Integrated Traffic and Communication Performance Evaluation of an Intelligent Vehicle Infrastructure Integration (VII) System for Online Travel Time Prediction

This paper presents a framework for online highway travel time prediction using traffic measurements that are likely to be available from Vehicle Infrastructure Integration (VII) systems, in which vehicle and infrastructure devices communicate to improve mobility and safety. In the proposed intelligent VII system, two artificial intelligence (AI) paradigms, namely Artificial Neural Networks (ANN) and Support Vector Regression (SVR), are used to determine future travel time based on such information as current travel time, VII-enabled vehicles’ flow and density. The development and performance evaluation of the VII-ANN and VII-SVR frameworks, in both of the traffic and communications domains, were conducted, using an integrated simulation platform, for a highway network in Greenville, South Carolina. Specifically, the simulation platform allows for implementing traffic surveillance and management methods in the traffic simulator PARAMICS, and for evaluating different communication protocols and network parameters in the communication network simulator, ns-2. The study’s findings reveal that the designed communications system was capable of supporting the travel time prediction functionality. They also demonstrate that the travel time prediction accuracy of the VII-AI framework was superior to a baseline instantaneous travel time prediction algorithm, with the VII-SVR model slightly outperforming the VII-ANN model. Moreover, the VII-AI framework was shown to be capable of performing reasonably well during non-recurrent congestion scenarios, which traditionally have challenged traffic sensor-based highway travel time prediction methods

A REAL-TIME TRAFFIC CONDITION ASSESSMENT AND PREDICTION FRAMEWORK USING VEHICLE-INFRASTRUCTURE INTEGRATION (VII) WITH COMPUTATIONAL INTELLIGENCE

This research developed a real-time traffic condition assessment and prediction framework using Vehicle-Infrastructure Integration (VII) with computational intelligence to improve the existing traffic surveillance system. Due to the prohibited expenses and complexity involved for the field experiment of such a system, this study adopted state-of-the-art simulation tools as an efficient alternative. This work developed an integrated traffic and communication simulation platform to facilitate the design and evaluation of a wide range of online traffic surveillance and management system in both traffic and communication domain. Using the integrated simulator, the author evaluated the performance of different combination of communication medium and architecture. This evaluation led to the development of a hybrid VII framework exemplified by hierarchical architecture, which is expected to eliminate single point failures, enhance scalability and easy integration of control functions for traffic condition assessment and prediction. In the proposed VII framework, the vehicle on-board equipments and roadside units (RSUs) work collaboratively, based on an intelligent paradigm known as \u27Support Vector Machine (SVM),\u27 to determine the occurrence and characteristics of an incident with the kinetics data generated by vehicles. In addition to incident detection, this research also integrated the computational intelligence paradigm called \u27Support Vector Regression (SVR)\u27 within the hybrid VII framework for improving the travel time prediction capabilities, and supporting on-line leaning functions to improve its performance over time. Two simulation models that fully implemented the functionalities of real-time traffic surveillance were developed on calibrated and validated simulation network for study sites in Greenville and Spartanburg, South Carolina. The simulation models\u27 encouraging performance on traffic condition assessment and prediction justifies further research on field experiment of such a system to address various research issues in the areas covered by this work, such as availability and accuracy of vehicle kinetic and maneuver data, reliability of wireless communication, maintenance of RSUs and wireless repeaters. The impact of this research will provide a reliable alternative to traditional traffic sensors to assess and predict the condition of the transportation system. The integrated simulation methodology and open source software will provide a tool for design and evaluation of any real-time traffic surveillance and management systems. Additionally, the developed VII simulation models will be made available for use by future researchers and designers of other similar VII systems. Future implementation of the research in the private and public sector will result in new VII related equipment in vehicles, greater control of traffic loading, faster incident detection, improved safety, mitigated congestion, and reduced emissions and fuel consumption

VEHICLE-INFRASTRUCTURE INTEGRATION (VII) ENABLED PLUG-IN HYBRID ELECTRIC VEHICLES (PHEVS) FOR TRAFFIC AND ENERGY MANAGEMENT

