Kased, AbanoubRabee, RanaFahmy, AkramMohamed, HussienYacoub, MarcoElhenawy, MohammedAshqar, HuthaifaHassan, Abdallah A.Glaser, SebastienRakotonirainy, Andry2024-10-282024-10-282023-09Kased, Abanoub, Rana Rabee, Akram Fahmy, Hussien Mohamed, Marco Yacoub, Mohammed Elhenawy, Huthaifa I. Ashqar, Abdallah A. Hassan, Sebastien Glaser, and Andry Rakotonirainy. “Intersection Detection Using Vehicle Trajectories Data: Deep Neural Network Application,” in Australasian Road Safety Conference, 2023. https://trid.trb.org/View/2431438.https://doi.org/10.33492/ARSC-2023http://hdl.handle.net/11603/368162023 Australasian Road Safety Conference, 19-21 September, Cairns, Queensland, AustraliaIn 2021, intersection-adjacent crashes were stated to cause 7.7% of total annual road deaths in Australia (BITRE, n.d.). Generating intersection maps is essential for future Cooperative Intelligent Transport Systems (C-ITS) deployment. Nonetheless, crowdsourced vehicle trajectories are a viable and affordable data source that can be used to generate maps. However, intersection maps are changeable, and building one map inference model for all intersection types is challenging. Therefore, we need an object detector that can detect and classify the different intersections using the 2-D scatter plot of the crowdsourced trajectories. Consequently, each subset of trajectories data points passed to the suitable intersection map inference model. This study used two real-world vehicle trajectory datasets, T-Drive and ECML-PKDD 15, to classify the intersections by building an object detection model using Deep Neural Network (DNN). We created 2000 images to train a Single-Shot detector the initial testing results were promising.3 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Text