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- AI-driven systems; Traffic safety analysis; Vehicle trajectory extraction; Digital twin framework; Deep learning algorithms (1)
- Active learning; Traffic safety; Video processing; Deep learning; Visual analytics (1)
- Adaptive traffic signal control; Deep reinforcement learning; Traffic safety; Real-time traffic dynamics; Multi-agent systems (1)
- Airline demand; COVID-19 impact; Flight delays; Econometric modeling; Spatial analysis (1)
- Autonomous vehicles; Public perception; Questionnaire design; Data analysis; Policy implications (1)
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- Bicycle safety; Speed management; Urban cycling; Crash analysis; Crowdsourced data (1)
- COVID-19 impact; Traffic volume analysis; Spatiotemporal modeling; Florida transportation; Vaccination effects on traffic (1)
- Computer vision; Origin-destination pairs; Public transportation; Passenger sensing; Route planning (1)
- Connected vehicles; Automated vehicles; Traffic simulation; Communication protocols; Safety evaluation (1)
- Connected vehicles; Mobile sensing; Real-time crash prediction; Deep learning; Data fusion (1)
- Crash frequency prediction; Time-specific models; Traffic management strategies; Iterative imputation method; Safety performance functions (1)
- Crash risk modeling; Artificial data generation; Naturalistic driving study; Traffic patterns simulation; Case-control study design (1)
- Deep learning; LSTM; Red light violations; Pedestrian safety; Traffic prediction (1)
- Deep learning; Pavement distress recognition; Image segmentation; Multi-class classification; Computer vision (1)
- Deep learning; Real-time prediction; Urban traffic; LSTM-CNN; Data imbalance (1)
- Deep learning; Traffic state estimation; Spatiotemporal data; Video-based detection; Weather detection (1)
- Diverging Diamond Interchange; Displaced Left-turn Intersection; Crash Modification Factors; Traffic Delay; Safety Performance (1)
- Driver attention; Complete streets; Vulnerable users; Context features; Psychological model (1)
- Electric vehicle adoption; Transportation planning; Power systems integration; Machine learning forecasting; Multi-agent optimization (1)
- Hurricane evacuation; Machine learning; Connected vehicle data; Traffic risk prediction; Real-time data analysis (1)
- Injury severity modeling; Econometric analysis; Machine learning comparison; Temporal factors; Disaggregate modeling (1)
- Land use change; Transportation impact; Parcel-level analysis; GIS data; Logistic regression model (1)
- Machine learning; Connected vehicles; Highway construction costs; Traffic flow prediction; Economic evaluation (1)
- Machine learning; Pavement marking; Retroreflectivity; Degradation model; Algorithm comparison (1)
- Machine learning; Urban transportation; Sensor technology; Traffic prediction; Data augmentation (1)
- Mobility behavior; GPS data; Ridesharing impacts; Transportation planning; Data modeling (1)
- Mobility-as-a-Service; Multi-modal transportation; Micro-simulation analysis; Urban corridor performance; Sustainability effects (1)
- Nutrient removal; Stormwater management; Karst geology; Best management practices; Groundwater contamination (1)
- Pedestrian safety; Deep learning; Video analysis; Surrogate safety measures; Traffic prediction (1)
- Recurrent neural networks; Traffic speed prediction; Machine learning; Hyper-parameter tuning; Data aggregation (1)
Articles 31 - 43 of 43
Full-Text Articles in Transportation Engineering
Applying Machine Learning Techniques To Improve Safety And Mobility Of Urban Transportation Systems Using Infrastructure- And Vehicle-Based Sensors, Zubayer Islam
Electronic Theses and Dissertations, 2020-2023
The importance of sensing technologies in the field of transportation is ever increasing. Rapid improvements of cloud computing, Internet of Vehicles (IoV), and intelligent transport system (ITS) enables fast acquisition of sensor data with immediate processing. Machine learning algorithms provide a way to classify or predict outcomes in a selective and timely fashion. High accuracy and increased volatility are the main features of various learning algorithms. In this dissertation, we aim to use infrastructure- and vehicle-based sensors to improve safety and mobility of urban transportation systems. Smartphone sensors were used in the first study to estimate vehicle trajectory using lane …
Evaluation Of Unconventional Signalized Intersections On Arterial Roads And A Proposition For A Novel Intersection Design, Ma'en Al-Omari
Evaluation Of Unconventional Signalized Intersections On Arterial Roads And A Proposition For A Novel Intersection Design, Ma'en Al-Omari
Electronic Theses and Dissertations, 2020-2023
