Radio-based Traffic Flow Detection and Vehicle Classification for Future Smart Cities

TL;DR

This paper presents a radio-based method utilizing signal attenuation and machine learning to accurately detect traffic flow and classify vehicles, aiming for low-cost, weather-independent smart city transportation solutions.

Contribution

It introduces a novel radio-based approach combining signal attenuation data with machine learning for vehicle classification and traffic detection in smart cities.

Findings

Achieved approximately 99% classification accuracy.

Demonstrated low-cost, weather-independent operation.

Validated effectiveness through comprehensive field measurements.

Abstract

Intelligent Transportation Systems (ITSs) providing vehicle-related statistical data are one of the key components for future smart cities. In this context, knowledge about the current traffic flow is used for travel time reduction and proactive jam avoidance by intelligent traffic control mechanisms. In addition, the monitoring and classification of vehicles can be used in the field of smart parking systems. The required data is measured using networks with a wide range of sensors. Nevertheless, in the context of smart cities no existing solution for traffic flow detection and vehicle classification is able to guarantee high classification accuracy, low deployment and maintenance costs, low power consumption and a weather-independent operation while respecting privacy. In this paper, we propose a radiobased approach for traffic flow detection and vehicle classification using signal attenuation measurements and machine learning algorithms. The results of comprehensive measurements in the field prove its high classification success rate of about 99%.