Applications of Deep Learning in Intelligent Transportation Systems
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ORIGINAL PAPER
Applications of Deep Learning in Intelligent Transportation Systems Arya Ketabchi Haghighat1 · Varsha Ravichandra‑Mouli1 · Pranamesh Chakraborty1 · Yasaman Esfandiari1 · Saeed Arabi1 · Anuj Sharma1 Received: 19 November 2019 / Revised: 19 November 2019 / Accepted: 17 July 2020 © Springer Nature Singapore Pte Ltd. 2020
Abstract In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company’s performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed. Keywords Deep learning · ITS · Survey · Transportation systems
Introduction The emergence of machine learning and its substitution for several statistical models have led to better problem-solving, which in turn has led various fields of study to turn their research paths to take advantage of this new method. Transportation systems have been influenced by the growth of machine learning, particularly in intelligent transportation systems (ITS).With the proliferation of data and advancements in computational techniques such as graphical processing units (GPUs), a specific class of machine learning known as deep learning (DL) has gained popularity. The capability of DL models to address large amounts of data and extract knowledge from complex systems has made them a powerful and viable solution in the domain of ITS. A variety of networks in DL have helped researchers to formulate their problems in a way that can be solved with one * Arya Ketabchi Haghighat [email protected] 1
Iowa State University, Ames, IA, USA
of these neural network techniques. Traffic signal control for better traffic management, increasing the security of transportation via surveillance sensors, traffic rerouting systems, hea
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