Several unconventional intersection designs were proposed and implemented to enhance traffic safety and operation at intersections. The efficiency of these intersection designs was not sufficiently evaluated in the previous research because of the limited implementation of such designs. However, with the growing interest in the implementation of unconventional intersections by municipalities and transport agencies, it has become a need for a comprehensive evaluation of their safety and operational benefits. Therefore, this dissertation aims to evaluate the safety and operational aspects of unconventional intersection designs by employing different research approaches: crash analysis, microscopic simulation, and driving simulation. Firstly, this dissertation evaluated …
Identifying The Links Between Mental Frameworks, Context Features, And Driver Attention In Complete Streets Environments, Patricia Tice
Identifying The Links Between Mental Frameworks, Context Features, And Driver Attention In Complete Streets Environments, Patricia Tice
Electronic Theses and Dissertations, 2020-2023
Complete street systems integrate a wide range of users in the same space, with unequal risks and responsibilities. This makes driver attention a critical factor in assuring the safety of vulnerable users. The Conditioned Anticipation of People psychological model of driver attention proposes that drivers reflexively reengage their metacognitive processes when they anticipate visually interacting with the human face or form due to the neurological priority that the brain places on human recognition. To test this model, an eye-tracking tabulation was generated from the SHRP2 Naturalistic Driving Study that measured midsegment percent of time on-task and multitasking behavior for 200 …
Safety Evaluation Of Innovative Intersection Designs: Diverging Diamond Interchanges And Displaced Left-Turn Intersections, Ahmed Abdelrahman
Safety Evaluation Of Innovative Intersection Designs: Diverging Diamond Interchanges And Displaced Left-Turn Intersections, Ahmed Abdelrahman
Electronic Theses and Dissertations, 2020-2023
Diverging diamond interchanges (DDIs) and Displaced left-turn intersections (DLTs) are designed to enhance the operational performance of conventional intersections that are congested due to heavy left-turn traffic volumes. Since drivers are not familiar with these types of intersections, there is a need to evaluate their safety performance to validate their effect, and to estimate reliable and representative Crash Modification Factors (CMFs). The safety evaluation was conducted based on three common safety assessment methods, which are before-and-after study with comparison group, Empirical Bayes before-and-after method, and cross-sectional analysis. Furthermore, since DLTs showed poor safety performance, the study also investigated the operational …
Smartphone Sensor-Based Pedestrian Activity Recognition For P2v Communication And Warning System, Dhrubo Hasan Chowdhury
Smartphone Sensor-Based Pedestrian Activity Recognition For P2v Communication And Warning System, Dhrubo Hasan Chowdhury
Electronic Theses and Dissertations, 2020-2023
The ubiquity of smartphones has made a remarkable influence on everyone's day to day life. Variety of useful built-in sensors provide smartphones with a convenient floor for data collection and analysis. Application development based on the user's location and movement is not a difficult task nowadays. But injuries and deaths due to smartphone-distracted movement on roadways is on the increase. This study explores the capabilities of smartphone inertial sensors for pedestrian activity recognition. Smartphone distracted movements can be predicted from the associated pedestrian's posture, thus inertial sensors can provide effective solution for this specific task. Volunteers were asked to perform …
Traffic Speed Prediction And Mobility Behavior Analysis Using On-Demand Ride-Hailing Service Data, Jiechao Zhang
Traffic Speed Prediction And Mobility Behavior Analysis Using On-Demand Ride-Hailing Service Data, Jiechao Zhang
Electronic Theses and Dissertations, 2020-2023
Providing accurate traffic speed prediction is essential for the success of Intelligent Transportation Systems (ITS) deployments. Accurate traffic speed prediction allows traffic managers take proper countermeasures when emergent changes happen in the transportation network. In this thesis, we present a computationally less expensive machine learning approach XGBoost to predict the future travel speed of a selected sub-network in Beijing's transportation network. We perform different experiments for predicting speed in the network from future 1 min to 20 min. We compare the XGBoost approach against other well-known machine learning and statistical models such as linear regression and decision tree, gradient boosting …
Prediction Of Pedestrians' Red Light Violations Using Deep Learning, Shile Zhang
Prediction Of Pedestrians' Red Light Violations Using Deep Learning, Shile Zhang
Electronic Theses and Dissertations, 2020-2023
