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Articles 1 - 30 of 103
Full-Text Articles in Transportation Engineering
Integrated Corridor Management Framework For Severe Freeway Incidents, Sanjida Afroz Iqra
Integrated Corridor Management Framework For Severe Freeway Incidents, Sanjida Afroz Iqra
Graduate Studies Theses and Dissertations 2026
Traffic incidents are a major source of non-recurrent congestion on urban freeways, generating substantial mobility, safety, and economic impacts. Severe incidents that block multiple or all travel lanes are particularly disruptive because they degrade freeway operations and propagate congestion onto surrounding arterials. Effective Integrated Corridor Management (ICM) requires the ability to identify severe incidents, estimate their network-wide impacts, and anticipate the traffic conditions and driver behaviors that contribute to instability. This dissertation develops a data-driven ICM framework to address these challenges using real-world incident, crash, detector, and connected vehicle data from major Central Florida corridors, including I-4 and SR-417. The …
Understanding Evacuation Traffic Safety Issues During Hurricane Evacuation Using Machine Learning And Connected Vehicle Data, Zaheen E Muktadi Syed
Understanding Evacuation Traffic Safety Issues During Hurricane Evacuation Using Machine Learning And Connected Vehicle Data, Zaheen E Muktadi Syed
Electronic Theses and Dissertations, 2020-2023
Hurricane evacuation, ordered to save lives of people of coastal regions, generates high traffic demand with increased crash risk. To mitigate such risk, transportation agencies need to anticipate highway locations with high crash risks to deploy appropriate countermeasures. With ubiquitous sensors and communication technologies, it is now possible to retrieve micro-level vehicular data containing individual vehicle trajectory and speed information. Such high-resolution vehicle data, potentially available in real time, can be used to assess prevailing traffic safety conditions. Using vehicle speed and acceleration profiles, potential crash risks can be predicted in real time. Previous studies on real-time crash risk prediction …
Transportation Electrification In Interdependent Power And Transportation Systems - Analysis, Planning, And Operation, Sina Baghali
Transportation Electrification In Interdependent Power And Transportation Systems - Analysis, Planning, And Operation, Sina Baghali
Electronic Theses and Dissertations, 2020-2023
Electric vehicles (EVs) are one of the eminent alternatives to decarbonize the transportation sector. However, large-scale EV adoption brings new challenges and opportunities to both transportation and power systems (TPSs). The challenges include the lack of understanding of EV driving behaviors and the associated charging demand (CD) distribution, the complex interaction of the decentralized decision-makers from TPSs, and the insufficient infrastructure from TPSs to accommodate the growing CD of EVs. On the other hand, the opportunities include benefiting the power systems by leveraging vehicle-to-grid (V2G) technologies and improving transportation mobility by incorporating strategic infrastructure planning. The goal of this dissertation …
Developing A Physics-Informed Deep Learning Paradigm For Traffic State Estimation, Jiheng Huang
Developing A Physics-Informed Deep Learning Paradigm For Traffic State Estimation, Jiheng Huang
Electronic Theses and Dissertations, 2020-2023
The traffic delay due to congestion cost the U.S. economy $ 81 billion in 2022, and on average, each worker lost 97 hours each year during commute due to longer wait time. Traffic management and control strategies that serve as a potent solution to the congestion problem require accurate information on prevailing traffic conditions. However, due to the cost of sensor installation and maintenance, associated sensor noise, and outages, the key traffic metrics are often observed partially, making the task of estimating traffic states (TSE) critical. The challenge of TSE lies in the sparsity of observed traffic data and the …
Modeling Individual Activity And Mobility Behavior And Assessing Ridesharing Impacts Using Emerging Data Sources, Jiechao Zhang
Modeling Individual Activity And Mobility Behavior And Assessing Ridesharing Impacts Using Emerging Data Sources, Jiechao Zhang
Electronic Theses and Dissertations, 2020-2023
Predicting individual mobility behavior is one of the major steps of transportation planning models. Accurate prediction of individual mobility behavior will be beneficial for transportation planning. Although previous studies have used different data sources to model individual mobility behaviors, they have several limitations such as the lack of complete mobility sequences and travel mode information, limiting our ability to accurately predict individual movements. In recent years, the emergence of GPS-based floating car data (FCD) and on-demand ride-hailing service platforms can provide innovative data sources to understand and model individual mobility behavior. Compared to the previously used data sources such as …
Smart Mobility Sensing Of Origin-Destination Pairs Using Computer Vision, Seyed Sina Shid Moosavi
Smart Mobility Sensing Of Origin-Destination Pairs Using Computer Vision, Seyed Sina Shid Moosavi
