Performance monitoring systems have experienced remarkable development in the past few decades. In today's world, an important issue for almost every industry is to find a way to appropriately evaluate the performance of the provided service. Having a reliable performance monitoring system is necessary, and researchers have developed assessment models and tools to deal with this concern. There are many approaches to the development of performance measurement and observation systems. The internet-of-things (IoT) creates a broad range of opportunities to monitor the systems by using the information from connected people and devices. The IoT is providing many new sources of data that need to be managed. One of the key issues that arises in any data management system is confidentiality and privacy.Significant progress has been made in development and deployment of performance monitoring systems in the signalized traffic environment. The current monitoring and data collection system relies mostly on infrastructure-based sensors, e.g. loop detectors, video surveillance, cell phone data, vehicle signatures, or radar. High installation and maintenance costs and a high rate of failure are the two major drawbacks of the existing system. Emerging technologies, i.e. connected vehicles (CV), will provide a new, high fidelity approach to be used for better performance monitoring and traffic control.This dissertation investigates the real-time performance observation system in a multi-modal connected vehicle environment. A trajectory awareness component receive and processes the connected vehicle data using the Basic Safety Message (BSM). A geo-fence section makes sure the infrastructure system (for example, roadside unit (RSU)) receives the BSM from only the connected vehicles on the roadway and within the communication range. The processed data can be used as an input to a real-time performance observer component.Three major classes of performance metrics, including mobility, signal, and CV-system measures, are investigated. Multi-modal dashboards that utilize radar diagrams are introduced to visualize large data sets in an easy to understand way. A mechanism to maintain the anonymity of vehicle information to ensure privacy was also developed. The proposed algorithm uses partial vehicle trajectories to estimate travel time average and variability on a link basis. It is shown that the model is not very sensitive to the market penetration rate of connected vehicles. This is a desirable feature especially because of the fact that the market penetration rate of connected vehicles will not be very high in near future. The system architecture for connected vehicle based performance observation applications was developed to be applicable for both a simulation environment and a real world traffic system. Both hardware-in-the-loop (HIL) and software-in-the-loop (SIL) simulation environments are developed and calibrated to mimic the real world. Comprehensive testing and assessment of the proposed models and algorithms are conducted in simulation as well as field test networks. A web application is also developed as part of a central system component to generate reports and visualizations of the data collection experiments.

Autonomous Vehicle are coming. But mass adoption is at least ten years away according to consensus compiled from interviews conducted with industry thought lenders. Questions remain as to what technology those vehicles will contain as there is no universal platform for autonomous vehicle technology, since manufacturers, hardware and software companies are developing their own proprietary products. A/V technology is expected to improve productivity, and provide a plethora of societal benefits, but while we await the closure of the time gap the US will lose almost 40,000 citizens each year with traffic fatalities. Connected vehicle technology, which is currently completing pilot studies, has been shown to reduce automobile accidents. This technology is not as complex as autonomous vehicle technology and is available now. Semi-autonomous vehicles which is Level 1 through Level 3 on the Society of Automobile Executives (SAE) scale is available on American automobiles today and has proven to be very popular amongst consumers. Technology convergence of semi-autonomous vehicle and connected vehicles can bridge the time gap until mass adoption of autonomous vehicle and contribute to reducing annual traffic fatalities. Combining these technologies will give drivers additional safety features thus providing them with the opportunity of making better decisions.

