The flow of traffic at an intersection is often controlled by a traffic signal. This research report models a T-intersection with a disjoint network for each direction of traffic flow, eastbound, westbound and southbound. The traffic signal is modeled with a fourth network. Three types of signal control (pretimed, semi-actuated and full-actuated) are modeled to examine the effect of each type on the average delay time and average length of queues for each lane of traffic queue at the intersection. The computer models presented in the report use SLAM computer language to simulate the traffic signal and vehicle flow.

M.Ing. / Traffic has become an extreme irritation and costly entity to deal with in recent years. Gone are the days where one could simply widen roadways to increase flow rates due to space constraints. Traffic costs countries billions of dollars per annum and thus the need to alleviate traffic congestion. Many technologies are currently available that can be used to lower the traffic density at an intersection, one of them being the use of cameras. Not only are digital cameras dropping in price, but the associated cost of maintenance is low. Distance information of a scene can thus be calculated via a visual system and from this information advanced control can be implemented in order to maximise traffic flow through an intersection. A traffic simulator was coded and analysed in order to validate the use of a visual system for increasing the amount of cars passing through the intersection per unit time over the current fixed timing system. Two different algorithms were compared to the current fixed timing scheme using a traffic simulator. The results showed that an improvement can be achieved over the current fixed timing scheme (of up to 19.92%). The use of stereovision as a method of attempting to monitor traffic flow is discussed. Vehicles were tracked using 13 trackers and the distance away from the stereo setup was calculated and compared to the actual distance away from the stereo setup. The best results found that with a baseline distance of 1500mm the average error in determining the distance of a vehicle was 16.46m. Although this error is quite large, it is still possible to monitor traffic flow using stereo vision with these inputs. Some of the issues that may cause these errors are camera quality, camera calibration and variable lighting conditions.

<p>   </p> <p>Historically, roadway performance measures have focused almost exclusively on vehicular movement. In most urban settings, pedestrian movements typically outnumber vehicular movements significantly. However,  historically there has been no way to collect such data at scale in a systematic manner. With the widespread introduction of cameras for monitoring vehicular flow, there is an opportunity to leverage this infrastructure to acquire insights into the patterns and trends of pedestrian activities at signalized intersections in an automated and systematic manner. Such data and performance measures are critical inputs for detailed analysis of pedestrian movements. Overall, addressing this issue is a vital component of transportation agencies that seek to develop equitable treatment of all transportation system users including vulnerable road users. This dissertation addresses the gap in the literature regarding detailed characterization of pedestrian movement patterns and trends. The dissertation leverages data from signalized intersection cameras to (1) quantify the required duration for the pedestrian walk-interval based on pedestrian volume and geometric features of the intersection, (2) carry out time series analysis to acquire insights on pedestrian demand patterns and the influential variables, and (3) build machine learning algorithms to accurately predict pedestrian volumes and tie it to signal timing, to enhance service for all roadway users.</p> <p>The first study provides quantitative guidance for walk time interval selection. This part reports on 1,500 pedestrian movement observations from 12 signalized intersections with varying pedestrian demand, pedestrian storage areas, and pedestrian push-button locations. That data were used to develop a model predicting start-up time with an R2 of 0.89. The study concludes by presenting a quantitative table with four timing categories ranging from negligible volume to high volume and corresponding appropriate durations for the pedestrian walk interval time, based on the demand per cycle, storage area for pedestrians, and offset of the pedestrian push-button from the crosswalk.</p> <p>The second study describes several scalable techniques for measuring and analyzing the movement of pedestrians on a typical university campus. Approximately 35.6 million pedestrian movements over 19 months were tabulated in 15-minute counts of pedestrian volumes by intersection. Counts are used in evaluating pedestrian activity dependency on select explanatory variables at both the network and intersection levels at each time step for the entire analysis period. The study reports on time series correlation and cross-correlation and measures the time-dependency between pedestrian activities and influential factors such as the academic calendar, football games, basketball games, and graduation ceremonies. It provides a comprehensive understanding of the factors that are most influential of  pedestrian volumes at intersections.</p> <p>The third study presents a data-driven approach to predict pedestrian volume per intersection quadrant at 15-minute intervals, and to connect this information to signal timing. Machine learning random forest and XGBoost classification models were trained on a large dataset of pedestrian counts consisting of approximately 2.6 million observations collected through 19 months at 13 exclusive pedestrian service intersections. The predicted pedestrian volumes were then categorized per the pedestrian walk-interval categories to provide optimal signal timing for each intersection quadrant, thus enabling potential dynamic pedestrian signal timing at exclusive service intersections. The results of this study showed that the developed models accurately predict pedestrian volumes per 15-minute intervals for each quadrant of an intersection, with a high degree of precision and a prediction accuracy of 82.3%. Signal timing optimization based on predicted pedestrian volume can significantly improve pedestrian mobility and maximize traffic flow. </p> <p>The findings of this study provide valuable insights for traffic engineers and planners interested in developing and deploying dynamic pedestrian signal timing systems. It is a practical and effective solution for improving mobility for all roadway users at intersections with exclusive pedestrian service.</p> <p>  </p>

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D.Ing. / Conventional traffic models used for the analysis of packet-switched data are Markovian in nature and are based on assumptions, such as Poissonian arrivals. The introduction of packet oriented networks has resulted in an influx of information highlighting numerous discrepancies from these assumptions. Several studies have shown that traffic patterns from diverse packet-switched networks and services exhibit the presence of properties such as self-similarity, long-range dependencies, slowly decaying variances, "heavy tailed" or power law distributions, and fractal structures. Heavy Tailed distributions decay slower than predicted by conventional exponential assumptions and lead to significant underestimation of network traffic variables. Furthermore, it was shown that the statistical multiplexing of multiple packet-switched sources do not give rise to a more homogenous aggregate, but that properties such as burstiness are conserved. The results of the above mentioned studies have shown that none of the commonly used traffic models and assumptions are able to completely capture the bursty behaviour of packet- and cellbased networks. Artificial Intelligent methods provide the capability to extract the inherent characteristics of a system and include soft decision-making approaches such as Fuzzy Logic. Adaptive methods such as Fuzzy Logic Self-learning algorithms have the potential to solve some of the most pressing problems of traffic Modelling and Management in modern packet-switched networks. This dissertation is concerned with providing alternative solutions to the mentioned problems, in the following three sub-sections; the Description of Heavy Tailed Arrival Distributions, Timeseries Forecasting of bursty Traffic Intensities, and Management related Soft Decision-Making. Although several alternative methods, such as Kalman Filters, Bayesian Distributions, Fractal Analysis and Neural Networks are considered, the main emphasis of this work is on Fuzzy Logic applications.