Vehicle Infrastructure Integration (VII) program (also known as IntelliDrive) has proven the potential to improve transportation conditions by enabling the communication between vehicles and infrastructure, which provides a wide range of applications in transportation safety and mobility. Plug-in hybrid electric vehicles (PHEVs) that utilize both electrical and gasoline energy are a commercially viable technology with potential to contribute to both sustainable development and environmental conservation through increased fuel economy and reduced emissions. Considering positive potentials of PHEVs and VII in ITS, a framework that integrates PHEVs with VII technology was created in this research utilizing vehicle-to-vehicle and vehicle-to-infrastructure communications for transmitting real time and predicted traffic information. This framework aims to adjust the vehicle speed at each time interval on its driving mission and dynamically optimize the total energy consumption during the trip. Equivalent Consumption Minimization Strategy (ECMS) was utilized as the control strategy of PHEVs energy management for minimization of the equivalent energy. It was found that VII traffic information has the capability to benefit energy management, as presented in this thesis, while supporting the broader national transportation goals of an active transportation system where drivers, vehicles and infrastructure are integrated in a real time fashion to improve overall traffic conditions

Connected and Autonomous Vehicles Applications Development and Evaluation for Transportation Cyber-Physical Systems

Cyber-Physical Systems (CPS) seamlessly integrate computation, networking and physical devices. A Connected and Autonomous Vehicle (CAV) system in which each vehicle can wirelessly communicate and share data with other vehicles or infrastructures (e.g., traffic signal, roadside unit), requires a Transportation Cyber-Physical System (TCPS) for improving safety and mobility, and reducing greenhouse gas emissions. Unfortunately, a typical TCPS with a centralized computing service cannot support real-time CAV applications due to the often unpredictable network latency, high data loss rate and expensive communication bandwidth, especially in a mobile network, such as a CAV environment. Edge computing, a new concept for the CPS, distributes the resources for communication, computation, control, and storage at different edges of the systems. TCPS with edge computing strategy forms an edge-centric TCPS. This edge-centric TCPS system can reduce data loss and data delivery delay, and fulfill the high bandwidth requirements. Within the edge-centric TCPS, Vehicle-to-X (V2X) communication, along with the in-vehicle sensors, provides a 360-degree view for CAVs that enables autonomous vehicles’ operation beyond the sensor range. The addition of wireless connectivity would improve the operational efficiency of CAVs by providing real-time roadway information, such as traffic signal phasing and timing information, downstream traffic incident alerts, and predicting future traffic queue information. In addition, temporal variation of roadway traffic can be captured by sharing Basic Safety Messages (BSMs) from each vehicle through the communication between vehicles as well as with roadside infrastructures (e.g., traffic signal, roadside unit) and traffic management centers. In the early days of CAVs, data will be collected only from a limited number of CAVs due to a low CAV penetration rate and not from other non-connected vehicles. This will result in noise in the traffic data because of low penetration rate of CAVs. This lack of data combined with the data loss rate in the wireless CAV environment makes it challenging to predict traffic behavior, which is dynamic over time. To address this challenge, it is important to develop and evaluate a machine learning technique to capture stochastic variation in traffic patterns over time. This dissertation focuses on the development and evaluation of various connected and autonomous vehicles applications in an edge-centric TCPS. It includes adaptive queue prediction, traffic data prediction, dynamic routing and Cooperative Adaptive Cruise Control (CACC) applications. An adaptive queue prediction algorithm is described in Chapter 2 for predicting real-time traffic queue status in an edge-centric TCPS. Chapter 3 presents noise reduction models to reduce the noise from the traffic data generated from the BSMs at different penetration of CAVs and evaluate the performance of the Long Short-Term Memory (LSTM) prediction model for predicting traffic data using the resulting filtered data set. The development and evaluation of a dynamic routing application in a CV environment is detailed in Chapter 4 to reduce incident recovery time and increase safety on a freeway. The development of an evaluation framework is detailed in Chapter 5 to evaluate car-following models for CACC controller design in terms of vehicle dynamics and string stability to ensure user acceptance is detailed in Chapter 5. Innovative methods presented in this dissertation were proven to be providing positive improvements in transportation mobility. These research will lead to the real-world deployment of these applications in an edge-centric TCPS as the dissertation focuses on the edge-centric TCPS deployment strategy. In addition, as multiple CAV applications as presented in this dissertation can be supported simultaneously by the same TCPS, public investments will only include infrastructure investments, such as investments in roadside infrastructure and back-end computing infrastructure. These connected and autonomous vehicle applications can potentially provide significant economic benefits compared to its cost

A Robust Data Exchange Framework for Connected Vehicle Technology Supported Dynamic Transit Operations