Pedestrians are regarded as Vulnerable Road Users (VRUs). Each year, thousands of pedestrians' deaths are caused by traffic crashes, which take up 16% of the total road fatalities and injuries in the U.S. (FHWA, 2018). Crashes can happen if there are interactions between VRUs and motorized transportation. And pedestrians' unexpected crossings, such as red-light violations at the signalized intersections, would expose them to motorized transportation and cause potential collisions. This thesis is intended to predict the pedestrians' red-light violation behaviors at the signalized crosswalks based on an LSTM (Long Short-term Memory) neural network. With video data collected from real traffic …
Modeling Of Incident Type And Incident Duration Using Data From Multiple Years, Sudipta Dey Tirtha
Modeling Of Incident Type And Incident Duration Using Data From Multiple Years, Sudipta Dey Tirtha
Electronic Theses and Dissertations, 2020-2023
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. …
Mobility-As-A-Service: Assessing Performance And Sustainability Effects Of An Integrated Multi-Modal Simulated Transportation Network, Mohamed El-Agroudy
Mobility-As-A-Service: Assessing Performance And Sustainability Effects Of An Integrated Multi-Modal Simulated Transportation Network, Mohamed El-Agroudy
Electronic Theses and Dissertations, 2020-2023
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. …
Evaluation Of Safety And Mobility Benefits Of Connected And Automated Vehicles By Considering V2x Technologies, Md Hasibur Rahman
Evaluation Of Safety And Mobility Benefits Of Connected And Automated Vehicles By Considering V2x Technologies, Md Hasibur Rahman
Electronic Theses and Dissertations, 2020-2023
The recent development in communication technologies facilitates the deployment of connected and automated vehicles (CAV) which are expected to change the future transportation system. CAV technologies enable vehicles to communicate with other vehicles through vehicle-to-vehicle (V2V) communications and the infrastructure through Vehicle-to-infrastructure (V2I) communications. Since the real-world CAV data is not currently available as of today, simulation is the most commonly used platform to evaluate the future V2X system. Although several studies evaluated the effectiveness of CAVs in a small roadway network, there is a lack of studies analyzing the impact of CAVs at the network level by considering both …
A Deep Learning Approach For Real-Time Crash Risk Prediction At Urban Arterials, Pei Li
A Deep Learning Approach For Real-Time Crash Risk Prediction At Urban Arterials, Pei Li
Electronic Theses and Dissertations, 2020-2023
Real-time crash risk prediction aims to predict the crash probabilities within a short time period, it is expected to play a crucial role in the advanced traffic management system. However, most of the existing studies only focused on freeways rather than urban arterials because of the complicated traffic environment of the arterials. This thesis proposes a long short-term memory convolutional neural network (LSTM-CNN) to predict the real-time crash risk at arterials. The advantage of this model is it can benefit from both LSTM and CNN. Specifically, LSTM captures the long-term dependency of the data while CNN extracts the time-invariant features. …
Investigation Of Recurrent Neural Network Architectures Based Deep Learning For Short-Term Traffic Speed Forecasting, Armando Fandango
Investigation Of Recurrent Neural Network Architectures Based Deep Learning For Short-Term Traffic Speed Forecasting, Armando Fandango
Electronic Theses and Dissertations, 2020-2023
The availability of large tranches of data and its influence on traffic flow, make the problem of short-term traffic speed prediction very complex in nature. For more than 40 years, various statistical time series forecasting methods have been applied for traffic speed prediction, and in the last 20 years, machine learning-based methods have gained prevalence. However, more recently, recurrent neural network (RNN) based methods have emerged to show better results for traffic speed prediction\cite{tian2015pred, zhao2017lstm,fu2017usin,chen2016long,dai2017deep,dai2019deep, kanestrom2017traf,shao2016traf,jia2017traf}. As the interest in applying RNN models to the traffic speed predictions started to grow, we found some critical and important unanswered questions with …
Improving Traffic Safety And Efficiency By Adaptive Signal Control Systems Based On Deep Reinforcement Learning, Yaobang Gong
Improving Traffic Safety And Efficiency By Adaptive Signal Control Systems Based On Deep Reinforcement Learning, Yaobang Gong
Electronic Theses and Dissertations, 2020-2023
As one of the most important Active Traffic Management strategies, Adaptive Traffic Signal Control (ATSC) helps improve traffic operation of signalized arterials and urban roads by adjusting the signal timing to accommodate real-time traffic conditions. Recently, with the rapid development of artificial intelligence, many researchers have employed deep reinforcement learning (DRL) algorithms to develop ATSCs. However, most of them are not practice-ready. The reasons are two-fold: first, they are not developed based on real-world traffic dynamics and most of them require the complete information of the entire traffic system. Second, their impact on traffic safety is always a concern by …