A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities

TL;DR

This paper introduces a real-time, cost-effective vehicle classification system using radio-fingerprinting, achieving over 99% accuracy in distinguishing cars and trucks for smart city traffic management.

Contribution

It presents a novel radio-fingerprinting-based vehicle classification system that is accurate, privacy-preserving, weather-independent, and suitable for real-time traffic control in smart cities.

Findings

Success ratio for classifying cars and trucks exceeds 99%.

System is cost-efficient and suitable for real-time deployment.

Performance validated through comprehensive field measurements.

Abstract

The measurement and provision of precise and upto-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic controls systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data such as velocity of individual vehicles as well as vehicle type information can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%.