Transit systems are an integral part of surface transportation systems. A connected vehicle technology (CVT) supported transit system will assist the users to manage trips both dynamically and efficiently. The primary focus of this research is to develop and evaluate the performance of a secure, scalable, and resilient data exchange framework. In the developed data exchange framework, a new data analytics layer, named Transit Cloud, is used to receive data from different sources, and send it to different users for a Dynamic Transit Operations (DTO) application. The DTO application allows the transit users to request trip information and obtain itineraries, using their personal information devices, (e.g., cell phone), and provides dynamic routing and scheduling information to the transit operators. A case study was conducted to investigate the effectiveness of the developed data exchange framework, by comparing the framework with the USDOT recommended data delivery delay requirements. This data exchange framework was simulated in the CloudLab, a distributed cloud infrastructure, in which, the data exchange delay for DTO was examined for different simulation scenarios, utilizing the synthetic data generated from Connected Vehicle Reference Implementation Architecture (CVRIA) and Research Data Exchange (RDE). Security, scalability, and resiliency of the developed data exchange framework are illustrated in this thesis. The results from the simulation network reveal that the data exchange delay satisfies the USDOT data delivery delay requirements. This suggests that the developed secure, scalable, and resilient data exchange framework, which is presented in this study, meets the application performance requirements. Thus, Transit Cloud is a more preferable alternative than the existing framework because of its added benefits in terms of security, scalability, and resiliency

Enhanced Concrete Bridge Assessment Using Artificial Intelligence and Mixed Reality

Conventional methods for visual assessment of civil infrastructures have certain limitations, such as subjectivity of the collected data, long inspection time, and high cost of labor. Although some new technologies (i.e. robotic techniques) that are currently in practice can collect objective, quantified data, the inspector\u27s own expertise is still critical in many instances since these technologies are not designed to work interactively with human inspector. This study aims to create a smart, human-centered method that offers significant contributions to infrastructure inspection, maintenance, management practice, and safety for the bridge owners. By developing a smart Mixed Reality (MR) framework, which can be integrated into a wearable holographic headset device, a bridge inspector, for example, can automatically analyze a certain defect such as a crack that he or she sees on an element, display its dimension information in real-time along with the condition state. Such systems can potentially decrease the time and cost of infrastructure inspections by accelerating essential tasks of the inspector such as defect measurement, condition assessment and data processing to management systems. The human centered artificial intelligence (AI) will help the inspector collect more quantified and objective data while incorporating inspector\u27s professional judgment. This study explains in detail the described system and related methodologies of implementing attention guided semi-supervised deep learning into mixed reality technology, which interacts with the human inspector during assessment. Thereby, the inspector and the AI will collaborate/communicate for improved visual inspection

Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The safety, mobility, environmental and economic benefits of Connected and Autonomous Vehicles (CAVs) are potentially dramatic. However, realization of these benefits largely hinges on the timely upgrading of the existing transportation system. CAVs must be enabled to send and receive data to and from other vehicles and drivers (V2V communication) and to and from infrastructure (V2I communication). Further, infrastructure and the transportation agencies that manage it must be able to collect, process, distribute and archive these data quickly, reliably, and securely. This paper focuses on current digital roadway infrastructure initiatives and highlights the importance of including digital infrastructure investment alongside more traditional infrastructure investment to keep up with the auto industry's push towards this real time communication and data processing capability. Agencies responsible for transportation infrastructure construction and management must collaborate, establishing national and international platforms to guide the planning, deployment and management of digital infrastructure in their jurisdictions. This will help create standardized interoperable national and international systems so that CAV technology is not deployed in a haphazard and uncoordinated manner

Low-cost deep learning UAV and Raspberry Pi solution to real time pavement condition assessment

In this thesis, a real-time and low-cost solution to the autonomous condition assessment of pavement is proposed using deep learning, Unmanned Aerial Vehicle (UAV) and Raspberry Pi tiny computer technologies, which makes roads maintenance and renovation management more efficient and cost effective. A comparison study was conducted to compare the performance of seven different combinations of meta-architectures for pavement distress classification. It was observed that real-time object detection architecture SSD with MobileNet feature extractor is the best combination for real-time defect detection to be used by tiny computers. A low-cost Raspberry Pi smart defect detector camera was configured using the trained SSD MobileNet v1, which can be deployed with UAV for real-time and remote pavement condition assessment. The preliminary results show that the smart pavement detector camera achieves an accuracy of 60% at 1.2 frames per second in raspberry pi and 96% at 13.8 frames per second in CPU-based computer

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain