No description available.

<div> <div> <p>This dissertation discussed the potential benefits that CAV systems can bring to the general well-being, and how the threat lies within the CAV system can affect its performance and functionality.<br></p> <p>Particularly, this dissertation discovered how CAV technology can benefit homeland security and crime investigations involving child abduction crimes. By proposing the initial design network, this dissertation proposed a solution that enhances the current AMBER Alert system using CAV technology. This dissertation also discussed how CAV technology can help perception in corner-case driving scenarios and reduce the risk of traffic accidents, by proposing a dataset that covers various corner cases including different weather and lighting conditions targeting the work zone. Evaluation is made on the collected data and several impact factors have been figured out. </p> <p>This dissertation also discussed an attack scenario that a ROS-based CAV platform was attacked by DoS attacks. We analized the system response after we attacked the system. Discussion and analysis was made on the functionality and stability of the system. </p> <p>Overall, we determined that CAV technology can greatly benefit in general well-being, and threats within the CAV system can cast potential negative benefits once the CAV system is being attacked. </p> </div> </div>

No description available.

No description available.

Vehicle trajectory data has played a critical role in the recent history of traffic flow and CAV operations-related studies. However, available trajectories have limited coverage, either spatial or temporal. The implementation of CAV technology is expected to produce a large-scale trajectory dataset. However, at the initial implementation level, the trajectory data produced is expected to have gaps in terms of completeness. This research develops a data model for large-scale trajectory data that can be built on CAV-collected trajectories and easily manipulated to produce traffic parameters for CAV control and operation research. A benchmarking process has been applied to test a trajectory reconstruction approach to develop a population database from partial trajectories to fill the expected data gap in CAV feedback. The large-scale trajectory data is then used in CAV operations-related studies focusing on CAV's integration with human drivers and developing performance matrices for CAV-controlled optimized trajectories. This research used large-scale vehicle trajectory data from Wide Area Motion Imagery (WAMI) developed by PVLabs for modeling and analyzing traffic characteristics as a surrogate of CAV-collected trajectories. This timestamped location data capture provides trajectory information at an interval of one second. Trajectories from an approximate area of four-square kilometers in downtown Hamilton, Canada, are used to develop a data model to extract and store traffic characteristics. The video data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like other moving object detection-based algorithms, this data suffers from false-positive detection, false-negative detection, and other positional inaccuracies caused by faulty image registration. A context-based trajectory filtering algorithm has been developed and validated against ten minutes of vehicle counts from actual WAMI images. The filtered data provides a sample of trajectories over the area, including complete and partial vehicle trajectories, excluding undetected ones. The missing trajectory reconstruction process using a dynamic state estimation process is developed to reconstruct partial and missing trajectories. A data analytics approach predicts the number of missing trajectories between two successive detections in the traffic stream on a roadway lane. A benchmarking test of the performance of the missing trajectory prediction algorithm is conducted using the NGSIM I80 database. A frame-by-frame learning method is developed to join the identified missing trajectories. This data analytics approach preserves the naturalistic property of the trajectory, which was a concern of previous traffic-flow model-based approaches. Joining partial/split trajectories provides a more comprehensive picture of the trajectory population. Due to data structure similarities, including the nature of the split and missing trajectories, the methods developed in this study to recover trajectories can be adopted for future CAV feedback data in a mixed traffic scenario. The applicability of using the large-scale trajectory data model is explored in two performance areas of CAV operations. The first is a scenario-based testing process, which evaluates the "intelligence" of a CAV in handling interactions with Human driven Vehicles (HV) by artificially replacing an HV in the traffic stream with a CAV. Scenario-based testing is conducted for a particular Operational Design Domain (ODD). The ODD is defined as operating conditions under which particular driver assistance or automated control systems are designed to function. Existing literature on scenario-based testing primarily focuses on CAV-HV interaction on highways as large-scale naturalistic trajectory data are available to facilitate such studies. This research explores car-following and lane-changing aspects of arterial CAV testing. The large-scale trajectory data model generates testing scenarios and calibrates the surrogate model for CAV operation. The modification to the trajectory data model to accommodate the scenario-based testing is illustrated. The second consists of using the large-scale trajectory data model to estimate a new trajectory smoothness parameter that can indicate the impact of intersection stop-and-go movement on the smoothness of the entire trajectory. This smoothness parameter can be applied as an optimization variable in future trajectory control-based intersection management. Long-duration trajectories from the large-scale trajectory data are used to estimate the spectral arc length parameter for trajectory smoothness. This research only estimates smoothness parameters for human-driven vehicles to illustrate its applicability for vehicle trajectories. This research developed a framework for applying expected partial trajectories from CAV technology in estimating near-complete trajectories. The large-scale data application process in two CAV operations-related studies is also provided. / Doctor of Philosophy / The decision-making process undertaken by transportation agencies for planning, evaluating, and operating transportation facilities relies on analyzing traffic and driver behavior for prevailing and future traffic conditions. The analytical tools for policy, design, decision-making, and safety analysis use aggregated and disaggregated traffic parameters. Traffic parameters are information about the dynamic state of the traffic. In the case of a vehicle, the dynamic state information can be location, speed, acceleration, heading, and spacing with other vehicles in the traffic stream. The sequence of these dynamic parameters is called vehicle trajectories in a broader term. The trajectory information is collected using several direct and indirect collection systems. The implementation of CAV technologies is expected to provide a new source of vehicle trajectory information. Trajectory data are integral to CAV safety, operational evaluation, and optimization control algorithms. Trajectory data are also used to develop, calibrate, and validate the models representing a particular aspect of human driver behavior, and the recent development of CAV has elevated the necessity and application of trajectory data. As a result, a significant demand exists in academia and industry for the procedure to create trajectories of the vehicle population in the traffic stream. The trajectory population represents the dynamic properties of all the vehicles moving over the data collection area. The primary goal of this research is to develop and apply a large-scale trajectory population database. Trajectories are typically stored in a Moving Object Database (MOD). This research leverages a MOD database collected by a new generation of Wide-Area Motion Imagery (WAMI). The WAMI system collects images from a high-altitude moving aerial platform with high-definition cameras at a fixed time interval, which captures the trajectories of vehicles in the collection area. However, validating the created trajectories for completeness and data noise revealed continuity and consistency gaps in trajectories. A multistep data mining process is undertaken to filter, process, and extract sample trajectories with reduced data noise. A trajectory reconstruction task is undertaken to reduce the data gap. A benchmarking performance test for trajectory reconstruction is conducted using NGSIM I80 data because it has been validated in multiple studies and contains trajectories of all vehicles during the collection period (i.e., trajectory population). The trajectory reconstruction methodology developed in this research can be adapted for future CAV-collected partial trajectory data. The development of the trajectory reconstruction methodology and training data created from NGSIM I80 is one of the main contributions of this research in the field of trajectory reconstruction. Several traffic flow measures are then estimated from the sample trajectories that outline the analytical requirements to integrate trajectory data with roadway infrastructure. A data model is developed to store and manipulate dynamic trajectory parameters efficiently. The resulting data processing and integration process can be applied to CAV-collected trajectories to create an analytical trajectory database. The large-scale trajectory database is used to illustrate its capability in evaluating CAV operating models, specifically the car-following and lane-changing models on an arterial network. The car-following model mimics the longitudinal movement of real-world drivers following another vehicle. The lane-changing model predicts lane-changing behavior due to path-planning requirements and navigating surrounding traffic conditions. The overall operational model evaluation process is called accelerated evaluation, in which the naturalistic vehicle movement data is used to measure CAV's operational and safety performance. For a second application of the large-scale trajectory data, long-duration trajectories are used to develop a trajectory smoothness performance measure that can be used to test different trajectory control approaches for intersection movement management. This research is one of the early attempts to leverage large-scale vehicle trajectory datasets in transportation engineering applications. Its primary contribution is the development of a comprehensive trajectory validation methodology that can be applied to future CAV feedback to produce a trajectory population database with enhanced analytical capability. The secondary output of this research is benchmarking results for different analytical methodologies to develop the trajectories that can be used in future research and development as a reference.

