| Title | A radio-fingerprinting-based vehicle classification system for intelligent traffic control in smart cities |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Sliwa, Benjamin, Haferkamp, Marcus, Al-Askary, Manar, Dorn, Dennis, Wietfeld, Christian |
| Conference Name | 2018 Annual IEEE International Systems Conference (SysCon) |
| Date Published | apr |
| Keywords | comprehensive measurements, cost-efficiency, CPS, cps privacy, cyber physical systems, data privacy, dynamic control methods, dynamic type-specific lane assignments, feature extraction, global system parameters, highly sophisticated traffic control methods, highly-dynamic Cyber Physical System, Human Behavior, human factors, individual vehicles, intelligent traffic control systems, key element, key performance indicators, learning (artificial intelligence), machine learning, machine learning based approach, Measurement, pattern classification, precise vehicle classification, privacy, privacy preservation, pubcrawl, radio transmitters, radio-fingerprint, radio-fingerprinting, Real-time, Receivers, road traffic control, road vehicles, Roads, smart cities, specific data, street network, Support vector machines, System performance, system state, traffic engineering computing, Traffic flow, up-to-date traffic, vehicle classification system, vehicle type information, vehicular ad hoc networks |
| Abstract | The measurement and provision of precise and up-to-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic control 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%. |
| DOI | 10.1109/SYSCON.2018.8369511 |
| Citation Key | sliwa_radio-fingerprinting-based_2018 |
A radio-fingerprinting-based vehicle classification system for intelligent traffic control in smart cities