People with vision impairment have various challenges in wayfinding, navigation, and crossing signalized intersections. They often face physical and information barriers that impede their mobility and undermine their safety along a trip. Visually impaired people usually use a white cane as their primary aid when crossing urban traffic intersections. In order to improve their mobility, safety and accessibility, it is important to provide an assistive system to help them in intersection navigation and to provide information regarding the surrounding environment. While assistive systems have been developed to help visually impaired pedestrians to navigate and find their way, using these systems may be inconvenient. Furthermore, none of the currently available systems provide communication between the users and traffic signal controller that can help them request pedestrian crossing signal timing. Emerging connected vehicle technologies can provide a solution to assist visually impaired people and address their challenges. Conflicts between vehicles and vulnerable road users (VRUs) often result in injuries and fatalities. A situational awareness system could be based on wireless communications between vehicles and VRUs for the exchange of situational awareness information. Compared to the radar-based and vision-based systems, the wireless-based system. can improve VRUs’ safety, especially in non-line-of-sight (NLOS) situations. In particular, it can be very helpful when drivers are making a right or left turn where there is a pedestrian in a crosswalk and visibility conditions are poor. The Smart Walk Assistant (SWA) system was designed, developed, and tested during the research of this dissertation. It includes two wireless communication pathways; pedestrian-to-infrastructure (P2I) and pedestrian-to-vehicle (P2V). The first communication pathway enables users to send a pedestrian signal request to the traffic signal controller and receive traffic signal status. The second communication pathway enables pedestrians and vehicles to exchange information, including location, speed, and heading, that can be used to detect possible conflict between pedestrian and vehicles and provide conflict alerts. The SWA system may be especially beneficial to pedestrians with disability (e.g., blind or visually impaired pedestrians) who would benefit from active support to safely cross streets at signalized intersections. Developing a reliable situational awareness system for pedestrians is much more challenging than for vehicles because a vehicle’s movement is more predictable and usually remains in the lane in the road. In order to provide better location-based services for pedestrians, a position accuracy is needed of, at most, the width of a crosswalk or sidewalk. The SWA system includes a method to estimate a pedestrian’s position. The algorithm is based on integrating Map-Matching and an Extended Kalman Filter (EKF) in a connected vehicle environment to provide precise location information. The system architecture for the SWA application was developed to be applicable for both a simulation environment and a real world traffic system. Hardware-in-the-loop (HIL) simulation environment is developed and calibrated to mimic the real world. Comprehensive testing and assessment of the system and algorithms are conducted in simulation as well as field test networks.

The environmental impact of a transportation system is critical in the assessment of the transportation system performance. Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems attempt to minimize vehicle fuel consumption and emission levels by controlling vehicle speed and acceleration levels. The majority of previous research efforts developed and applied Eco-CACC systems on either freeway or signalized intersections independently on simple and small transportation networks without consideration of the interaction among these controls. This thesis extends the state-of-the-art in Eco-CACC evaluation by conducting a comprehensive evaluation on a complex network considering Eco-CACC control on both freeways and arterials individually and simultaneously. The goal of this study is to compare Eco-CACCs on arterial facilities (Eco-CACC-A), freeway facilities (Eco-CACC-F) and both facilities (Eco-CACC-F+A). The effects of Eco-CACC are evaluated considering various Measures of Effectiveness (MOEs), including: average vehicle delay, fuel consumption, and emission levels using simulated results from INTEGRATION, a microscopic traffic assignment and simulation software, considering different freeway speed limits, traffic demand levels and system market penetration rates. In total, 19 traffic scenarios for each of the four different cases (Eco-CACC-A, Eco-CACC-F and Eco-CACC-F+A plus a base no control case) were tested. In total 760 simulation runs were conducted (4 cases * 19 scenarios * 10 repetitions). T-tests and pairwise mean comparison (Tukey HSD) were conducted to identify any statistical differences between control cases and the base case from the simulation results. This thesis shows that arterial and freeway Eco-CACCs can work well together and their effects will be largely influenced by network characteristics. / Master of Science

The dissertation presents a connected vehicle based traffic signal control model (CVTSCM) for signalized arterials. The model addresses different levels of traffic congestion starting with the initial deployment of connected vehicle technologies focusing on two modules created in CVTSCM. For near/under-saturated intersections, an arterial-level traffic progression optimization model (ALTPOM) is being proposed. ALTPOM improves traffic progression by optimizing offsets for an entire signalized arterial simultaneously. To optimize these offsets, splits of coordinated intersections are first adjusted to balance predicted upcoming demands of all approaches at individual intersections. An open source traffic simulator was selected to implement and evaluate the performance of ALTPOM. The case studies’ field signal timing plans were coordinated and optimized using TRANSYT-7F as the benchmark. ALTPOM was implemented with connected vehicles penetration rates at 25% and 50%, ALTPOM significantly outperforms TRANSYT-7F with at least 26.0% reduction of control delay (sec/vehicle) and a 4.4% increase of throughput for both directions of major and minor streets. This technique differs from traditional traffic coordination which prioritizes major street traffic, and thereby generally results in degrading performance on minor streets. ALTPOM also provides smooth traffic progression for the coordinated direction with little impact on the opposite direction. The performance of ALTPOM improves as the penetration rate of connected vehicles increases. For saturated/oversaturated conditions, two queue length management based Active Traffic Management (ATM) strategies are proposed, analytically investigated, and experimentally validated. The first strategy distributes as much green time as possible for approaches with higher saturation discharge rate in order to reduce delay. For the second approach, green times are allocated to balance queue lengths of major and minor streets preventing queue spillback or gridlock. Both strategies were formulated initially using uniform arrival and departure, and then validated using field vehicle trajectory data. After validation of the modules, the effectiveness of CVTSCM is proven. Then, conclusions and recommendations for future researches are presented at the end.