The expectations of potential users of Connected and Automated Vehicles (CAVs) reveal howusers conceptualize the technology, how they expect it to serve them and what they need from the service. Previous studies of CAVs have concentrated research on user adoption, willingness to use, pay, and future challenges of the technology. However, a few studies have explored the expectations of potentialusers of CAVs. The knowledge of the expectations of potential users is essential for service designers to understand the needs of each category of users to enhance user-level satisfaction and prioritize different alternatives for service improvements. Through a qualitative and explorative study of potential users inSweden, this study presents three categories of the expectations of potential users of CAVs: Optimistic,Pessimistic, and Contradictory expectations. The Optimistic expectations represent potential users'positive insights of what they need for CAVs to be a successful innovation. The Pessimistic expectations relate to the potential user's hope that adverse events will happen in the introduction of CAVs and thatthe service will produce negative outcomes. The Contradictory expectations are conflicting expectations that potential users have for CAVs which share both optimistic and pessimistic views. This studydiscusses the implications of the categories of the expectations of potential users for service designersand researchers. The study also proposes future recommendations for the extension of this researchwork.

No description available.

No description available.

<p><a></a></p><p>Emerging technologies in transportation engineering including connected and automated vehicles (CAVs) exhibit much potential to solve a variety of persistent problems that have impaired the safety and mobility performance of transportation systems. A well-known context of such problems is the construction work zone where agencies have grappled with solutions that range from no closure, partial closure to full closure of road sections during construction, rehabilitation, or maintenance work. Road agencies also seek to develop and implement such workzone plans in a manner that does not unduly jeopardize the economic, social and environmental resources of the road users and the community where the workzone is located. In order to ensure that these three components of sustainable development are attained during road construction workzone management, road agencies seek to develop and implement tools that they can use to guide road users in a network to minimize overall delay, emissions, and fuel consumption. This thesis examines this specific context of highway administration. The thesis developed detour routing guidance for the road users in a road network with work zones in case of full closure, in a manner that is consistent with sustainable development. The research did this for the Automated vehicles (this unlikely scenario is merely considered to demonstrate the potential of connectivity in the network) and the era of connected and automated vehicles. In doing this, the thesis identified the potential benefits that CAV technology can offer in sustainable systemwide management of road work zones. The thesis considered the following sustainability-related evaluation criteria: economic (accessibility to businesses, user costs of fuel consumption, and user costs of travel delay; social (rapid access by emergency services such as ambulance); and environmental (noise pollution and Greenhouse Gas (GHG) emissions). The routing optimization was modeled as a linear programming problem and numerical experiments were carried out. The road network of Sioux Falls city was used to demonstrate the study results. The results suggest that the developed optimal sustainable routing scheme yielded significant improvement in terms of the sustainability criteria while maintaining the acceptable levels of service The results also provided insights on the prospective benefits of routing schemes developed via system optimal management (achieved through centrally-guided detour movements that is facilitated by CAV technology) vis-à-vis user equilibrium management, specifically, Nash Equilibrium.<br></p>

No description available.