Asset managers continue to prepare physical infrastructure investments needed to accommodate the emerging technologies, namely vehicle connectivity, electrification, and automation. The provision of new infrastructure and modification of existing infrastructure is expected to incur a significant amount of capital investment. Secondly, with increasing EV and CAV operations, the revenues typically earned from vehicle registrations and fuel tax are expected to change due to changing demand for vehicle ownership and amount of travel, respectively. This research estimated (i) the changes in highway expenditures in an era of ECAV operations, (ii) the net change in highway revenues that can be expected to arise from ECAV operations, and (iii) the changes in user equity across the highway user groups (vehicle classes). In assessing the changes in highway expenditures, the research developed a model to predict the cost of highway infrastructure stewardship based on current and/ or future system usage. <div><br></div><div>The results of the research reveal that CAVs are expected to significantly change the travel patterns, leading to increased system usage which in turn results in increased wear and tear on highway infrastructure. This, with the need for new infrastructure to support and accommodate the new technologies is expected to result in increased highway expenditure. At the same time, CAVs are expected to have significantly improved fuel economy as compared to their human driven counterparts, leading to a decrease in fuel consumption per vehicle, resulting in reduced fuel revenues. Furthermore, the prominence of EVs is expected to exacerbate this problem. This thesis proposed a revision to the current user fee structure to address these impacts. This revision contains two major parts designed to address the system efficiency and equity in the near and long term. For the near term, this thesis recommended a variable tax scheme under which each vehicle class pays a different fuel tax rate. This ensures that both equity and system efficiency are improved during the transition to ECAV. In the long term, this thesis recommended supplementing the fuel tax with a distance based VMT tax, applicable to electric vehicles.<br></div>

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<p dir="ltr">Motion planning for Autonomous Vehicles (AVs) and Connected Autonomous Vehicles (CAVs) involves the crucial task of translating road environmental data obtained from sensors and connectivity devices into a sequence of executable vehicle actions. This task is critical for AVs and CAVs, because the efficacy of their driving decisions and overall performance depend on the quality of motion planning.</p><p dir="ltr">In the context of motion planning technologies, several fundamental questions and challenges remain despite the widespread adoption of advanced learning-based methods, including deep learning (DL) and deep reinforcement learning (DRL). In this regard, the following critical questions need to be answered: 1) How to design suitable DL architectures to comprehensively understand the driving scenario by integrating data from diverse sources including sensors and connectivity devices? 2) How to effectively use the fused information to make improved driving decisions, accounting for various optimality criteria? 3) How to leverage vehicle connectivity to generate cooperative decisions for multiple CAVs, in a manner that optimizes system-wide utility? 4) How to address the inherent interpretability limitations of DL-based methods to enhance user trust in AVs and CAVs? 5) Is it possible to extend learning-based approaches to operational-level decisions in a way that overcomes the inherent disadvantage of low explainability and lack of safety guarantee?</p><p dir="ltr">In an effort to address these questions and expand the existing knowledge in this domain, this dissertation introduces several learning-based motion planning frameworks tailored towards different driving scenarios of AV and CAV. Technically, these efforts target on developing trustworthy AI systems with a focus on the information fusion, “explainable AI” or XAI and safety critical AI. From a computational perspective, these frameworks introduce new learning-based models with state-of-the-art (SOTA) structures, including Convolutional Neural Network (CNN). Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), Attention networks, and Transformers. They also incorporate reinforcement learning (RL) agents, such as Deep Q Networks (DQN) and Model-based RL. From an application standpoint, these developed frameworks can be deployed directly in AVs and CAVs at Level 3 and above. This can enhance the AV/CAV performance in terms of individual and system performance metrics, including safety, mobility, efficiency, and driving comfort.</p>

Public transport prioritization is used at signalized intersections to reduce travel times and increase the attractiveness of public transport. In the future, analog communication technologies for public transport prioritization are soon to be replaced by the promising vehicle-to-everything (V2X) technology. This abstract presents a holistic approach using V2X communication in public transport prioritization for an automated vehicle. In order to take full advantage of the V2X technology, this means to V2X-enable the traffic infrastructure and change the way of communication as well as the traffic light control. The approach was implemented and tested under real-life conditions at the research intersection Tostmannplatz in Braunschweig.

