This thesis describes a simplified method for estimating distances directly from terrestrial photographs. It was felt that any method devised must overcome present limitations and meet three basic criteria to be practical. These criteria are: 1) that the method require no subject-visible markings, 2) that it require no special equipment or training to use, and 3) that it provides sufficient accuracy to be useful. A number of possible methods were considered, and were evaluated against these criteria. the Accuacy ( reliablity) of the methods was tested using a variety of statistical tests. The recommended method consists of first selecting a reference distance whose length is known. This reference should be in a plane parallel to and approximately the same distance from the camera as the desired distance. It was found that a vehicle dimension such as the tire track gives consistently the best results. Both the reference and the desired distances are then scaled on the photograph and a ratio is applied to drectly estimate the desired distance. Greatest reliability is achieved when the subject is directly in fromt of, or behind the camera and at a distance between 50 and 250 feet. By following thers guidelines the stated objectives can be met by using this method.

Recent years have shown a growing concern over increasing traffic volume worldwide. The insufficient road capacity and the resulting congestions have become major problems in many urban areas. Congestions negatively impact the economy, the environment and the health of the population as well as the drivers satisfaction. Current solutions to this topical and timely problem rely on the exploitation of Intelligent Transportation Systems (ITS) technologies. ITS urban traffic management involves the collection and processing of a large amount of geographically distributed information to control distributed infrastructure and individual vehicles. The distributed nature of the problem prompted the development of a novel, scalable ITS-Cloud platform. The ITS-Cloud organises the processing and manages distributed data sources to provide traffic management methods with more accurate information about the state of the traffic. A new approach to service allocation, derived from the existing cloud and grid computing approaches, was created to address the unique needs of ITS traffic management. The ITS-Cloud hosts the collection of software services that form the Cloud based Traffic Management System (CTMS). CTMS combines intersection control algorithms with intersection approach advices to the vehicles and dynamic routing. The CTMS contains a novel Two-Step traffic management method that relies on the ITS-Cloud to deliver a detailed traffic simulation image and integrates an adaptive intersection control algorithm with a microscopic prediction mechanism. It is the first method able to perform simultaneous adaptive intersection control and intersection approach optimization. The Two-Step method builds on a novel pressure based adaptive intersection control algorithm as well as two new traffic prediction schemes. The developed traffic management system was evaluated using a new microscopic traffic simulation tool tightly integrated with the ITS-Cloud. The novel traffic management approaches were shown to outperform benchmark methods for a realistic range of traffic conditions and road network configurations. Unique to the work was the investigation of interactions between ITS components.

Wider bike facilities intuitively accommodate a greater number of cyclists in the same amount of time, but specific queue discharge characteristics associated with varying widths and/or types of bike facilities have not been thoroughly documented. The focus of this research analyzed queues of cyclists at four signalized intersections in Portland, OR with varying widths on the approach and downstream intersection legs. A total of 2,820 cyclists within 630 groups of queued cyclists were observed at five different intersection layouts in Portland, Oregon. The layouts consisted of: a standard bike lane six feet wide connecting bicyclists to a standard bike lane six feet wide, a standard bike lane five feet wide connecting bicyclists to two standard bike lanes each five feet wide, a buffered bike lane 12 feet wide connecting bicyclists to a standard bike lane 6.5 feet wide, a bike box 21 feet wide connecting bicyclists to a buffered bike lane 10 feet wide, and a bike box 15 feet wide connecting bicyclists to two standard bike lanes each five feet wide. For each configuration, the following aspects were analyzed: average headway per cyclist within each queue, the time required for queues to enter the intersection, the time required for queues to clear the intersection, the number of cyclists within queues, the width of the bicycle facilities, the approach grade, and the utilization of a bike box at the intersection approach if it was present. The first major focus of the analysis reviewed the average headway values associated with each observed queue of cyclists. The queue size with the lowest mean of the average headway was for groups of seven cyclists with an average headway of approximately 0.8 seconds per cyclist. For queues larger than seven in size, the mean of the average headway remained relatively stable until queues of 12 in size and started to slightly increase toward approximately 1.0 seconds for queues larger than 12 cyclists. In addition, it appears that utilization of a bike box has a potential relationship with a reduced average headway as compared to queues that do not utilize a bike box. The associated reduction in the mean of the average headway was approximately 0.2 to 0.3 seconds per cyclist for queues of three or more in size. The second major focus of the analysis reviewed the queue discharge rate associated with each observed queue of cyclists. The results appear to potentially indicate that wider bike facilities approaching an intersection, wider receiving bike facilities, or utilization of a bike box generally discharge queues of bicyclists into the intersection over a shorter amount of time as compared to facilities that are narrower or underutilized. The installation of a bike box at one of the study intersections increased the approach width from five to 15 feet and resulted in consistently lower average discharge times for all queue sizes, a reduction of greater than one second for queues of two cyclists to as much as about four seconds for queues of nine cyclists. The third major focus of the analysis reviewed the intersection clearance time associated with each observed queue of cyclists. The results appear to potentially indicate that wider bike facilities approaching an intersection, wider receiving bike facilities, or utilization of a bike box generally clear queues of bicyclists through the intersection over a shorter amount of time as compared to facilities that are narrower or underutilized.

