With the rapid growth of urban populations and increasing vehicular traffic, congestion has become a major challenge for transportation systems worldwide. Accurate estimation of travel time plays a crucial role in mitigating congestion and enhancing traffic management. This research focuses on developing a novel methodology that utilizes machine learning models to estimate travel time using real-time traffic data collected through Bluetooth sensors deployed at traffic intersections. The research compares five different prediction systems for replicating travel time estimation, evaluating their performance and accuracy. The results highlight the effectiveness of the machine learning models in accurately predicting travel time. Lastly, the research explores the creation of a model specifically designed to predict the travel time during peak hours, considering the impact of traffic lights on travel time between intersections. The findings of this study contribute to the development of efficient and reliable travel time prediction systems, enabling commuters to make informed decisions and improving traffic management strategies.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6874 |
| Date | 08 August 2023 |
| Creators | Nampalli, Vignaan Vardhan |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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