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

arXiv: https://arxiv.org/abs/1801.03317 · HTML: https://mlsanity.com/paper/1801.03317/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2018.

## Built on by (most cited first, 2 of 2)

- [Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning](https://mlsanity.com/paper/2101.09143/) (2021)
- [LiVeR: Lightweight Vehicle Detection and Classification in Real-Time](https://mlsanity.com/paper/2206.06173/) (2022)

## Abstract (arXiv)

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%.