Electronic Theses and Dissertations, 2020-2023
The measurement of the origin-destination (OD) flow of individual passengers within the public transportation system plays a vital role in understanding resident mobility and facilitating route planning. Particularly in resource-limited communities, where access to advanced technologies like smart card systems may not be available, bus transportation systems play a crucial role in daily life. Traditional methods, such as driver logs, are not only time-consuming but also difficult to provide precise measurements of individualized OD pairs. Therefore, the primary objective of this study is to propose an automatic passenger sensing system (software) capable of accurately measuring OD pairs for individual passengers …
Development,Validation, And Integration Of Ai-Driven Computer Vision System And Digital-Twin System For Traffic Safety Dignostics, Ou Zheng
Electronic Theses and Dissertations, 2020-2023
The use of data and deep learning algorithms in transportation research have become increasingly popular in recent years. Many studies rely on real-world data. Collecting accurate traffic data is crucial for analyzing traffic safety. Still, traditional traffic data collection methods that rely on loop detectors and radar sensors are limited to collect macro-level data, and it may fail to monitor complex driver behaviors like lane changing and interactions between road users. With the development of new technologies like in-vehicle cameras, Unmanned Aerial Vehicle (UAV), and surveillance cameras, vehicle trajectory data can be collected from the recorded videos for more comprehensive …
Understanding Changes In Land Use Patterns At A Parcel Level In Central Florida Counties (Orange, Seminole, Osceola), Youssef Mohamed Abdelaziz Elsebaie
Understanding Changes In Land Use Patterns At A Parcel Level In Central Florida Counties (Orange, Seminole, Osceola), Youssef Mohamed Abdelaziz Elsebaie
Electronic Theses and Dissertations, 2020-2023
In Florida, a state with significant population growth, it is essential to understand how land use change and transportation interact. Understanding these interactions between land use and transport is useful for transportation planners and land use modelers. Hence, in this research, we attempt to quantify the impact of the transportation system on land use and vice-versa. The research, using high-resolution land use data from 2011 to 2019, builds a binary logistic regression model of land use change. The model accounts for various independent variables, including socio-demographic attributes, built environment characteristics, and transportation network variables. The data set covers GIS data …
Enabling Large-Scale Transportation Electrification For Shared And Connected Mobility Systems, Md Rakibul Alam
Enabling Large-Scale Transportation Electrification For Shared And Connected Mobility Systems, Md Rakibul Alam
Graduate Thesis and Dissertation 2023-2024
Owing to advancements in technology, substantial investments within the automotive industry, and the formulation of supportive state policies, the future landscape of the transportation sector is poised to witness a shift from traditional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs). While EVs have made inroads in the market, they still face significant hurdles in the form of range anxiety and prolonged charging durations, inhibiting their widespread adoption. To tackle these challenges, a comprehensive approach to smart transportation electrification is proposed, emphasizing the pivotal roles of infrastructure development, particularly in the allocation of charging stations, and strategic operational decisions, …
Assessment Of Midblock Pedestrian Crossing Facilities Using Surrogate Safety Measures And Vehicle Delay, Nafis Anwari
Assessment Of Midblock Pedestrian Crossing Facilities Using Surrogate Safety Measures And Vehicle Delay, Nafis Anwari
Graduate Thesis and Dissertation 2023-2024
This dissertation has contributed to the pedestrian safety literature by assessing and comparing safety benefits and traffic efficiency among midblock Rectangular Rapid Flashing Beacon (RRFB) and Pedestrian Hybrid Beacon (PHB) sites. Video trajectory data were used to calculate pedestrian Surrogate Safety Measures (SSMs) and vehicles' delay. Regression models of SSMs and vehicles' delay revealed that PHB sites offer more safety benefits, at the expense of increased vehicles' delay, compared to RRFB sites. The presence of the PHB, weekday, signal activation, lane count, pedestrian speed, vehicle speed, land use mix, traffic flow, time of day, and pedestrian starting position from the …
Time-Specific Safety Performance Functions For Different Advanced Traffic Management Strategies, Jingwan Fu
Time-Specific Safety Performance Functions For Different Advanced Traffic Management Strategies, Jingwan Fu
Electronic Theses and Dissertations, 2020-2023
Time-specific Safety Performance Functions (SPFs) were proposed to achieve accurate and dynamic crash frequency predictions and bridge the gap between annual crash frequency prediction and real-time crash likelihood prediction. This research proposed time-specific SPFs considering the temporal variation in crashes and traffic characteristics. Firstly, the developed time-specific SPFs that include different ATM strategies (i.e., HOV, merge, diverge and reversible lanes segments) were investigated in this study. The results indicate that the traffic turbulence during specific hours would relate to crash occurrence. Further, the variables that represent the speed and occupancy differences between the HOV lanes/reversible lanes and general-purpose lanes were …
