This thesis is a collection of poems.

Recognizing the distinct non-motorist injury severity profiles by crash location (segment or intersection), we propose a joint modeling framework to study crash location type and non-motorist injury severity as two dimensions of the severity process. We employ a copula-based joint framework that ties the crash location type (represented as a binary logit model) and injury severity (represented as a generalized ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The data for our analysis is drawn from the Central Florida region for the years of 2015 to 2021. The model system explicitly accounts for temporal heterogeneity across the two dimensions. A comprehensive set of independent variables including non-motorist user characteristics, driver and vehicle characteristics, roadway attributes, weather and environmental factors, temporal and sociodemographic factors are considered for the analysis. We also conducted an elasticity analysis to show the actual magnitude of the independent variables on non-motorist injury severity at the two locations. The results highlight the importance of examining the effect of various independent variables on non-motorist injury severity outcome by different crash locations.

The aim of this thesis Analysis of Czech television automotive magazines is to describe characteristic elements of television format entitled motoring (or automotive) magazine. This thesis defines the basic terms typical for the issue, describes the history of motoring magazines in the Czech television environment and through the use of content analysis determines content of today's motoring magazines broadcasted in the Czech television environment. Results of the content analysis are afterwards explained by analysis of historical documents and by an interview with one of the authors of television motoring magazine. The content of the magazines (Auto Moto Revue, Autosalon and Garáž) is also aligned with the British motoring show Top Gear and with the original form of Auto Moto Revue, which ranks among the oldest television motoring magazines in the world.

<p>  </p> <p>The research described in this dissertation began in response to frequent questions from users of several crosswalks near a university campus. At each crosswalk was a sign indicating that motorists should yield to pedestrians in the crosswalk. That this message was not being interpreted uniformly was a concern at locations where heterogeneous road users (pedestrians, cyclists, and motorists) were interacting. Instead of trying to impose a single interpretation on users of each crosswalk, it was decided to observe and analyze interactions between users of the crosswalk. </p> <p>Several hours of video were recorded of pedestrians and motorists “negotiating” the right of way at the crosswalk. Because these crossing locations were marked but not signalized, they were called “semi-controlled crosswalks”. The negotiations took place during what were called pedestrian-motorist interactions (PMIs). The PMIs observed on video can be characterized as a “zebra-crossing” game, as described in Chapter 4 of this dissertation. </p> <p>Recently, computer vision (CV) algorithms have been extensively used in road users’ detection and tracking at an unparalleled spatial-temporal scale. In this study, CV algorithms have been applied to convert the video recordings into a large-scale spatial-temporal trajectory dataset including 800 pedestrians and cyclists interacting with more than 500 vehicles. Utilizing the trajectory dataset, a spatial-temporal graph convolutional network-based sequence to sequence (ST-GCN-Seq2Seq) algorithm has been developed to reasonably forecast heterogeneous road users’ trajectories and behavior in real time. Combining CV and ST-GCN-Seq2Seq algorithms can help both design an intelligent tracking system and achieve a form of “smart” interaction at semi-controlled crosswalks for heterogeneous road users.</p> <p>Based on road users’ arrival patterns detected from CV algorithms, it is likely that a "smart" control strategy can minimize the delay of pedestrians and motorists at crosswalks.  Therefore, another branch of this study is to investigate the “smart” control strategies at crosswalks using traffic signal controllers. A reinforcement learning framework was proposed as the “smart” control strategy, and several experiments were conducted using microsimulation. The proposed reinforcement learning framework is able to reduce traffic delay (efficiency), considering real-time pedestrian flow rates and vehicle flow rates with appropriate sensors.</p>

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