This work first discusses the Intelligent Transportation System (ITS)-oriented dynamic and static Information Acquisition Models (IAMs) and explains the information collection mechanism of the Internet of Things (IoT)-based ITS. The goal is to improve travel conditions and contribute to a better urban environment. In order to do so, the Faster Region-based Convolutional Neural Network (Faster R-CNN) is introduced to extract the IoT-based ITS's electronic data features. It is observed that the Faster R-CNN has excellent recall and accuracy in extracting the features from the ITS electronic data sets. Specifically, the Faster R-CNN's average recall and accuracy reach 83.89% and 86.79%. The accuracy is 6.20% higher than the R-CNN method. Thus, the Faster R-CNN algorithm features more robust and reliable performance for collecting and analyzing ITS data. Overall, this work examines ITS-oriented electronic information collection and automatic detection against the technological background of applying Computer Vision, Deep Learning, and IoT in urban traffic management. In particular, it explains the IoT-based ITS's electronic information collection mechanism under Deep Learning (Faster R-CNN). The finding offers a theoretical foundation for implementing Deep Learning technologies in collecting ITS-oriented big data and smart city construction.INDEX TERMS Intelligent transportation system (ITS), information acquisition model, Internet of Things (IoT), deep learning, faster R-CNN.

At present, the transportation industry is developing rapidly. Studying intelligent highway systems in the Internet of Things (IoT) context is of practical significance. This paper aims to promote the comprehensive integration of modern information technology with transportation facility management and operation management. Firstly, the fundamental nature and laws of IoT and intelligent transportation are studied. Based on the current situation, the feasibility of developing intelligent transportation based on the IoT technology of the highway is analyzed. Secondly, the design requirements of the highway Intelligent Transportation System (ITS) are analyzed. Then, the overall architecture of the highway ITS is designed by comprehensively using various advanced information, communication, and control technologies guided by demand analysis and relying on the technology platform of the IoT. It is identified as three layers: a perceptual layer, a transport layer, and an application layer. Finally, the main application function design of the highway ITS is carried out. The results show that: 1) the efficiency and effectiveness of the maintenance management system in dealing with emergencies before equipment failure is much higher than that of the traditional highway management system; 2) after using the updated system, user experience satisfaction and executive experience satisfaction have improved. It is more ideal than traditional highway traffic processing methods; and 3) expectation of the public for the innovative system designed here is 4.8. It reveals that the proposed intelligent transportation system meets the expectation of the majority of the public for highway traffic management. The purpose of this paper is to promote the development of transportation informatization and intelligence, improve the efficiency of the transportation system, and enhance the safety of transportation through IoT information technology.