Stormwater road runoff is a widespread non-point source of contaminants such as nutrients, which endangers water bodies, especially in vulnerable karst areas such as Florida. While roadside vegetated filter strips (VFSs) and stormwater basins are generally accepted best management practices (BMPs) for stormwater management, uncertainties about VFS nutrient removal are reported and stormwater basins are concerned of facilitating contaminant transport. In this dissertation, the application and efficacy of engineered infiltration media was tested as a subgrade for the enhanced nutrient removal from roadway runoff. Results of field-scale laboratory testing indicated that a VFS with engineered biosorption activated media (BAM) outperformed a Control with sandy soil concerning nitrate removal (mean 94±6% reduction vs. 23±64% increase) and total nitrogen removal (mean 80±5% vs. 38±23% reduction) within a 6 m filter width. However, BAM and soil performed similarly with respected to total phosphorus removal within the first 1.5 m filter width (84±9% vs. 82±12% reduction). Next, field sampling was conducted to characterize nutrient load and delivery in stormwater road runoff in different events, providing insights to improve design of BMPs. Three types of runoff events were characterized, where nutrients are transported differently under the controls of nutrient supply and transport conditions. Antecedent dry period was strongly related to nutrient supply and runoff volume was correlated to nutrient transport capacity. Finally, the configuration of the subsurface in stormwater basins and runoff movement to and within karst aquifer near Silver Springs in central Florida were investigated using geophysical surveys (ground penetrating radar and frequency domain electromagnetics) and tracer tests. Numerous subsurface anomalies and surface sinkholes were detected in the basins. High groundwater velocities in the surficial aquifer (10-6 to 10-3 ms-1) and Upper Floridan Aquifer (maximum on the order of 10-1 ms-1) indicated that the basins act as hotspots of groundwater contamination in the area.

We develop a model system that recognizes the distinct traffic incident duration profiles based on incident type. Specifically, a copula-based joint framework with a scaled multinomial logit model (SMNL) system for incident type and a grouped generalized ordered logit (GGOL) model system for incident duration to accommodate for the impact of observed and unobserved effects on incident type and incident duration. The model system is estimated using traffic incident data from 2012 through 2017 for the Greater Orlando region, employing a comprehensive set of exogenous variables – incident characteristics, roadway characteristics, traffic condition, weather condition, built environment and socio-demographic characteristics. In the presence of multiple years of data, the copula-based methodology is also customized to accommodate for observed and unobserved temporal effects (including heteroscedasticity) on incident duration. Based on a rigorous comparison across different copula models, parameterized Frank-Clayton-Frank specification was found to offer the best data fit. The value of the proposed model system is illustrated by comparing predictive performance of the proposed model relative to the traditional single duration model on a holdout sample.

Most safety performance analysis employs cross-sectional and time-series datasets, posing an important challenge to safety performance and crash modification analysis. The traditional safety model analysis paradigm relying on observed data only allows relative comparisons between analysis methods and is unable to establish how well the methods mimic the true underlying crash generation process. Assumptions are made about the data, but whether the assumptions truly characterize the safety data generation in the real world remains unknown. To address this issue, this thesis proposes the generation of realistic artificial data (RAD). In developing a prototype RAD generator for crash data, we mimic the process of crash occurrence, simulating daily traffic patterns and evaluating each trip for crash risk. For each crash, details such as crash location, crash type, and crash severity are also generated. As part of the artificial data generation, this thesis also proposes a framework for employing naturalistic driving study (NDS) data to understand and predict crash risk at a disaggregate trip level. This framework proposes a case-control study design for understanding trip level crash risk. The study also conducts a comparison of different case to control ratios and finds the model parameters estimated with these control ratios are reasonably similar. A multi-level random parameters binary logit model was estimated where multiple forms of unobserved variables were tested. This model was calibrated by modifying the constant parameter to generate a population conforming risk model, and then tested on a hold-out sample of data records. This thesis contributes to safety research through the development of a prototype RAD generator for traffic crash data, which will lead to new information about the underlying causes of crashes and ways to make roadways safer.

