This paper presents the development of a novel decentralized freeway merge assistance system using the Dedicated Short Range Communication (DSRC) technology. The system provides visual advisories on a Google map through a smart phone application. To the best of our knowledge, this is the first implementation of a DSRC-based freeway merging assistance system-integrated with smart phone application via Bluetooth-that has been tested in real-world on an interstate highway in an uncontrolled environment. Results from field operational tests indicate that this system can successfully advise drivers towards a collaborative and smooth merging experience on typical "Diamond" interchanges.

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Cyber security is evidently more important in todays and tomorrows connected and autonomous vehicles because the increasing connectivity in these vehicles is widening the vehicle attack surface area. Without having a cyber-secure vehicle, it is not possible to argue that connected and autonomous vehicles are safe for all road-users. Two cyber security references UN R155 and ISO 21434 were released to help vehicle manufacturers tackle cyber security risks. To comply with R155 and ISO 21434, security engineers must conduct threat analysis and risk assessment (TARA) and conduct attack path analysis to identify the possible attack paths that could be used to exploit a connected vehicle. In this paper, the researcher looked into the MITRE attack framework which stores attackers’ tactics, techniques and procedures and the automotive attack matrix by VicOne to build and create knowledge that would be helpful in conducting attack path analysis. The researcher collaborated with AFRY to conduct an action research which resulted in the creation of a novel threat (TARA)catalogue from the Mitre attack framework, automotive attack matrix and the annex 5 threats in R155. The threat catalogue developed in the study is an initial step in providing structure to the process of conducting attack path analysis.

This dissertation discusses network wide signal control strategies base on connected vehicle technology. Traffic congestion on arterials has become one of the largest threats to economic competitiveness, livability, safety, and long-term environmental sustainability in the United States. In addition, arterials usually experience more blockage than freeways, specifically in terms of intersection congestion. There is no doubt that emerging technologies provide unequaled opportunities to revolutionize “retiming” and mitigate traffic congestion. Connected vehicle technology provides unparalleled safety benefits and holds promise in terms of alleviating both traffic congestion and the environmental impacts of future transportation systems. The objective of this research is to improve the mobility, safety and environmental effects at signalized arterials with connected vehicles. The proposed solution of this dissertation is to formulate traffic signal control models for signalized arterials based on connected vehicle technology. The models optimize offset, split, and cycle length to minimize total queue delay in all directions of coordinated intersections. Then, the models are implemented in a centralized system—including closed-loop systems—first, before expanding the results to distributed systems. The benefits of the models are realized at the infant stage of connected vehicle deployment when the penetration rate of connected vehicles is around 10%. Furthermore, the benefits incentivize the growth of the penetration rate for drivers. In addition, this dissertation contains a performance evaluation in traffic delay, volume throughput, fuel consumption, emission, and safety by providing a case study of coordinated signalized intersections. The case study results show the solution of this dissertation could adapt early deployment of connected vehicle technology and apply to future connected vehicle technology development.

Connected vehicle is a fully connected transportation system in which vehicles, infrastructure, and mobile devices are enabled to exchange information in real-time to bring advancements in transportation operations. It is important to incorporate the new characteristics of the connected vehicle in the transportation planning process. Also, it is vital for planning and road agencies to better understand the impacts of connected vehicle on transportation networks, system demand, and travel behavior of road users in order to properly prepare for them. In addition, developers of connected vehicle systems can gain insight into how their systems will impact road users and network performance. When a change in performance of a transportation network occurs it can potentially cause users to change travel modes, known as mode choice. In this research, the change in mode choice, due to the change in network performance by introduction of connected vehicle is studied. This provides a more accurate depiction of the performance of the network and indicates how connected vehicles could change travellers’ preference in travel mode. The effect of this technology is explored on the performance of the Toronto waterfront, in a microsimulation environment. The results show that average travel time increases for high market penetrations when a dynamic route guidance algorithm is implemented, a phenomenon that occurs in dense, and complex traffic networks. Analysis of mode choice shows a loss in the auto mode share, for high market penetrations, due to the increased auto travel times. This loss in the auto mode share is compensated by increases in the other modes. / Thesis / Master of Applied Science (MASc)

