Applications of Deep Learning in Intelligent Transportation Systems

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.

How Prediction Accuracy Can Affect the Decision-Making Process in Pavement Management System

“…Probabilistic models predict the future condition of pavements by giving a transition matrix with which the pavement would fall into a particular condition state, describing the possible pavement conditions of the random process. Neural Network (NN) models have received more attention in the past few years between researchers because of their capability to interconnect neurons between layers [23][24][25][26]. NN applications can solve complex problems in a more efficient way than traditional methods [26][27][28][29].…”

“…Neural Network (NN) models have received more attention in the past few years between researchers because of their capability to interconnect neurons between layers [23][24][25][26]. NN applications can solve complex problems in a more efficient way than traditional methods [26][27][28][29]. These problems can be in different categories of pavement engineering, based on research conducted by Ceylan in 2014 [30].…”

Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

“…ITS management has become more efficient due to the application of deep learning and machine learning techniques that perfectly complement other analytical and statistical techniques. This, in turn, has facilitated traffic management and traffic planning, enhanced safety and security in transit roads, reduced maintenance costs, and optimized public transportation as well as ride-sharing performance [30]. Thus, for example, Fang et al [31] proposed a support vector machine to classify user transportation and vehicular modes after considering different machine learning methods.…”

Applications of Deep Learning Techniques for Pedestrian Detection in Smart Environments: A Comprehensive Study

“…Haghighat et al [17] investigated the application of deep learning models in intelligent transportation systems. In the following, the advantages and disadvantages of embedded systems were discussed, and finally, the use of deep learning techniques to predict the occurrence of traffic on different road routes was examined.…”