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.

Motivated by recent advances in vehicle positioning and vehicle-to-infrastructure (V2I) communication, traffic signal controllers are able to make smarter decisions. Most of the current state-of-the-practice signal priority control systems aim to provide priority for only one mode or based on first-come-first-served logic. Consideration of priority control in a more general framework allows for several different modes of travelers to request priority at any time from any approach and for other traffic control operating principles, such as coordination, to be considered within an integrated signal timing framework. This leads to provision of priority to connected priority eligible vehicles with minimum negative impact on regular vehicles. This dissertation focuses on providing a real-time decision making framework for multi modal traffic signal control that considers several transportation modes in a unified framework using Connected Vehicle (CV) technologies. The unified framework is based on a systems architecture for CVs that is applicable in both simulated and real world (field) testing conditions. The system architecture is used to design both hardware-in-the-loop and software-in-the-loop CV simulation environment. A real-time priority control optimization model and an implementation algorithm are developed using priority eligible vehicles data. The optimization model is extended to include signal coordination concepts. As the penetration rate of the CVs increases, the ability to predict the queue more accurately increases. It is shown that accurate queue prediction improves the performance of the optimization model in reducing priority eligible vehicles delay. The model is generalized to consider regular CVs as well as priority vehicles and coordination priority requests in a unified mathematical model. It is shown than the model can react properly to the decision makers' modal preferences.

Safety in transportation systems is of paramount concern to society; many improvements have been made in recent decades and yet thousands of fatalities still occur annually. Work zones in particular are areas with increased safety risks in transit networks. Advances in electronics have now allowed engineers to merge powerful computing and communication technologies with modern automotive and vehicular technology, known as connected vehicle. Connected vehicle will allow vehicles to exchange data wirelessly with each other and infrastructure to improve safety, mobility and sustainability. This thesis presents a paper that focuses on evaluating the impact of connected vehicle on work zone traffic safety. A dynamic route guidance system based on decaying average-travel-time and shortest path routing was developed and tested in a microscopic traffic simulation environment to avoid routes with work zones. To account for the unpredictable behaviour and psychology of driver’s response to information, three behaviour models, in the form of multinomial distributions, are proposed and studied in this research. The surrogate safety measure improved Time to Collision was used to gauge network safety at various market penetrations of connected vehicles. Results show that higher market penetrations of connected vehicles decrease network safety due to increased average travel distance, while the safest conditions, 5%-10% reduction in critical Time to Collision events, were observed at market penetrations of 20%-40% connected vehicle, with network safety strongly influenced by behaviour model. / Thesis / Master of Applied Science (MASc)

With the rapid development of connected vehicles, automotive security has become one of the most important topics. To study how to protect the security of vehicle communication, we analyze potential threats for connected vehicles and discuss countermeasures to mitigate these threats. In this thesis, we examine 25 services that connected vehicles can provide. Entities, connections, and message flows in these services are investigated and synthesized into a vehicle network structure. The 25 services are divided into six use cases including: infotainment service, remote monitoring, device control, Vehicle-toeverything (V2X), diagnostics service, and in-vehicle Intrusion Detection System (IDS). We establish communication models for these use cases and analyze the potential threats based on Confidentiality, Integrity and Availability (CIA) criteria. We discuss possible countermeasures that can mitigate these threats based on existing network security techniques. Each alternative countermeasure’s advantages and limitations are presented. To filter possible attacks, we investigate and design firewalls in four components of a vehicle: Dedicated Short-Range Communications (DSRC) module, gateway, Telematic Control Unit (TCU), and Human-Machine Interface (HMI). We also simulate a firewall for an HMI application by building a communication model in Python. / Med den snabba utvecklingen av anslutna fordon har bilsäkerhet blivit ett av de viktigaste ämnena. För att studera hur man skyddar säkerheten för fordonskommunikation analyserar vi potentiella hot mot anslutna fordon och diskuterar motåtgärder för att mildra dessa hot. I denna avhandling undersöker vi 25 tjänster som anslutna fordon kan tillhandahålla. Entiteter, anslutningar och meddelandeflöden i dessa tjänster undersöks och syntetiseras i en fordonsnätverksstruktur. De 25 tjänsterna är indelade i sex användarvägar, inklusive: infotainment service, fjärrövervakning, enhetskontroll, Fordon-tillallt (V2X), diagnostikservice och IDS-system (Intrusion Detection System). Vi etablerar kommunikationsmodeller för dessa användningsfall och analyserar de potentiella hot som baseras på CIA-kriterier (Confidentiality, Integrity and Availability). Vi diskuterar eventuella motåtgärder som kan mildra dessa hot baserat på befintliga nätverkssäkerhetstekniker. Varje alternativ motåtgärds fördelar och begränsningar presenteras. För att filtrera eventuella attacker undersöker vi och utformar brandväggar i fyra delar av ett fordon: Dedicated Short-Range Communications (DSRC) -modul, gateway, Telematic Control Unit (TCU) och Human Machine Interface (HMI). Vi simulerar också en brandvägg för en HMI-applikation genom att bygga en kommunikationsmodell i Python.