Connected Vehicle research has emerged as one of the highest priorities in the transportation systems because connected vehicle technology has the potential to improve safety, mobility, and environment for the current transportation systems. Various connected vehicle based applications have been identified and evaluated through various measurements to assess the performance of connected vehicle applications. However, most of these previous studies have used hypothetical study areas with simple networks for connected vehicle environment. This study represents connected vehicle environment in TRANSIMS to assess the performance of V2V communication applications in the realistic urban network. The communication duration rate and spatial-temporal dispersion of equipped vehicles are investigated to evaluate the capability of V2V communication based on the market penetration rate of equipped vehicles and wireless communication coverage in the whole study area. The area coverage level is used to assess the spatial-temporal dispersion of equipped vehicles for two study areas. The distance of incident information propagation and speed estimation error are used to measure the performance of event-driven and periodic applications based on different market penetration rates of equipped vehicles and wireless communication coverage in both morning peak and non-peak times. The wireless communication coverage is the major factor for event-driven application and the market penetration rate of equipped vehicles has more impact on the performance of periodic application. The required minimum levels of deployment for each application are determined for each scenario. These study findings will be useful for making decisions about investments on deployment of connected vehicle applications to improve the current transportation systems. Notably, event-driven applications can be reliably deployed in the initial stage of deployment despite the low level of market penetration of equipped vehicles. / Ph. D.

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The decision to select the best Intelligent Transportation System (ITS) technologies from available options has always been a challenging task. The availability of connected vehicle/automated vehicle (CV/AV) technologies in the near future is expected to add to the complexity of the ITS investment decision-making process. The goal of this research is to develop a multi-criteria decision-making analysis (MCDA) framework to support traffic agencies’ decision-making process with consideration of CV/AV technologies. The decision to select between technology alternatives is based on identified performance measures and criteria, and constraints associated with each technology. Methods inspired by the literature were developed for incident/bottleneck detection and back-of-queue (BOQ) estimation and warning based on connected vehicle (CV) technologies. The mobility benefits of incident/bottleneck detection with different technologies were assessed using microscopic simulation. The performance of technology alternatives was assessed using simulated CV and traffic detector data in a microscopic simulation environment to be used in the proposed MCDA method for the purpose of alternative selection. In addition to assessing performance measures, there are a number of constraints and risks that need to be assessed in the alternative selection process. Traditional alternative analyses based on deterministic return on investment analysis are unable to capture the risks and uncertainties associated with the investment problem. This research utilizes a combination of a stochastic return on investment and a multi-criteria decision analysis method referred to as the Analytical Hierarchy Process (AHP) to select between ITS deployment alternatives considering emerging technologies. The approach is applied to an ITS investment case study to support freeway bottleneck management. The results of this dissertation indicate that utilizing CV data for freeway segments is significantly more cost-effective than using point detectors in detecting incidents and providing travel time estimates one year after CV technology becomes mandatory for all new vehicles and for corridors with moderate to heavy traffic. However, for corridors with light, there is a probability of CV deployment not being effective in the first few years due to low measurement reliability of travel times and high latency of incident detection, associated with smaller sample sizes of the collected data.

Traveler information systems play a significant role in most travelers’ daily trips. These systems assist travelers in choosing the best routes to reach their destinations and possibly select suitable departure times and modes for their trips. Connected Vehicle (CV) technologies are now in the pilot program stage. Vehicle-to-Infrastructure (V2I) communications will be an important source of data for traffic agencies. If this data is processed properly, then agencies will be able to better determine traffic conditions, allowing them to take proper countermeasures to remedy transportation system problems under different conditions. This research focuses on developing methods to assess the potential of utilizing CV data to support the traveler information system data collection process. The results from the assessment can be used to establish a timeline indicating when an agency can stop investing, at least partially, in traditional technologies, and instead rely on CV technologies for traveler information system support. This research utilizes real-world vehicle trajectory data collected under the Next Generation Simulation (NGSIM) program and simulation modeling to emulate the use of connected vehicle data to support the traveler information system. NGSIM datasets collected from an arterial segment and a freeway segment are used in this research. Microscopic simulation modeling is also used to generate required trajectory data, allowing further analysis, which is not possible using NGSIM data. The first step is to predict the market penetration of connected vehicles in future years. This estimated market penetration is then used for the evaluation of the effectiveness of CV-based data for travel time and volume estimation, which are two important inputs for the traveler information system. The travel times are estimated at different market penetrations of CV. The quality of the estimation is assessed by investigating the accuracy and reliability with different CV deployment scenarios. The quality of volume estimates is also assessed using the same data with different future scenarios of CV deployment and partial or no detector data. Such assessment supports the identification of a timeline indicating when CV data can be used to support the traveler information system.