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Full-Text Articles in Engineering
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-
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-
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. …
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-
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. …
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-
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 …
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-
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 …
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-
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 …
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-
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-
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 …
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-
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 …
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-
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. …