Optimizing Internet of Things-Based Intelligent Transportation System’s Information Acquisition Using Deep Learning

Abstract

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.

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

“…Figure 5 depicts a graphical depiction of the error rate in smart parking versus the number of vehicles, ranging from 4500 to 40500. The results graphically illustrate that the proposed CRFORIELM technique achieves a lower error rate compared to existing methods [1], [2], and [3]. For each method, ten different results were observed with varying numbers of vehicles.…”

“…This part presents comparative study of CRFORIELM method, along with three existing methods, Faster R-CNN [1] SGRU-LSTM [2], DQN-based algorithm [3]. The performance analysis employs metrics such as accuracy, error rate, route detection time, sensitivity and specificity.…”

“…The observed outcomes demonstrate that the CRFORIELM technique outperforms other deep learning methods. The overall accuracy performance using the CRFORIELM method is enhanced by 3%, 1%, and 1% than the [1], [2], [3]. This enhancement achieved through the application of robust outlier incremental extreme learning machines for analyzing smart car parking using Cox regression based on the capacity of parking slot and occupancy level of parking slot.…”

“…Faster R-CNN was designed in [1], to enhance sensitivity as well as accuracy at feature extraction for IoTbased ITS. However, it faced challenges in accurately identifying smart parking availability.…”

EVATL: A novel framework for emergency vehicle communication with adaptive traffic lights for smart cities

“…Systems such as traffic lights, signals, and others govern the direction of traffic. After all, a new traffic-controlling environment will be required as AI develops [14][15][16].…”