Abstract
Vehicular traffic is an important planning concern for commuters and businesses. However, there is no application available which can estimate traffic congestion and flow in the future up to five days. The problem is to develop an application that can predict vehicular traffic density and flow rate based on weather data, calendar data and special events data. This information would be valuable for commuters planning short or long- distance trips, and for transportation and infrastructure departments for better planning the maintenance of roads. The proposed research will combine image processing and machine learning methods.
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Patel, M. (2020). Vehicle Traffic Estimation Using Weather and Calendar Data. In: Goutte, C., Zhu, X. (eds) Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science(), vol 12109. Springer, Cham. https://doi.org/10.1007/978-3-030-47358-7_57
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DOI: https://doi.org/10.1007/978-3-030-47358-7_57
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