Spatial Ensemble Distillation Learning Based Real-Time Crash Prediction And Management Framework, Md Rakibul Islam
Spatial Ensemble Distillation Learning Based Real-Time Crash Prediction And Management Framework, Md Rakibul Islam
Graduate Thesis and Dissertation 2023-2024
Real-time crash prediction is a complex task, since there is no existing framework to predict crash likelihood, types, and severity together along with a real-time traffic management strategy. Developing such a framework presents various challenges, including not independent and identically distributed data, imbalanced data, large model size, high computational cost, missing data, sensitivity vs. false alarm rate (FAR) trade-offs, estimation of traffic restoration time after crash occurrence, and real-world deployment strategy. A novel spatial ensemble distillation learning modeling technique is proposed to address these challenges. First, large-scale real-time data were used to develop a crash likelihood prediction model. Second, the …
Safety Considerations For Setting Variable Speed Limits On Freeways, Md Tarek Hasan
Safety Considerations For Setting Variable Speed Limits On Freeways, Md Tarek Hasan
Graduate Thesis and Dissertation 2023-2024
This thesis focuses on evaluating the appropriate speed at which vehicles should travel under different traffic conditions on freeways and its impact on crash frequency. The common belief is that the lower speed results in fewer crashes as reduced speed provides drivers with more time to react effectively and avoid collisions. However, this perspective overlooks the interplay among traffic speed, average spacing between consecutive vehicles, and the distance available for stopping a vehicle. Hence, we propose a safety parameter termed ‘Safety Correlate' (SCORE), which is defined as the proportion of average spacing relative to the stopping distance. To determine the …
Fight For Flight: The Narratives Of Human Versus Machine Following Two Aviation Tragedies, Andrew Prahl, Rio Kin Ho Leung, Alicia Ning Shan Chua
Fight For Flight: The Narratives Of Human Versus Machine Following Two Aviation Tragedies, Andrew Prahl, Rio Kin Ho Leung, Alicia Ning Shan Chua
Human-Machine Communication
This study provides insight into the relationship between human and machine in the professional aviation community following the 737 MAX accidents. Content analysis was conducted on a discussion forum for professional pilots to identify the major topics emerging in discussion of the accidents. A subsequent narrative analysis reveals dominant arguments of human versus machine as zero-sum, surrender to machines, and an epidemic of mistrust. Results are discussed in the context of current issues in human-machine communication, and we discuss what other quickly automating industries can learn from aviation’s experience.
Detecting And Tracking Vulnerable Road Users' Trajectories Using Different Types Of Sensors Fusion, Zhongchuan Wang
Detecting And Tracking Vulnerable Road Users' Trajectories Using Different Types Of Sensors Fusion, Zhongchuan Wang
Electronic Theses and Dissertations, 2020-2023
Vulnerable road user (VRU) detection and tracking has been a key challenge in transportation research. Different types of sensors such as the camera, LiDAR, and inertial measurement units (IMUs) have been used for this purpose. For detection and tracking with the camera, it is necessary to perform calibration to obtain correct GPS trajectories. This method is often tedious and necessitates accurate ground truth data. Moreover, if the camera performs any pan-tilt-zoom function, it is usually necessary to recalibrate the camera. In this thesis, we propose camera calibration using an auxiliary sensor: ultra-wideband (UWB). USBs are capable of tracking a road …
High Fidelity Injury Severity Analysis Using Econometric Modeling Approaches, Ahmed Kabli
High Fidelity Injury Severity Analysis Using Econometric Modeling Approaches, Ahmed Kabli
Electronic Theses and Dissertations, 2020-2023
Crash severity models are typically developed using police reported injury severity databases. However, several research studies have identified various challenges associated with police reported data. Therefore, the current dissertation is focusing on developing high resolution crash severity models based on medical professional driver injury severity reported using Abbreviated Injury Scale for eight body regions. The dissertation focused on developing a disaggregate injury severity modeling framework that can enhance the estimation accuracy of independent variable impacts on severity. Within this broad research vision, the dissertation has multiple objectives. First, a joint random parameters multivariate model structure with as many dimensions as …
Machine Learning Algorithms For Forecasting The Impacts Of Connected And Automated Vehicles On Highway Construction Costs, Amirsaman Mahdavian
Machine Learning Algorithms For Forecasting The Impacts Of Connected And Automated Vehicles On Highway Construction Costs, Amirsaman Mahdavian