Since the general public will play a central role in the evolution of AVs, research has been performed to assess their perception and acceptance of AVs. Nevertheless, the most potential users of AVs, i.e., young, students, and more educated people, have not received any particular focus in those studies. This research gap has motivated us to assess their perceptions. Extensive data analyses of the survey at the University of Central Florida with a sample of 315 reveal that on average 57% of the respondents were familiar with AVs, and about 44% of the respondents felt positive perceptions toward AVs. Around 51% of the respondents had some concerns regarding the perceived negative aspects of AVs, however, a significant percentage of people (around 34%) maintained a neutral position regarding the negative aspects of AVs. In addition, structural equation modeling was performed considering five latent variables and 32 observed variables to investigate the inter-relationship among those variables. Model results suggest that as more people have positive primary perceptions about different aspects of AVs, their attitudes toward AVs would be more positive, and the concerns regarding AVs would be reduced. Demographic characteristics do not significantly influence the willingness to possess AVs, and people want to own AVs despite their different demographic backgrounds. These study findings could help policymakers to apprehend different prospects of people's perceptions regarding AVs and have implications for the stakeholders of autonomous vehicles. In addition to that, the study proposed an organized questionnaire based on which the responses of the stakeholders should be collected and analyzed. Findings from literature using heterogeneous questionnaires produced perplexing results for making relevant policies for the adoption and deployment of AVs. The current study addressed this research gap. Particularly this study attempted to identify the organizational pattern of the questionnaire of the previous studies, and eventually proposed a uniform questionnaire based on which future studies might be conducted to obtain varying outcomes from different contexts for the same input. The proposed questionnaire is divided into two portions: a) general content, and b) special content. The general content is applicable to all studies that seek to assess the perceptions of people regarding AVs. This content consists of 4 main categories i.e., perceptions, concerns, expected benefits, and ownership. In addition to general content, special content is also proposed to be added with the general content for some specific cases where the studies will focus on Shared AVs (SAVs) or investigate the perceptions of vulnerable road users or assess the perceptions of the respondents after riding AVs. The current study has the potential to help future studies produce effective policy measures for the quick adoption and deployment of AVs.

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task, however, remains challenging due to the high variations in road objects and pavement types, variety of lighting condition, low contrast, and background noises in pavement images. In this dissertation, we propose novel deep learning algorithms for image-based road condition assessment to tackle current challenges in detection, classification and segmentation of pavement images. Motivated by the need for classifying a wide range of objects in road monitoring, this dissertation introduces a Multi-Scale Convolution Neural Network (MCNN) for multi-class classification of pavement images. MCNN improves the classification performance by encoding contextual information through multi-scale input tiles. Then, an Attention-Based Multi-Scale CNN (A+MCNN) is proposed to further improve the classification results through a novel mid-fusion strategy for combining multi-scale features extracted from multi-scale input tiles. An attention module is designed as an adaptive fusion strategy to generate importance scores and integrate multi-scale features based on how informative they are to the classification task. Finally, Dual Attention CNN (DACNN) is introduced to improve the performance of multi-class classification using both intensity and range images collected with 3D laser imaging devices. DACNN integrates information in intensity and range images to enhance distinct features improving the objects classification in noisy images under various illumination conditions. The standard road condition assessment includes determining not only the type of defects but also the severity of detects. In this regard, a pavement crack segmentation algorithm, CrackSegmenter, is proposed to detect crack at pixel level. The CrackSegmenter leverages residual blocks, attention blocks, Atrous Spatial Pyramid Pooling (ASSP), and squeeze and excitation blocks to improve segmentation performance in pavement crack images.

Advances in information technology services have seen profound impacts on the state of transport services in the urban traffic environment. Mobility-as-a-Service (MaaS) represents the digital consolidation of users, operators, and public-private managing entities to provide totally comprehensive, integrated trip-making services. Users now enjoy extra flexibility for trip-making with new modal alternatives such as micro-mobility (e.g Lime Bikes, Spin Scooters) and rideshare (e.g. Lyft, Uber). However, current knowledge on the performance and interactive effects of these newer alternative modes is vague if not inconsistent. As such, these effects were studied through micro-simulation analysis of a multi-modal urban corridor in Orlando, Florida. D-Optimal experimental designs are generated to evaluate the hard performance and sustainability effects of five (5) modes: personal vehicles, bus transit, rideshare, walking, and micro-mobility. Bus transit demonstrates the lowest impact per person-trip on a route-level (i.e. travel time, queuing), while significantly enhancing network-level performance factors such as average delay and travel speed. For instance, a relatively minor eight (8) percent increase in transit share resulted in a 15.5 percent decrease in average delay through the network. Moreover, the route-level impacts of transit decrease to zero as the network approaches congestion. Conversely, rideshare demonstrates significant adverse effects across all performance measures, worsening in more congested conditions, while walking and micro-mobility effects are found to vary and are dictated mainly by their interactions with other sidewalk and roadway users. Furthermore, curbside facilities such as lay-bys also demonstrated substantial roadway performance impacts. Lastly, various cost analyses are used to demonstrate the potential cost-efficiency of even the most cutting-edge transit-focused services in terms of project budgeting and externalities. Discussion of the findings provided valuable insights for street-and-city-level multi-modal planning design, as well as the broader operational implications of autonomous technologies taking on a greater role in the transportation service industry.