The methods for efficient safety message dissemination in VANETs were proposed. First, the method for using multi-channel was proposed. Using the proposed multi-channel method (divide-and-deliver algorithm), the safety message was delivered to the target device with less delay compared to the traditional single-channel method. This method showed resilient performance even in poor wireless channels compared to the single-channel method. Second, to improve low reliability in low vehicle density situations, the enhanced divide-and-deliver algorithm was proposed. The network coding was a key technique to the enhancement. For the efficient use of network coding, rigorous analysis was conducted and an algorithm was proposed to change the number of network coding packets adaptively by the vehicle densities. Finally, the method for delivering safety messages to multi-direction was proposed. This multi-vehicle selection broadcast (MSB) algorithm avoided the collision between multiple rebroadcasts among vehicles and removed unnecessary packets by using backoff slots. The contributions of this research include reducing delay and increasing reliability for the dissemination of safety messages.

Signal control systems have experienced tremendous development both in hardware and in control strategies in the past 50 years since the advent of the first electronic traffic signal control device. The state-of-art real-time signal control strategies rely heavily on infrastructure-based sensors, including in-pavement or video based loop detectors for data collection. With the emergence of connected vehicle technology, mobility applications utilizing vehicle to infrastructure (V2I) communications enable the intersection to acquire a much more complete picture of the nearby vehicle states. Based on this new source of data, traffic controllers should be able to make "smarter" decisions. This dissertation investigates the traffic signal control strategies in a connected vehicle environment considering mobility as well as safety. A system architecture for connected vehicle based signal control applications under both a simulation environment and in the real world has been developed. The proposed architecture can be applied to applications such as adaptive signal control, signal priority including transit signal priority (TSP), freight signal priority (FSP), emergency vehicle preemption, and integration of adaptive signal control and signal priority. Within the framework, the trajectory awareness of connected vehicles component processes and stores the connected vehicle data from Basic Safety Message (BSM). A lane level intersection map that represents the geometric structure was developed. Combined with the map and vehicle information from BSMs, the connected vehicles can be located on the map. Some important questions specific to connected vehicle are addressed in this component. A geo-fencing area makes sure the roadside equipment (RSE) receives the BSM from only vehicles on the roadway and within the Dedicated Short-range Communications (DSRC) range. A mechanism to maintain anonymity of vehicle trajectories to ensure privacy is also developed. Vehicle data from the trajectory awareness of connected vehicles component can be used as the input to a real-time phase allocation algorithm that considers the mobility aspect of the intersection operations. The phase allocation algorithm applies a two-level optimization scheme based on the dual ring controller in which phase sequence and duration are optimized simultaneously. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicles based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Dangerous driving behaviors may occur if a vehicle is trapped in the dilemma zone which represents one safety aspect of signalized intersection operation. An analytical model for estimating the number of vehicles in dilemma zone (NVDZ) is developed on the basis of signal timing, arterial geometry, traffic demand, and driving characteristics. The analytical model of NVDZ calculation is integrated into the signal optimization to perform dilemma zone protection. Delay and NVDZ are formulated as a multi-objective optimization problem addressing efficiency and safety together. Examples show that delay and NVDZ are competing objectives and cannot be optimized simultaneously. An economic model is applied to find the minimum combined cost of the two objectives using a monetized objective function. In the connected vehicle environment, the NVDZ can be calculated from connected vehicle data and dilemma zone protection is integrated into the phase allocation algorithm. Due to the complex nature of traffic control systems, it is desirable to utilize traffic simulation in order to test and evaluate the effectiveness and safety of new models before implementing them in the field. Therefore, developing such a simulation platform is very important. This dissertation proposes a simulation environment that can be applied to different connected vehicle related signal control applications in VISSIM. Both hardware-in-the-loop (HIL) and software-in-the-loop (SIL) simulation are used. The simulation environment tries to mimic the real world complexity and follows the Society of Automotive Engineers (SAE) J2735 standard DSRC messaging so that models and algorithms tested in the simulation can be directly applied in the field with minimal modification. Comprehensive testing and evaluation of the proposed models are conducted in two simulation networks and one field intersection. Traffic signal priority is an operational strategy to apply special signal timings to reduce the delay of certain types of vehicles. The common way of serving signal priority is based on the "first come first serve" rule which may not be optimal in terms of total priority delay. A priority system that can serve multiple requests with different priority levels should perform better than the current method. Traditionally, coordination is treated in a different framework from signal priority. However, the objectives of coordination and signal priority are similar. In this dissertation, adaptive signal control, signal priority and coordination are integrated into a unified framework. The signal priority algorithm generates a feasible set of optimal signal schedules that minimize the priority delay. The phase allocation algorithm considers the set as additional constraints and tries to minimize the total regular vehicle delay within the set. Different test scenarios including coordination request, priority vehicle request and combination of coordination and priority requests are developed and tested.

