The dissertation aims to classify drivers based on driving and environmental behaviors. The research determined significant factors using factor analysis, identified different driver types using k-means clustering, and studied how the same drivers map in each classification domain. The research consists of two study cases. In the first study case, a new variable is proposed and then is used for classification. The drivers were divided into three groups. Two alternatives were designed to evaluate the environmental impact of driving behavior changes. In the second study case, two types of data sets were constructed: driving data and environmental data. The driving data represents driving behavior of individual drivers. The environmental data represents emissions and fuel consumption estimated by microscopic energy and emissions models. Significant factors were explored in each data set using factor analysis. A pair of factors was defined for each data set. Each pair of factors was used for each k-means clustering: driving clustering and environmental clustering. Then the factors were used to identify groups of drivers in each clustering domain. In the driving clustering, drivers were grouped into three clusters. In the environmental clustering, drivers were clustered into two groups. The groups from the driving clustering were compared to the groups from the environmental clustering in terms of emissions and fuel consumption. The three groups of drivers from the driving clustering were also mapped in the environmental domain. The results indicate that the differences in driving patterns among the three driver groups significantly influenced the emissions of HC, CO, and NOx. As a result, it was determined that the average target operating acceleration and braking did essentially influence the amount of emissions in terms of HC, CO, and NOx. Therefore, if drivers were to change their driving behavior to be more defensive, it is expected that emissions of HC, CO, and NOx would decrease. It was also found that spacing-based driving tended to produce less emissions but consumed more fuel than other groups, while speed-based driving produced relatively more emissions. On the other hand, the defensively moderate drivers consumed less fuel and produced fewer emissions. / Ph. D.

The purpose of this thesis was to perform microsimulation analyses on intersections near Highway-Railroad Grade Crossings (HRGCs) to determine if controlling mean train speed and train speed variability would improve safety and reduce delays. This research focused on three specific areas. First, average vehicle delay was examined, and this delay was compared for seven specific train speed distributions, including existing conditions. Furthermore, each distribution was associated with train detectors that were placed at the distance the fastest train could travel during the given warning time. Second, pedestrian cutoffs were investigated. These cutoffs represented an occasion when the pedestrian phases were truncated or shortened due to railroad signal preemption. Finally, vehicle emissions were analyzed using a modal emissions model. A microscopic simulation model of the Wellborn Corridor in College Station, Texas was created using VISSIM. The model was run twenty times in each train speed distribution for each of three train lengths. Average vehicle delay was collected for three intersections, and delays were compared using the Pooled t-test with a 95% confidence interval. Comparisons were made between the distributions, and generally, distributions with higher mean train speeds were associated with lower average delay, and train length was not a significant factor. Unfortunately, pedestrian cutoffs were not specifically controlled in this project; therefore, no statistical conclusions can be made with respect to the pedestrian cutoff problem. However, example cases were devised to demonstrate how these cutoffs could be avoided. In addition, vehicle emissions were examined using the vehicle data from VISSIM as inputs for CMEM (Comprehensive Modal Emissions Model). For individual vehicles, as power (defined as the product of velocity and acceleration) increased, emissions increased. When comparing emissions from different train speed distributions, few significant differences were found. However, a scenario with no train was tested, and it was shown to have significantly higher emissions than three of the distributions with trains. Ultimately, this thesis shows that average vehicle delay and vehicle emissions could be lowered by specific train speed distributions. Also, work could be done to investigate the pedestrian cutoff problem.

The purpose of this thesis was to perform microsimulation analyses on intersections near Highway-Railroad Grade Crossings (HRGCs) to determine if controlling mean train speed and train speed variability would improve safety and reduce delays. This research focused on three specific areas. First, average vehicle delay was examined, and this delay was compared for seven specific train speed distributions, including existing conditions. Furthermore, each distribution was associated with train detectors that were placed at the distance the fastest train could travel during the given warning time. Second, pedestrian cutoffs were investigated. These cutoffs represented an occasion when the pedestrian phases were truncated or shortened due to railroad signal preemption. Finally, vehicle emissions were analyzed using a modal emissions model. A microscopic simulation model of the Wellborn Corridor in College Station, Texas was created using VISSIM. The model was run twenty times in each train speed distribution for each of three train lengths. Average vehicle delay was collected for three intersections, and delays were compared using the Pooled t-test with a 95% confidence interval. Comparisons were made between the distributions, and generally, distributions with higher mean train speeds were associated with lower average delay, and train length was not a significant factor. Unfortunately, pedestrian cutoffs were not specifically controlled in this project; therefore, no statistical conclusions can be made with respect to the pedestrian cutoff problem. However, example cases were devised to demonstrate how these cutoffs could be avoided. In addition, vehicle emissions were examined using the vehicle data from VISSIM as inputs for CMEM (Comprehensive Modal Emissions Model). For individual vehicles, as power (defined as the product of velocity and acceleration) increased, emissions increased. When comparing emissions from different train speed distributions, few significant differences were found. However, a scenario with no train was tested, and it was shown to have significantly higher emissions than three of the distributions with trains. Ultimately, this thesis shows that average vehicle delay and vehicle emissions could be lowered by specific train speed distributions. Also, work could be done to investigate the pedestrian cutoff problem.

Traffic and parking congestion are significant issues at many universities nationwide. The delays experienced result in wasted time, money, and fuel for students, faculty and staff, not to mention the negative contributions to the environment. This paper quantifies the amount of vehicle emissions generated during an average morning peak hour in the university environment. Using VISSIM and CMEM microsimulation packages, a model is created for California Polytechnic State University- San Luis Obispo to aggregate the collective transportation behaviors and practices of the campus and recognize the implications these behaviors pose on the transportation network as a whole. Reasonable estimates are generated for overall HC, CO, and NOx type emissions as well as fuel consumption. Scenarios are proposed which reflect the sensitivity of outputs to key input parameters. The findings of this research can be useful for future campus planning and the ideas can be extended to similar environments with traffic and parking problems such as business parks, corporate campuses, downtown districts, and special event venues.