The landscape of vehicular transportation is quickly shifting as emerging technologies continue to increase in intelligence and complexity. From the introduction of Intelligent Transportation Systems (ITS) to the quickly developing field of Connected and Autonomous Vehicles (CAVs), the transportation industry is experiencing a shift in focus. A move to more autonomous and intelligent transportation systems brings with it a promise of increased equity, efficiency, and safety. However, one aspect that is overlooked in this shift is cybersecurity. As intelligent systems and vehicles have been introduced, a large amount of research has been conducted showing vulnerabilities in them. With a new connected transportation system emerging, a multidisciplinary approach will be required to develop a cyber-resilient network. Ensuring protection against cyberattacks and developing a system that can handle their consequences is a key objective moving forward. The first step to developing this system is understanding how different cyberattacks can negatively impact the operations of the transportation system. This research aimed to quantify the safety and efficiency impacts of an attack on the transportation network. To do so, a simulation was developed using Veins software to model a network of intelligent intersections in an urban environment. Vehicles communicated with Road-Side Units (RSUs) to make intersection reservations – effectively simulating CAV vehicle network. Denial of Service (DoS) and Man in the Middle (MITM) attacks were simulated by dropping and delaying vehicle's intersection reservation requests, respectively. Attacks were modeled with varying degrees of severity by changing the number of infected RSUs in the system and their attack success rates. Data analysis showed that severe attacks, either from a DoS or MITM attack, can have significant impact on the transportation network's operations. The worst-case scenario for each introduced an over 20% increase in delay per vehicle. The simulation showed also that increasing the number of compromised RSUs directly related to decreased safety and operational efficiency. Successful attacks also produced a high level of variance in their impact. One other key finding was that a single compromised RSU had very limited impact on the transportation network. These findings highlight the importance of developing security and resilience in a connected vehicle environment. Building a network that can respond to an initial attack and prevent an attack's dissemination through the network is crucial in limiting the negative effects of the attack. If proper resilience planning is not implemented for the next generation of transportation, adversaries could cause great harm to safety and efficiency with relative ease. The next generation of vehicular transportation must be able to withstand cyberattacks to function. Understanding their impact is a key first step for engineers and planners on the long road to ensuring a secure transportation network. / Master of Science / The landscape of transportation is quickly shifting as transportation technologies continue to increase in intelligence and complexity. The transportation industry is shifting its focus to Connected and Autonomous Vehicles (CAVs). The move to more autonomous and intelligent transportation systems brings with it a promise of increased transportation equity, efficiency, and safety. However, one aspect that is often overlooked in this shift is cybersecurity. As intelligent systems and vehicles have been introduced, a large amount of research has been conducted showing cyber vulnerabilities in them. With a new connected transportation system emerging, a multidisciplinary approach will be required to prevent and handle attacks. Ensuring protection against cyberattacks is a key objective moving forward. The first step to developing this system is understanding how different cyberattacks can negatively impact the operations of the transportation system. This research aimed to measure the safety and efficiency impacts of an attack on the transportation network. To do so, a simulation was developed to model an intelligent urban road network. Vehicles made reservations at each intersection they passed – effectively simulating an autonomous vehicle network. Denial of Service (DoS) and Man in the Middle (MITM) attacks were simulated by dropping, and delaying vehicle's intersection reservation requests, respectively. These cyberattacks were modeled with varying degrees of severity to test the different impacts on the transportation network. Analysis showed that severe attacks can have significant impact on the transportation network's operations. The worst-case scenario for each attack introduced an over 20% increase in delay per vehicle. The simulation showed also that increasing the number of attacked intersections directly related to decreased safety and operational efficiency. Successful attacks also produced a high level of variance in their impact. One other key finding was that a single compromised RSU had very limited impact on the transportation network. These findings highlight the importance of developing security and resilience in a connected vehicle environment. Building a transportation network that can respond to an initial attack and prevent it from impacting the entire network is crucial in limiting the negative effects of the attack. If proper resilience planning is not implemented for CAVs, hackers could cause great harm to safety and efficiency with relative ease. The next generation of vehicular transportation must be able to withstand cyberattacks to function. Understanding their impact is a key first step for engineers and planners on the long road to ensuring a secure transportation network.