Context. Connected vehicles use sensors such as cameras or radars to collect data about surrounding environments automatically and share these data with each other or with road side infrastructure using short-range wireless communication. Due to the large amount of information generated, strategies are required to minimize information redundancy when important information is propagated among connected vehicles. Objectives. This research aims to develop an information propagation strategy in connected vehicle systems using software agent-based coordination strategies to reduce unnecessary message broadcast and message propagation delay. Methods. A review of related work is used to acquire a deep insight as well as knowledge of the state-of-the-art and the state-of-practice from relevant studies in the subject area. Based on the review of related work, we propose an agent-based coordination strategy for information propagation in connected vehicle systems, in which connected vehicles coordinate their message broadcast activities using auctions. After that, a simulation experiment is conducted to evaluate the proposed strategy by comparing it with existing representative strategies. Results. Results of simulation experiments and statistical tests show that the proposed agent-based coordination strategy manifest some improvements in reducing unnecessary message broadcast and message propagation delay compared to other strategies involved in the simulation experiments. Conclusions. In this research, we suggest a new strategy to manage the propagation of information in connected vehicle systems. According to the small scale simulation analysis, the use of auctions to select message transmitters enables our proposed strategy to achieve some improvements in reducing unnecessary message broadcast and propagation delay than existing strategies. Thus, with the help of our proposed strategy, unnecessary message broadcast can be minimized and the communication resources of connected vehicle systems can be utilized effectively. Also, important safety messages can be propagated to drivers faster, negative traffic events could be averted. / 0707708513

