Master of Science in Civil Engineering (MSCE)
Civil and Environmental Engineering
In recent times, the term ‘congestion’ has gained a lot of attention due to its negative impact on the mobility and efficiency of transportation network. Congestion control and mitigation of its effects has become a prime concern to many transportation agencies. With breakthroughs in transportation technologies, effective management and utilization of existing infrastructure capacities has been possible. One of the key functions of technological advancements is the ability to understand the different characteristics of traffic that prevail on major freeways and arterials, and to model short-term predictions of traffic conditions with reasonable accuracy. Providing accurate, real-time information makes travelers aware of the traffic conditions on the network and influences travelers’ decisions in terms of trip time, mode and route choice. This helps spread the traffic demand and reduces congestion. Over the past few years, transportation researchers presented different approaches to model traffic conditions. However, no significant effort was made to study the stochastic characteristics of freeway traffic—particularly during breakdown and recovery periods—and to develop models which can forecast variations in traffic conditions. Extant models do not consider the future most probable values. The main objective of this research is to capture and analyze traffic patterns, obtained from real world freeway data, and to develop a series of models that can correlate between current and future traffic states. Traffic conditions evolving over varying time horizons have been successfully modeled and studied. The research ultimately aims to improve our understanding of the characteristics of breakdown and recovery conditions of traffic. The research was conducted using massive freeway data collected from a 40-mile segment of Interstate - 4, in Orlando, Florida.
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Mamidala, Chaitanya, "Exploring Stochastic Characteristics of Freeway Traffic Breakdown and Recovery Conditions" (2008). LSU Master's Theses. 3085.