Electronic Theses and Dissertations, 2020-2023
A multitude of externalities affects transport efficiency and numbers of trips. Population expansion, urban development, political issues, fiscal trends, and growth in the field of connected, automated, shared, and electric (CASE) vehicles have all played prominent roles. While the market is keenly aware of the upcoming shift to the CASE vehicles, the transformation itself is reliant upon the development of technologies, customer outlook, and guidelines. The purpose of this research is to establish an overview of the possible network design problems, as well as potential consequences to vehicle automation systems by employing machine learning and system dynamics analysis. Finally, the …
A Spatiotemporal Evaluation Of Freeway Traffic Demand In Florida During Covid-19 Pandemic, Md Istiak Jahan
A Spatiotemporal Evaluation Of Freeway Traffic Demand In Florida During Covid-19 Pandemic, Md Istiak Jahan
Electronic Theses and Dissertations, 2020-2023
This thesis contributes to our understanding of the changes in traffic volumes on major roadway facilities in Florida due to COVID-19 pandemic from a spatiotemporal perspective. Three different models were tested in this study- a) Linear regression model, b) Spatial Autoregressive Model (SAR) and c) Spatial Error Model (SEM). For the model estimation, traffic volume data for the year 2019 and 2020 from 3,957 detectors were augmented with independent variables, such as- COVID-19 case information, socioeconomics, land-use and built environment characteristics, roadway characteristics, meteorological information, and spatial locations. Traffic volume data was analyzed separately for weekdays and holidays. SEM models …
Using Machine Learning Technique To Develop A Deterioration Predicting Model For Pavement Marking In Florida, Ehab Abdelmaksoud
Using Machine Learning Technique To Develop A Deterioration Predicting Model For Pavement Marking In Florida, Ehab Abdelmaksoud
Electronic Theses and Dissertations, 2020-2023
Longitudinal pavement markings play a significant role on the roadways by delivering information to motorists to help them navigate and follow the road. These markings are also considered to be a crucial control device that can enhance ideal nighttime visibility, especially on rural roads where the surrounding luminance is insufficient. Hence, the main question for public agencies or officials is about when the replacement of the pavement markings needs to take place. The Federal Highway Administration (FHWA) is considering proposing a minimum level of retroreflectivity standard and based on that, the Manual on Uniform Traffic Control Devices (MUTCD) set aside …
Generative Modeling Of Human Behavior: Social Interaction And Networked Coordination In Shared Facilities, Saumya Gupta
Generative Modeling Of Human Behavior: Social Interaction And Networked Coordination In Shared Facilities, Saumya Gupta
Electronic Theses and Dissertations, 2020-2023
Urbanization is bringing together various modes of transport, and with that, there are challenges to maintaining the safety of all road users, especially vulnerable road users (VRUs). Therefore, there is a need for street designs that encourages cooperation and resource sharing among road users. Shared space is a street design approach that softens the demarcation of vehicles and pedestrian traffic by reducing traffic rules, traffic signals, road markings, and regulations. Understanding the interactions and trajectory formations of various VRUs will facilitate the design of safer shared spaces. It will also lead to many applications, such as implementing reliable ad hoc …
Analytical Study Of Deep Learning Methods For Road Condition Assessment, Elham Eslami
Analytical Study Of Deep Learning Methods For Road Condition Assessment, Elham Eslami
Electronic Theses and Dissertations, 2020-2023
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 …
An Econometric Analysis Of Domestic Aviation In The Us, Sudipta Dey Tirtha
An Econometric Analysis Of Domestic Aviation In The Us, Sudipta Dey Tirtha
Electronic Theses and Dissertations, 2020-2023
In this dissertation, we examine two dimensions of domestic aviation - demand and delay - that directly influence economic impact of the sector. We conduct a comprehensive analysis of airline demand employing airline data compiled by Bureau of Transportation Statistics. The demand analysis is conducted in three steps. First, we propose a novel modeling approach for modeling airline demand evolution over time. Specifically, we develop a joint panel group generalized ordered probit (GGOP) model system for modeling air passenger arrivals and departures in a discretized framework that subsumes the traditional linear regression approach. Further, we consider the influence of observed …
Distracted Driving And Pedestrians' Effects On Headway At Signalized Intersections, Bassel Elgamal
Distracted Driving And Pedestrians' Effects On Headway At Signalized Intersections, Bassel Elgamal
Electronic Theses and Dissertations, 2020-2023
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 …
A Deep Learning Approach For Spatiotemporal-Data-Driven Traffic State Estimation, Amr Hatem Ragaa Abdelraouf
A Deep Learning Approach For Spatiotemporal-Data-Driven Traffic State Estimation, Amr Hatem Ragaa Abdelraouf