Traffic signal control systems have been tremendously improved since the first colored traffic signal light was installed in London in December 1868. There are many different types of traffic signal control systems that can be categorized into three major control types: fixed-time, actuated, and adaptive. Choosing a proper traffic signal system is very important since there exists no perfect signal control strategy that fits every traffic network. One example is traffic signal coordination, which is the most widely used traffic signal control system. It is believed that performance measures, such as travel times, vehicle delay, and number of stops, can be enhanced by synchronizing traffic signals over a corridor. However, it is not always true that the coordination will have the same benefits for all the traffic in the network. Most of the research on coordination has focused only on strengthening the major movement along the coordinated routes without considering system-wide impacts on other traffic. Therefore, before implementing a signal control system to a specific traffic network, a thorough investigation should be conducted to see how the control strategy may impact the entire network in terms of the objectives of each type of traffic control system. This dissertation first considers two different kinds of systematic performance analyses for traffic signal control systems. Then, it presents two types of signal control strategies that account for current issues in coordination and priority control systems, respectively. First, quantitative analysis of smooth progression for traffic flow is investigated using connected vehicle technology. Many studies have been conducted to measure the quality of progression, but none has directly considered smooth progression as the significant factor of coordination, despite the fact that the definition of coordination states that the goal is to have smooth traffic flow. None of the existing studies concentrated on measuring a continuous smooth driving pattern for each vehicle in terms of speed. In order to quantify the smoothness, this dissertation conducts an analysis of the speed variation of vehicles traveling along a corridor. A new measure is introduced and evaluated for different kinds of traffic control systems. The measure can be used to evaluate how smoothly vehicles flow along a corridor based on the frequency content of vehicle speed. To better understand the impact of vehicle mode, a multi-modal analysis is conducted using the new measure. Second, a multi-modal system-wide evaluation of traffic signal systems is conducted. This analysis is performed for traffic signal coordination, which is compared with fully actuated control in terms of a systematic assessment. Many optimization models for coordination focus mainly on the objective of the coordinated route and do not account for the impacts on side street movements or other system-wide impacts. In addition, multi-modality is not considered in most optimized coordination plans. Thus, a systematic investigation of traffic signal coordination is conducted to analyze the benefits and impacts on the entire system. The vehicle time spent in the system is measured as the basis of the analysis. The first analysis evaluates the effect of coordination on each route based on a single vehicle mode (regular passenger vehicles). The second analysis reveals that how multi-modality affects the performance of the entire system. Third, in order to address traffic demand fluctuation and traffic pattern changes during coordination periods, this dissertation presents an adaptive optimization algorithm that integrates coordination with adaptive signal control using data from connected vehicles. Through the algorithm, the coordination plan can be updated to accommodate the traffic demand variation and remain optimal over the coordination period. The optimization framework consists of two levels: intersection and corridor. The intersection level handles phase allocation in real time based on connected vehicle trajectory data, while the corridor level deals with the offsets optimization. The corridor level optimization focuses on the performance of the vehicle movement along the coordinated phase, while at the intersection level, all movements are considered to create the optimal signal plan. The two levels of optimizations apply different objective functions and modeling methodologies. The objective function at the intersection level is to minimize individual vehicle delay for both coordinated and non-coordinated phases using dynamic programming (DP). At the corridor level, a mixed integer linear programming (MILP) is formulated to minimize platoon delay for the coordinated phase. Lastly, a peer priority control strategy, which is a methodology that enhances the multi modal intelligent traffic signal system (MMITSS) priority control model, is presented based on peer-to-peer (P2P) and dedicated short range communication (DSRC) in a connected vehicle environment. The peer priority control strategy makes it possible for a signal controller to have a flexible long-term plan for prioritized vehicles. They can benefit from the long-term plan within a secured flexible region and it can prevent the near-term priority actions from having a negative impact on other traffic by providing more flexibility for phase actuation. The strategy can be applied to all different modes of vehicles such as transit, freight, and emergency vehicles. Consideration for far side bus stops is included for transit vehicles. The research that is presented in this dissertation is constructed based on Standard DSRC messages from connected vehicles such as Basic Safety Messages (BSMs), Signal Phasing and Timing Messages (SPaTs), Signal Request Messages (SRMs), and MAP Messages, defined by Society of Automotive Engineers (SAE) (SAE International 2016).

The Secure, Autonomic, FAult-Resiliant and Intelligent Taxi Hailing System (SAFARI-Taxi), currently undergoing prototyping, will broker rides between taxi drivers and spontaneous taxi users, or hailers. SAFARI-Taxi will leverage anticipated growth in connected vehicle infrastructure (V2X) as enabled by dedicated short range communications (DSRC) technology to replace line-of-sight street hailing with automated dispatch, via public kiosks and smart phone apps Hailing will be managed with a novel protocol, based on Hailing Request, Service Offer, Hailer Response, and Ride Cancellation messages. Threats to its operation will be mitigated using distributed dispatch; provisions for assuring correctness, timeliness, and appropriate content; and account lockouts, "hailing deposits", and ticketing. Preliminary results indicate that the system will reduce the time to match hailers with taxis. The project's goals align with the U.S. Dept. of Transportation's vision for dynamic mobility applications, including Integrated Dynamic Transit Operation, which specifically targets integration of taxis with public transportation through a citywide connected infrastructure.