Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 35). / Traffic simulation is an important aspect of understanding how people move throughout various road systems. It can provide insight into the design of city streets and how well they can handle certain traffic patterns. There are various simulators available, ranging from free tools such as TRANSIMS to commercial implementations such as TransCAD. The available tools provide complex, large scale and very detailed simulation capabilities. The Virtual City Testbed addresses aspects that are not available in these tools. Primarily, the test bed provides the ability for interaction with the traffic system in real time. Instead of basing the simulation solely on automated vehicle models, we allow for human participants to interact with individual cars via a remote simulation client. Thus we are able to inject realistic human input into our simulation. A second feature provided by our simulation is the ability to disrupt a simulation in progress. A disruption usually involves disabling access to a set of streets which forces the traffic to adapt as it moves around the road system. This yields a way to study the way traffic motion changes within a road system under the presence of unexpected events such as natural disasters or other real life disruptions. Ultimately, we provide a test bed for studying traffic under varying environmental conditions. / by Oleg I. Kozhushnyan. / M.Eng.

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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.

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.

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