Electronic Theses and Dissertations, 2020-2023
The past decade witnessed rapid developments in traffic data sensing technologies in the form of roadside detector hardware, vehicle on-board units, and pedestrian wearable devices. The growing magnitude and complexity of the available traffic data has fueled the demand for data-driven models that can handle large scale inputs. In the recent past, deep-learning-powered algorithms have become the state-of-the-art for various data-driven applications. In this research, three applications of deep learning algorithms for traffic state estimation were investigated. Firstly, network-wide traffic parameters estimation was explored. An attention-based multi-encoder-decoder (Att-MED) neural network architecture was proposed and trained to predict freeway traffic speed …
Automated Vehicle To Vehicle Conflict Analysis At Signalized Intersections By Camera And Lidar Sensor Fusion, Alabi Mehzabin Anisha
Automated Vehicle To Vehicle Conflict Analysis At Signalized Intersections By Camera And Lidar Sensor Fusion, Alabi Mehzabin Anisha
Electronic Theses and Dissertations, 2020-2023
This research presents an approach for safety diagnosis using sensor fusion techniques. This work fuses the outputs of a roadside low-resolution camera and a solid-state LiDAR. For vehicle classification and detection in videos, the YOLO v5 object detection model was utilized. The raw 3D point clouds generated by the LiDAR are processed by two manual steps - ground plane transformation and background segmentation, and two real-time steps - foreground clustering, and bounding box fitting. Taking the generated 2D bounding boxes of both camera and LiDAR, we associate the common bounding box pairs by thresholding on the Euclidean distance threshold of …
Assessing Public Perception And Proposing An Organized Questionnaire For The Deployment And Adoption Of Autonomous Vehicles, Md Rakibul Islam
Assessing Public Perception And Proposing An Organized Questionnaire For The Deployment And Adoption Of Autonomous Vehicles, Md Rakibul Islam
Electronic Theses and Dissertations, 2020-2023
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 …
Development Of Active Learning Data Fixing Tool With Visual Analytics To Enhance Traffic Near-Miss Diagnosis, Jinyu Pei
Electronic Theses and Dissertations, 2020-2023
This study proposes a software to upgrade the UCF SST's Automated Roadway Conflicts Identification System (ARCIS), a pixel-to-pixel manner automated safety diagnostics and conflict identification system. The system is developed to extract vehicles' trajectories and traffic parameters using unmanned aerial vehicles (UAV) video and utilizing deep learning techniques. A user-friendly tool to improve rapid system development with active-learning, data analysis, and visualization techniques is introduced, which is capable of traffic safety near-miss diagnostics based on the ARCIS output. Multiple approaches are used to enhance the system performance, including video stabilization, object filtering, stitching multiple videos, vehicle detection and tracing. In …
Effect Of Various Speed Management Strategies On Bicycle Crashes For Urban Roads In Central Florida, Jorge Ugan
Effect Of Various Speed Management Strategies On Bicycle Crashes For Urban Roads In Central Florida, Jorge Ugan
Electronic Theses and Dissertations, 2020-2023
In recent years, cycling has become an increasingly popular transportation mode around the world. In contrast to other popular modes of transportation, cycling is more economic and energy efficient. While many studies have been conducted for the bicycle safety analysis, most of them were limited in terms of bicycle exposure data and on-street data. This study tries to improve the current safety performance functions for bicycle crashes at urban corridors by utilizing crowdsource data from STRAVA and on-street speed management strategies data. Speed management strategies are any roadway alterations that causes a change in motorists' driving behavior. In Florida, these …
Data Driven Methods For Large Scale Network Level Traffic Modeling, Rezaur Rahman
Data Driven Methods For Large Scale Network Level Traffic Modeling, Rezaur Rahman
Electronic Theses and Dissertations, 2020-2023
Rapid growth in population along with urban-centric activities impose a massive demand on existing transportation systems, thus increasing traffic congestion and other mobility related challenges. To overcome such challenges, we need network-scale models to accurately predict real-time traffic demand and associated congestion. However, traditional network modeling approaches have shortcomings due to the complexity in traffic flow modeling, limited scope to incorporate real-time data available from emerging data sources and requiring excessive computation time to generate accurate estimation of traffic flows. Advancement in traffic sensing technologies with big data has created a new opportunity to overcome these challenges and implement deployable …
Improving Pedestrian Safety Using Video Data, Surrogate Safety Measures And Deep Learning, Shile Zhang
Improving Pedestrian Safety Using Video Data, Surrogate Safety Measures And Deep Learning, Shile Zhang
Electronic Theses and Dissertations, 2020-2023
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 …