estimated. The models simultaneously account for panel data effects and cross equation error correlation. To further address unobserved heterogeneity, six grouped random parameter bivariate probit models for two outcomes (observed and perceived non-aggressive and aggressive driving) are estimated. Each model type is estimated using different barriers as driving behavior separators (either physical barriers in the distribution, or basic statistical measures). The results show that different socio-demographic characteristics, driving experience and exposure, and behavioral information of the participants affect the observed and the perceived aggressive driving behavior. The proposed approach, as a whole, provides an incremental step towards better understanding the different factors that affect the observed and the perceived aggressive driving behavior.</p>

<p> This research studied the behavior of several controllable variables that affect the fuel efficiency of trucks. Re-routing is the process of modifying the parameters of the routes for a set of trips to optimize fuel consumption and also to increase customer satisfaction through efficient deliveries. This is an important process undertaken by a food distribution company to modify the trips to adapt to the immediate necessities. A predictive model was developed to calculate the change in Miles per Gallon (MPG) whenever a re-route is performed on a region of a particular distribution area. The data that was used, was from the Dallas center which is one of the distribution centers owned by the company. A consistent model that could provide relatively accurate predictions across five distribution centers had to be developed. It was found that the model built using the data from the Corporate center was the most consistent one. The timeline of the data used to build the model was from May 2013 through December 2013. The predictive model provided predictions of which about 88% of the data that was used, was within the 0-10% error group. This was an improvement on the lesser 43% obtained for the linear regression and K-means clustering models. The model was also validated on the data for January 2014 through the first two weeks of March 2014 and it provided predictions of which about 81% of the data was within the 0-10 % error group. The average overall error was around 10%, which was the least for the approaches explored in this research. Weight, stop count and stop time were identified as the most significant factors which influence the fuel efficiency of the trucks. Further, neural network architecture was built to improve the predictions of the MPG. The model can be used to predict the average change in MPG for a set of trips whenever a re-route is performed. Since the aim of re-routing is to reduce the miles and trips; extra load will be added to the remaining trips. Although, the MPG would decrease because of this extra load, it would be offset by the savings due to the drop in miles and trips. The net savings in the fuel can now be translated into the amount of money saved.</p>