In the United States, commercial and residential buildings are responsible for 40% of total energy consumption, which provides an important opportunity for energy impact. As we spend the majority of our active moments during the day in transportation, commercial buildings, streets, and infrastructure, some of the greatest opportunities to reduce energy usage occur when we are outside of the home. A large percentage of energy consumption in the built environment directly or indirectly services humans; thus, there is a significant amount of untapped energy savings that can be achieved by involving humans in the optimization process. By including occupants in the building co-optimization process, we can gain a better understanding of individual energy responsibility and significantly improve energy consumption, thermal comfort and air quality over non human-in-the-loop systems and strategies. First, we present ePrints, a scalable energy footprinting system capable of providing personalized energy footprints in real-time. ePrints supports different apportionment policies, with microsecond-level footprint computation time and graceful scaling with the size of the building, frequency of energy updates, and rate of occupant location changes. Finally, we present applications enabled by our system, such as mobile and wearable applications to provide users timely feedback on the energy impacts of their actions, as well as applications to provide energy saving suggestions and inform building-level policies. Next, we extend the idea of energy footprinting to the city-scale with CityEnergy a city-scale energy footprinting system that utilizes the city's digital twin to provide real-time energy footprints with a focus on 100% coverage. CityEnergy takes advantage of existing sensing infrastructure and data sources in urban cities to provide energy and population estimates at the building level, even in built environments that do not have existing or accessible energy or population data. CityEnergy takes advantage of LFTSys, a low frame-rate vehicle tracking and traffic flow system that we implement on New York City's traffic camera network, to aid in building population estimates. Evaluations comparing CityEnergy with building level energy footprints and city-wide data demonstrate the potential for CityEnergy to provide personal energy footprint estimates at the city-scale. We then tackle the challenge of involving humans in the building energy optimization process by developing recEnergy, a recommender system for reducing energy consumption in commercial buildings with human-in-the-loop. recEnergy learns actions with high energy saving potential through deep reinforcement learning, actively distribute recommendations to occupants in a commercial building, and utilize feedback from the occupants to better learn four different types of energy saving recommendations. Over a four week user study, recEnergy improves building energy reduction from a baseline saving (passive-only strategy) of 19% to 26%. Finally, we extend the recommender system to co-optimize over energy consumption, occupant thermal comfort, and air quality. The recommender system utilizes a multi-task deep reinforcement learning architecture, and is trained using a simulation environment. The simulation environment is built using different models trained on data captured from a digital twin of a real deployment. To measure occupant thermal comfort, the digital twin utilizes a real-time comfort estimation system that extracts and integrates facial temperature features with environmental sensing to provide personalized comfort estimates. We studied three different use cases in this deployment by varying the objective weights in the recommender system, and found that the system has the potential to further reduce energy consumption by 8% in energy focused optimization, improve all objectives by 5-10% in joint optimization, and improve thermal comfort by up to 21% in comfort and air quality focused optimization by incorporating move recommendations.

This thesis descries the evolution and testing of a fully portable, inductive loop vehicle counter system. As a component of the NFS Embedded Distributed Simulation for Transportation System Management project, the system's cellular modem transmits real-time data to servers at Georgia Institute of Technology. From there, the data can be fed into simulations predicting travel behavior. Researchers revised both the detector circuit, and the temporary, reusable loop pad several times over multiple rounds of field testing. The final tested version of this system demonstrates the efficacy of uncommonly small inductive loops. When paired with a reliable data transmission channel, the system was shown to capture nearly 96% of actual through traffic.