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.

Connected temporary traffic control devices (cTTCDs) that provide their location and status are a new tool that infrastructure owners and operators can begin to use to improve the accuracy of work zone data. By improving work zone data, better information can be provided to the public. Publishing these data through the WZDx (Work Zone Data Exchange) aims to improve safety by notifying drivers and vehicles of the location of verified work zones. Connected devices such as smart arrow boards and connected cones have continued to increase in number in the market, but little has been done to determine the best method of integrating these devices into a department of transportation’s (DOT’s) system. An approach is presented that integrates deployed smart arrow boards to indicate actual conditions as part of a planned work zone by leveraging a DOT’s linear referencing system. This method does not require any additional effort from field staff and improves the locational and temporal accuracy of work zone information as part of a WZDx. When fully deployed, this system showed that smart arrow boards could be automatically associated with a work zone in controlled test scenarios as well as in a limited sample under real-world conditions. In real-world conditions, contractors did not need to provide additional information to associate the smart arrow board with the 511 work zone event. This effort represents a starting point for how cTTCDs could be integrated into DOT systems to improve work zone data accuracy.