Wireless-Signal-Based Vehicle Counting and Classification in Different Road Environments

Abstract

Traffic monitoring is key to modern city planning. However, the costs associated with monitoring devices limit the large-scale deployment of existing traffic monitoring systems. In this article, we propose and evaluate an algorithm to automatically count the number of vehicles that have passed through a low-cost system for traffic monitoring. The system uses deviations in the Wi-Fi signals strength to predict the presence of a vehicle on the road and its type (car, bus). The study further systematically compares six analytical techniques for the classification of detected vehicles. The methods were tested with data from three road scenarios in the city of Münster, Germany. Vehicle classification accuracy ranged from 83% up to 100% in our study. We also observed that a higher Wi-Fi frequency (5 GHz) was superior to the 2.4 GHz for improving the overall vehicle detection and the results of the classification algorithms. The results suggest that the Wi-Fi-based techniques proposed in this study are promising for cost-efficient traffic monitoring in cities in a privacy-preserving manner.

Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure

“…[82,83] This approach uses the signal interuption for detect a vehicle presence. [47] Not found publication articles on using this sensor as a solution to this problem at this moment.…”

“…The principle of using radio waves for data exchange between devices enables the localisation of the connected device through the strength of the signal from the Wi-Fi transceiver [45]. Comparing the signal's strength between the Wi-Fi receiver and transmitter also enables detection of vehicle motion [46,47].…”

“…It uses spatial parameters information. [47] Indirectly, installing this system as a vehicle counter in many places in the same road section. [86] The possibility of exact occupancy-free parking slots checking.…”

A multiple directions turning vehicle counting method at intersections based on arbitrary-oriented detection and stack Long Short-Term Memory

“…The video surveillance system based on computer vision methods [1] plays an important role in intelligent transportation system. For the vehicle counting task at intersections, visual based methods are more flexible and do not rely on complex sensors compared to traditional sensors such as wireless signals [2], radars [3], and sensing circuits [4]. With the rapid development of UAV (unmanned aerial vehicle) technology, high resolution aerial-view images have significantly been improved in quantity and quality.…”