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Traffic Forecasting Applications Using Crowdsourced Traffic Reports and Deep Learning

Intelligent transportation systems (ITS) are essential tools for traffic planning, analysis, and forecasting that can utilize the huge amount of traffic data available nowadays. In this work, we aggregated detailed traffic flow sensor data, Waze reports, OpenStreetMap (OSM) features, and weather data, from California Bay Area for 6 months. Using that data, we studied three novel ITS applications using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first experiment is an analysis of the relation between roadway shapes and accident occurrence, where results show that the speed limit and number of lanes are significant predictors for major accidents on highways. The second experiment presents a novel method for forecasting congestion severity using crowdsourced data only (Waze, OSM, and weather), without the need for traffic sensor data. The third experiment studies the improvement of traffic flow forecasting using accidents, number of lanes, weather, and time-related features, where results show significant performance improvements when the additional features where used.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc1703305
Date05 1900
CreatorsAlammari, Ali
ContributorsBuckles, Bill, Nielsen, Rodney, Fu, Song, Namuduri, Kamesh, Habib, Abdulrahman
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
Formatx, 71 pages, Text
RightsPublic, Alammari, Ali, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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