Improving road safety and optimizing the traffic flow – these are major challenges at urban intersections. In particular, strengthening the needs of vulnerable road users (VRUs) such as pedestrians, cyclists and e-scooter drivers is becoming increasingly important, combined with support for automated and connected driving. In the LTSA project, a new system is being developed and implemented exactly for this purpose. The LTSA is an intelligent infrastructure system that records the movements of all road users in the vicinity of an intersection using a combination of several locally installed sensors e.g. video, radar, lidar. AI-based software processes the detected data, interprets the movement patterns of road users and continuously analyzes the current traffic situation (digital twin). Potentially dangerous situations are identified, e.g. right turning vehicles and simultaneously crossing VRUs, and warning messages can be sent to connected road users via vehicle-to-infrastructure communication (V2X). Automated vehicles can thus adapt their driving maneuvers. In addition, the collected data is applied to improve traffic light control depending on the current traffic situation, especially for VRUs. This abstract describes the LTSA system and its implementation in the German city of Potsdam. The current project state is presented and an outlook on next steps is given.

<p>  </p> <p>Advancements in technology have propelled the availability of enriched and more frequent information about traffic conditions as well as the external factors that impact traffic such as weather, emergency response etc. Most newer vehicles are equipped with sensors that transmit their data back to the original equipment manufacturer (OEM) at near real-time fidelity. A growing number of such connected vehicles (CV) and the advent of third-party data collectors from various OEMs have made big data for traffic commercially available for use. Agencies maintaining and managing surface transportation are presented with opportunities to leverage such big data for efficiency gains. The focus of this dissertation is enhancing the use of CV data and applications derived from fusing it with other datasets to extract meaningful information that will aid agencies in data driven efficient decision making to improve network wide mobility and safety performance.   </p> <p>One of the primary concerns of CV data for agencies is data sampling, particularly during low-volume overnight hours. An evaluation of over 3 billion CV records in May 2022 in Indiana has shown an overall CV penetration rate of 6.3% on interstates and 5.3% on non-interstate roadways. Fusion of CV traffic speeds with precipitation intensity from NOAA’s High-Resolution Rapid-Refresh (HRRR) data over 42 unique rainy days has shown reduction in the average traffic speed by approximately 8.4% during conditions classified as very heavy rain compared to no rain. </p> <p>Both aggregate analysis and disaggregate analysis performed during this study enables agencies and automobile manufacturers to effectively answer the often-asked question of what rain intensity it takes to begin impacting traffic speeds. Proactive measures such as providing advance warnings that improve the situational awareness of motorists and enhance roadway safety should be considered during very heavy rain periods, wind events, and low daylight conditions.</p> <p>Scalable methodologies that can be used to systematically analyze hard braking and speed data were also developed. This study demonstrated both quantitatively and qualitatively how CV data provides an opportunity for near real-time assessment of work zone operations using metrics such as congestion, location-based speed profiles and hard braking. The availability of data across different states and ease of scalability makes the methodology implementable on a state or national basis for tracking any highway work zone with little to no infrastructure investment. These techniques can provide a nationwide opportunity in assessing the current guidelines and giving feedback in updating the design procedures to improve the consistency and safety of construction work zones on a national level.  </p> <p>CV data was also used to evaluate the impact of queue warning trucks sending digital alerts. Hard-braking events were found to decrease by approximately 80% when queue warning trucks were used to alert motorists of impending queues analyzed from 370 hours of queueing with queue trucks present and 58 hours of queueing without the queue trucks present, thus improving work zone safety. </p> <p>Emerging opportunities to identify and measure traffic shock waves and their forming or recovery speed anywhere across a roadway network are provided due to the ubiquity of the CV data providers. A methodology for identifying different shock waves was presented, and among the various case studies found typical backward forming shock wave speeds ranged from 1.75 to 11.76 mph whereas the backward recovery shock wave speeds were between 5.78 to 16.54 mph. The significance of this is illustrated with a case study of  a secondary crash that suggested  accelerating the clearance by 9 minutes could have prevented the secondary crash incident occurring at the back of the queue. Such capability of identifying and measuring shock wave speeds can be utilized by various stakeholders for traffic management decision-making that provide a holistic perspective on the importance of both on scene risk as well as the risk at the back of the queue. Near real-time estimation of shock waves using CV data can recommend travel time prediction models and serve as input variables to navigation systems to identify alternate route choice opportunities ahead of a driver’s time of arrival.   </p> <p>The overall contribution of this thesis is developing scalable methodologies and evaluation techniques to extract valuable information from CV data that aids agencies in operational decision making.</p>