The research aims to improve pedestrian safety at signalized intersections using video data, surrogate safety measures and deep learning. Machine learning (including deep learning) models are proposed for predicting pedestrians' potentially dangerous situations. On the one hand, pedestrians' red-light violations can expose the pedestrians to motorized traffic and pose potential threats to pedestrian safety. Thus, the prediction of pedestrians' crossing intention during red-light signals is carried out. The pose estimation technique is used to extract features on pedestrians' bodies. Machine learning models are used to predict pedestrians' crossing intention at intersections' red-light, with video data collected from signalized intersections. Multiple prediction horizons are used. On the other hand, SSMs (Surrogate Safety Measures) can be used to better investigate the mechanisms of crashes proactively compared with crash data. With the SSMs indicators, pedestrians' near-crash events can be identified. The automated computer vision techniques such as Mask R-CNN (Region-based Convolutional Neural Network) and YOLO (You Only Look Once) are utilized to generate the features of the road users from video data. The interactions between vehicles and pedestrians are analyzed. Based on that, the prediction of pedestrians' conflicts in time series with deep learning models is carried out at the individual-vehicle level. Besides, two SSMs indicators, PET (Post Encroachment Time) and TTC (Time to Collision), are derived from videos to label pedestrians' near-crash events. Deep learning model such as LSTM (Long Short-term Memory) is used for modeling. To make the model more adaptive to a real-time system, the signal timing data ATSPM© (Automated Traffic Signal Performance Measures) can be used. The signal cycles that contain pedestrian phases are labeled with the SSMs indicators derived from videos and then modeled. With the above-mentioned models proposed, the decision makers can determine the possible countermeasures, or the warning strategies for drivers at intersections.

Available tools are insufficient to provide the needed systemwide view for planning future freight transportation systems based on the coordinated use of more than one mode of transportation. Many existing tools are either mode specific (they only address a single mode of transportation) or too microscopic in scope (they address only detailed traffic flows or facility operations). No comprehensive tool exists that considers the level of performance of the total system, which is important due to the many interdependencies that exist between the different modes of transportation. In some cases, optimizing just a particular component of the transportation network could result in sub-optimization of the entire transportation system. Intermodal freight transportation planning tools are needed to optimize future freight transportation systems. This thesis presents a prototype Virtual Intermodal Transportation System (VITS) that simulates the movement of freight via highways, railways, and waterways on a statewide level. The requirements for the VITS are researched and identified. The general processes of building the VITS prototype, the results from hypothetical case studies using the VITS as a planning and analysis tool, and potential improvements to the methodology are also discussed.

Distracted driving and pedestrians pose one of the most difficult challenges to ensuring a safe and efficient transportation system. Modern communications have delivered greater convenience. However, this has come at the cost of attention spans. Safety has been thoroughly explored in terms of distracted driving and pedestrians. However, impacts on traffic operations have received minimal research attention. Few studies provided a theoretical mechanism on how intersection operations can be affected but failed to quantify the real-life impacts on traffic operations. Furthermore, new Florida laws prohibit cellphone usage while driving but is allowed when the vehicle is stationary, which may result in increased cellphone use at red lights. This research aims to quantify how distracted driving and pedestrians impact vehicle headways at signalized intersections. Thousands of observations were collected from eighteen (18) approaches at ten (10) intersections in Orange County, Florida, covering a variety of land uses, intersection configurations, and periods of high demand. The results demonstrated that the percentage of distracted drivers in the through and left movement was approximately 50% and 87%, respectively. Drivers were more distracted in commercial zones and more attentive to the signal changes than in school and residential areas. Cell phone usage for through and left movements had a significant percentage of distraction types, 31% and 28%. The statistical model showed that distracted drivers had nearly double the base headway compared to undistracted drivers' base headway. Drivers are more alert in the AM peak and less likely to be distracted by their phones than in the PM peak. The results also revealed that the first vehicle position in the queue had a detrimental effect on the headway and the overall intersection capacity. The pedestrians' analysis showed that around half the pedestrians were distracted. Pedestrians are less distracted in school and college land use than other land-use types. Distracted pedestrians did not cause a significant negative impact on the traffic operations, but they increased the crossing time by approximately 4%.