<p>Road networks are significantly affected by traffic signal operations, which contribute from 5% to 10% of all traffic delay in the United States. It is therefore important for agencies to systematically monitor signal performance to identify locations where operations do not function as desired and where mobility could be improved.</p> <p><br></p> <p>Currently, most signal performance evaluations are derived from infrastructure-based Automated Traffic Signal Performance Measures (ATSPMs). These performance measures rely on high-resolution detector and phase information that is collected at 10 Hz and reported via TCP/IP connections. Even though ATSPMs have proven to be a valid approach to estimate signal performance, significant initial capital investment required for infrastructure deployment can represent an obstacle for agencies attempting to scale these techniques. Further, fixed vehicle detection zones can create challenges in the accuracy and extent of the calculated performance measures.</p> <p><br></p> <p>High-resolution connected vehicle (CV) trajectory data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, this data set provides unique opportunities to derive accurate signal performance measures without the need for infrastructure upgrades. This dissertation provides a comprehensive suite of CV-based techniques to generate actionable and scalable traffic signal performance measures.</p> <p><br></p> <p>Turning movements of vehicles at intersections are automatically identified from attributes included in the commercial CV data set to facilitate movement-level analyses. Then, a trajectory-based visualization from which relevant performance measures can be extracted is presented. Subsequently, methodologies to identify signal retiming opportunities are discussed. An approach to evaluate closely-coupled intersections, which is particularly challenging with detector-based techniques, is then presented. Finally, a data-driven methodology to enhance the scalability of trajectory-based traffic signal performance estimations by automatically mapping relevant intersection geometry components is provided.</p> <p><br></p> <p>The trajectory data processing procedures provided in this dissertation can aid agencies make data-driven decisions on resource allocation and signal system modifications. The presented techniques are transferable to any location where CV data is available, and the scope of analysis can be easily varied from the movement to intersection, corridor, region, and state level.</p>

Variable Speed Limit (VSL) strategies identify and disseminate dynamic speed limits that are determined to be appropriate based on prevailing traffic conditions, road surface conditions, and weather conditions. This dissertation develops and evaluates a shockwave-based VSL system that uses a heuristic switching logic-based controller with specified thresholds of prevailing traffic flow conditions. The system aims to improve operations and mobility at critical bottlenecks. Before traffic breakdown occurrence, the proposed VSL’s goal is to prevent or postpone breakdown by decreasing the inflow and achieving uniform distribution in speed and flow. After breakdown occurrence, the VSL system aims to dampen traffic congestion by reducing the inflow traffic to the congested area and increasing the bottleneck capacity by deactivating the VSL at the head of the congested area. The shockwave-based VSL system pushes the VSL location upstream as the congested area propagates upstream. In addition to testing the system using infrastructure detector-based data, this dissertation investigates the use of Connected Vehicle trajectory data as input to the shockwave-based VSL system performance. Since the field Connected Vehicle data are not available, as part of this research, Vehicle-to-Infrastructure communication is modeled in the microscopic simulation to obtain individual vehicle trajectories. In this system, wavelet transform is used to analyze aggregated individual vehicles’ speed data to determine the locations of congestion. The currently recommended calibration procedures of simulation models are generally based on the capacity, volume and system-performance values and do not specifically examine traffic breakdown characteristics. However, since the proposed VSL strategies are countermeasures to the impacts of breakdown conditions, considering breakdown characteristics in the calibration procedure is important to have a reliable assessment. Several enhancements were proposed in this study to account for the breakdown characteristics at bottleneck locations in the calibration process. In this dissertation, performance of shockwave-based VSL is compared to VSL systems with different fixed VSL message sign locations utilizing the calibrated microscopic model. The results show that shockwave-based VSL outperforms fixed-location VSL systems, and it can considerably decrease the maximum back of queue and duration of breakdown while increasing the average speed during breakdown.

No description available.

