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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.

Air traffic demand is growing. New methods of airspace design are required that can enable new designs, do not depend on current operations, and can also support quantifiable performance goals. The main goal of this thesis is to develop methods to model inherent safety and control cost so that these can be included as principal objectives of airspace design, in support of prior work which examines capacity. The first contribution of the thesis is to demonstrate two applications of airspace analysis and design: assessing the inherent safety and control cost of the airspace. Two results are shown, a model which estimates control cost depending on autonomy allocation and traffic volume, and the characterization of inherent safety conditions which prevent unsafe trajectories. The effects of autonomy ratio and traffic volume on control cost emerge from a Monte Carlo simulation of air traffic in an airspace sector. A maximum likelihood estimation identifies the Poisson process to be the best stochastic model for control cost. Recommendations are made to support control-cost-centered airspace design. A novel method to reliably generate collision avoidance advisories, in piloted simulations, by the widely-used Traffic Alert and Collision Avoidance System (TCAS) is used to construct unsafe trajectory clusters. Results show that the inherent safety of routes can be characterized, determined, and predicted by relatively simple convex polyhedra (albeit multi-dimensional and involving spatial and kinematic information). Results also provide direct trade-off relations between spatial and kinematic constraints on route geometries that preserve safety. Accounting for these clusters thus supports safety-centered airspace design. The second contribution of the thesis is a general methodology that generalizes unifying principles from these two demonstrations. The proposed methodology has three steps: aggregate data, synthesize lean model, and guide design. The use of lean models is a result of a natural flowdown from the airspace view to the requirements. The scope of the lean model is situated at a level of granularity that identifies the macroscopic effects of operational changes on the strategic level. The lean model technique maps low-level changes to high-level properties and provides predictive results. The use of lean models allows the mapping of design variables (route geometry, autonomy allocation) to design evaluation metrics (inherent safety, control cost).

Drivers are expected to traverse through an intersection in malfunctioning flash mode in the same manner as a stop-sign controlled intersection. Red/red flash corresponds to four-way stop control and yellow/red flash corresponds to two-way stop control. However, at a red flashing signal there is no assurance that a driver can see the cross street indication (i.e., yellow or red flash) and therefore does not know if the intersection is operating as a two-way or four-way flash. In addition, some drivers appear unclear on the rules at a flashing signal. This confusion makes the intersection more accident prone. This study builds upon several previous studies, using data extracted from existing files. The objective of this study is to determine the level of drivers' understanding of the flash control through an analysis of violation rates and types at recorded intersection in malfunction flash. Comparing these violation rates to those at comparable stop-control intersections will help illustrate the difference in drivers' understanding of these similar intersection control devices.

"In many cities around the world traffic congestion has been increasing faster than can be dealt with by new road construction. To resolve this problem traffic management devices and technology such as ramp meters are increasingly being utilized."--leaf 1. / Masters of Information Technology

Road incidents and incident induced traffic congestions are a big threat to the mobility and safety of our daily life. Timely and accurate incident detection using automated incident detection (AID) systems is essential to effectively tackle incident induced congestion problems and to improve traffic management. The core of an AID system is an incident detection algorithm that interprets real time traffic data and makes decision on incidents. / Literature review of existing AID algorithms and their applications reveals that 1) there is no single freeway algorithm that can fulfil the universality aspect of incident detection which is required by the advanced traffic management systems, and 2) how to achieve the effective and stable arterial road incident detection remains a big issue of AID research. In addition, there exists a strong need for incorporating existing expert traffic knowledge into AID algorithms to enhance incident detection performance. / Thesis (PhDTransportSystemsEngineering)--University of South Australia, 2005.

"In many cities around the world traffic congestion has been increasing faster than can be dealt with by new road construction. To resolve this problem traffic management devices and technology such as ramp meters are increasingly being utilized."--leaf 1. / Masters of Information Technology

In this research, a new approach for arterial road travel time estimation and prediction has been proposed and developed for providing reliable dynamic travel time information for arterial road networks. The results of the research should benefit arterial road traffic management and some travel time related applications. / Thesis (PhD)--University of South Australia, 2008

In this research, a new approach for arterial road travel time estimation and prediction has been proposed and developed for providing reliable dynamic travel time information for arterial road networks. The results of the research should benefit arterial road traffic management and some travel time related applications. / Thesis (PhD)--University of South Australia, 2008

Thesis (M. Eng. Sc.)--University of Melbourne, 1994. / Typescript (photocopy). Includes bibliographical references (leaves 123-126).