The objective of this study is to analyze the driving behavior at toll plazas by examining multiple scenarios using a driving simulator to study the effect of different options including different path decisions, various signs, arrow markings, traffic conditions, and extending auxiliary lanes before and after the toll plaza on the driving behavior. Also, this study focuses on investigating the effect of drivers' characteristics on the dangerous driving behavior (e.g. speed variation, sudden lane change, drivers' confusion). Safety and efficiency are the fundamental goals that transportation engineering is always seeking for the design of highways. Transportation agencies have a crucial challenging task to accomplish traffic safety, particularly at the locations that have been identified as crash hotspots. In fact, toll plaza locations are one of the most critical and challenging areas that expressway agencies have to pay attention to because of the increasing traffic crashes over the past years near toll plazas. Drivers are required to make many decisions at expressway toll plazas which result in drivers' confusion, speed variation, and abrupt lane change maneuvers. These crucial decisions are mainly influenced by three reasons. First, the limited distance between toll plazas and the merging areas at the on-ramps before the toll plazas. In additional to the limited distance between toll plazas and the diverging areas after the toll plazas at the off-ramps. Second, it is also affected by the location and the configuration of signage and pavement markings. Third, drivers' decisions are affected by the different lane configurations and tolling systems that can cause drivers' confusion and stress. Nevertheless, limited studies have explored the factors that influence driving behavior and safety at toll plazas. There are three main systems of the toll plaza, the traditional mainline toll plaza (TMTP), the hybrid mainline toll plaza (HMTP), and the all-electronic toll collection (AETC). Recently, in order to improve the safety and the efficiency of the toll plazas, most of the traditional mainline toll plazas have been converted to the hybrid toll plazas or the all-electronic toll collection plazas. This study assessed driving behavior at a section, including a toll plaza on one of the main expressways in Central Florida. The toll plaza is located between a close on-ramp and a nearby off-ramp. Thus, these close distances have a significant effect on increasing driver's confusion and unexpected lane change before and after the toll plaza. Driving simulator experiments were used to study the driving behavior at, before and after the toll plaza. The details of the section and the plaza were accurately replicated in the simulator. In the driving simulator experiment, Seventy-two drivers with different age groups were participated. Subsequently, each driver performed three separate scenarios out of a total of twenty-four scenarios. Seven risk indicators were extracted from the driving simulator experiment data by using MATLAB software. These variables are average speed, standard deviation of speed, standard deviation of lane deviation, acceleration rate, standard deviation of acceleration (acceleration noise), deceleration rate, and standard deviation of deceleration (braking action variation). Moreover, various scenario variables were tested in the driving simulator including different paths, signage, pavement markings, traffic condition, and extending auxiliary lanes before and after the toll plaza. Divers' individual characteristics were collected from a questionnaire before the experiment. Also, drivers were filling a questionnaire after each scenario to check for simulator sickness or discomfort. Nine variables were extracted from the simulation questionnaire for representing individual characteristics including, age, gender, education level, annual income, crash experience, professional drivers, ETC-tag use, driving frequency, and novice international drivers. A series of mixed linear models with random effects to account for multiple observations from the same participant were developed to reveal the contributing factors that affect driving behavior at toll plazas. The results uncovered that all drivers who drove through the open road tolling (ORT) showed higher speed and lower speed variation, lane deviation, and acceleration noise than other drivers who navigate through the tollbooth. Also, the results revealed that providing adequate signage, and pavement markings are effective in reducing risky driving behavior at toll plazas. Drivers tend to drive with less lane deviation and acceleration noise before the toll plaza when installing arrow pavement markings. Adding dynamic message sign (DMS) at the on-ramp has a significant effect on reducing speed variation before the toll plaza. Likewise, removing the third overhead sign before the toll plaza has a considerable influence on reducing aggressive driving behavior before and after the toll plaza. This result may reflect drivers' desire to feel less confusion by excessive signs and markings. Third, extending auxiliary lanes with 660 feet (0.125 miles) before or after the toll plaza have an effect on increasing the average speed and reducing the lane deviation and the speed variation at and before the toll plaza. It also has an impact on increasing the acceleration noise and the braking action variation after the toll plaza. Finally, it was found that in congested conditions, participants drive with a lower speed variation and lane deviation before the toll plaza but with a higher acceleration noise after the toll plaza. On the other hand, understanding drivers' characteristics is particularly important for exploring their effect on risky driving behavior. Young drivers (18-25) and old drivers (older than 50 years) consistently showed a higher risk behavior than middle age drivers (35 to 50). Also, it was found that male drivers are riskier than female drivers at toll plazas. Drivers with high education level, drivers with high income, ETC-tag users, and drivers whose driving frequency is less than three trips per day are more cautious and tend to drive at a lower speed.