<p>  </p> <p>Most new vehicles manufactured in the last two years are connected vehicles (CV) that transmit back to the original equipment manufacturer at near real-time fidelity. These CVs generate billions of data points on an hourly basis, which can provide valuable data to agencies to improve the overall mobility experience for users. However, with this growing scale of CV big data, stakeholders need efficient and scalable methodologies that allow agencies to draw actionable insights from this large-scale data for daily operational use. This dissertation presents a suite of applications, illustrated through case studies, that use CV data for assessing and managing mobility and safety on surface transportation systems.</p> <p>A systematic review of construction zone CV data and crashes on Indiana’s interstates for the calendar year 2019, found a strong correlation between crashes and hard-braking event data reported by CVs. Trajectory-level CV data analyzed for a construction zone on interstate 70 provided valuable insights into travel time and traffic signal performance impacts on the surrounding road network. An 11-state analysis of electric and hybrid vehicle usage in proximity to public charging stations highlighted regions under and overserved by charging infrastructure, providing quantitative support for infrastructure investment allocations informed by real-world usage trends. CV data were further leveraged to document route choice behavior during active freeway incidents providing stakeholders with a historical record of observed routing patterns to inform future alternate route planning strategies. CV trajectory data analysis facilitated the identification of trip chaining activities resulting in improved outlier curation and realistic estimation of travel time metrics.</p> <p>The overall contribution of this thesis is developing analytical big data procedures to process billions of CV data records to inform engineering and public policy investments in infrastructure capacity, highway safety improvements, and new EV infrastructure. These scalable and efficient analysis techniques proposed in this dissertation will help agencies at the federal, state and local levels in addition to private sector stakeholders in assessing transportation system performance at-scale and enable informed data-driven decision making.</p>

Developments of commercial self-driving vehicle (SDV) technology has a potential for a paradigm shift in traffic control technology. Contrary to some previous research approaches, this research argues that, as any other technology, traffic control technology for SDVs should be developed having in mind improved quality of life through a sustainable developmental approach. Consequently, this research emphasizes upon the social perspective of sustainability, considering its neglect in the conventional control principles, and the importance of behavioral considerations for accurately predicting impacts upon economic or environmental factors. The premise is that traffic control technology can affect the distribution of advantages and disadvantages in a society, and thus it requires a framework of social justice. The framework of social justice is inspired by John Rawls' Theory of Justice as fairness, and tries to protect the inviolability of each user in a system. Consequently, the control objective is the distribution of delay per individual, considering for example that the effect of delay is not the same if a person is traveling to a grocery store as opposed to traveling to a hospital. The notion of social justice is developed as a priority system, with end-user responsibility, where user is able to assign a specific Priority Level for each individual trip with SDV. Selected Priority Level is used to determine the right-of-way for each self-driving vehicle at an intersection. As a supporting mechanism to the priority system, there is a structure of non-monetary Priority Credits. Rules for using Priority Credits are determined using knowledge from social science research and through empirical evaluation using surveys, interviews, and web-based experiment. In the physical space, the intersection control principle is developed as hierarchical self-organization, utilizing communication, sensing, and in-vehicle technological capabilities. This distributed control approach should enable robustness against failure, and scalability for future expansion. The control mechanism has been modeled as an agent-based system, allowing evaluation of effects upon safety and user delay. In conclusion, by reaching across multiple disciplines, this development provides the promise and the challenge for evolving SDV control technology. Future efforts for SDV technology development should continue to rely upon transparent public involvement and understanding of human decision-making. / Ph. D.

Over the last decade, extensive research efforts have been placed on performance evaluation and the benefits of innovative CV applications. Findings indicate that CV technology can effectively mitigate the safety, mobility, and environmental challenges experienced on transportation networks. Most of research evaluated CV technology through simulation studies. However, a field study provides a more ideal method of assessing CV technology effectiveness. Therefore, a field study to obtain the actual effectiveness of CV technology was warranted, to validate previous findings, and to add to the body of knowledge surrounding this topic. This thesis presents both a field study and simulation evaluation of the effectiveness of CV smartphone technology on a 1.1 mile segment of State Road 121, containing five intersections, in Gainesville, Florida. Field observations were conducted using a CV application, developed by Connected Signals, Inc., that uses a smartphone application, called EnLighten, to communicate intersection information to driver’s smartphone, which serves as a vehicle on-board unit. Traffic operation and safety performance was evaluated using start-up lost time, discharge distribution model, and speed harmonization. Findings show that the CV smartphone technology improved intersection performance with a reduction in start-up lost time of approximately 86%. Additionally, driving safety improved with a reduction in speed variability by nearly 61% between vehicles in a specific lane for a 100% CV penetration rate. Cost analyses of deploying CV smartphone technology indicate that implementation may result in an average total economic cost savings associated with crashes of nearly $6.8 million at the study site, and approximately $5.6 billion statewide. Findings of the simulation evaluation revealed that the CV technology improved performance of intersections operating at a Level of Service (LOS) B or better, compared to lower operating levels. Operational performance improved at intersections operating at a LOS C with a 30% to 60% CV penetration rate.