Urban arterials connect multiple areas in the city and encourage non-motorist activities. Hence, the safety and operations on urban arterials is vital as they improve the mobility of daily commuters and road users. This research aims to facilitate traffic operations on urban arterials by proposing multiple mythological approaches to estimate and predict turning movement counts at signalized intersections using traffic data from adjacent intersections. Further, it aims to improve the safety by developing crash prediction models, identifying the hotspots for multiple crash types, and indicating the factors contributing to operating speed as well as non-motorist crashes. The analyses included tuning, testing, and comparing multiple parametric and machine learning models. First, a framework was proposed to estimate cycle-level turning movements' counts at signalized intersections based on traffic data from adjacent intersections. As a result, generic Extreme Gradient Boosting (XGBoost) models were developed to estimate through and left turn movements with Mean Absolute Error (MAPE) 9.53% and 4.7%, respectively. Afterwards, multiple machine learning models were trained and compared to predict through and left turning movements. The GRU models outperformed other developed models and were able to provide accurate time horizon predictions for five cycles in the future. The developed models for estimation and prediction could emulate detection systems at signalized intersections, improve traffic signals optimization, assist in corridor management and safety, and save capital by eliminating the need for equipment investment at many intersections. On the other hand, aiming to improve road users' safety on urban arterials, this research proposed an integrated approach to identify the hotspots by developing crash prediction models (Safety Performance Functions) considering context classification. The study utilized big data and compared a wide array of statistical and machine learning models that were developed to estimate reliable non-motorist exposure. The results indicated that XGBoost is the best model to estimate non-motorist exposure at intersections and along the roadway segments. Further, the proposed approach included developing Safety Performance Functions (SPFs) to identify the hotspots for two types of crashes (i.e., vulnerable road users' crashes at intersections and bike crashes along the road). It was found that the hotspots were more likely to be located near the city of Orlando. Coastal roadways were classified as cold categories regarding bike crashes. Finally, the research investigated the factors contributing to operating speed considering context classifications. The analyses indicated the significant factors that influence operating speed or non-motorist crashes such as average block length, shoulder width, proportion of population below poverty, and number of signalized intersections per mile. It also illustrated the potential speed management countermeasures that significantly influence the operating speed. These countermeasures could have potential influence on roadway safety if implemented. This could help the decision makers to determine the best countermeasures to be implemented along roadway segments for different context classification roads.


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Graduation Date





Abdel-Aty, Mohamed


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science


Civil, Environmental, and Construction Engineering

Degree Program

Civil Engineering




CFE0008495; DP0024171





Release Date


Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)