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.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1703305 |
| Date | 05 1900 |
| Creators | Alammari, Ali |
| Contributors | Buckles, Bill, Nielsen, Rodney, Fu, Song, Namuduri, Kamesh, Habib, Abdulrahman |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | x, 71 pages, Text |
| Rights | Public, Alammari, Ali, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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