# CrowdRadar: a mobile crowdsensing framework for urban traffic green travel safety risk assessment

## TL;DR

CrowdRadar uses mobile devices to assess urban traffic safety for green travel, detecting risky behaviors and predicting safety risks in real time.

## Contribution

A novel mobile edge crowdsensing framework for real-time urban green travel safety risk assessment.

## Key findings

- CrowdRadar detected traffic high-risk behaviors with an average F1-score of 86.5%.
- The framework achieved an R2 score of 0.85 in assessing travel safety risks.
- Outperformed baseline methods in real-time safety risk assessment.

## Abstract

As environmental awareness increased due to the surge in greenhouse gases, green travel modes such as bicycles and walking have gradually became popular choices. However, the current traffic environment has many hidden problems that endanger the personal safety of traffic participants and hinder the development of green travel. Traditional methods, such as identifying risky locations after traffic accidents, suffer from the disadvantages of delayed response and lack of foresight. Against this background, we proposed a mobile edge crowdsensing framework to dynamically assess urban traffic green travel safety risks. Specifically, a large number of mobile devices were used to sense the road environment, from which a semantic detection framework detected the traffic high-risk behaviors of traffic participants. Then multi-source and heterogeneous urban crowdsensing data were used to model the travel safety risk to achieve a comprehensive and real-time assessment of urban green travel safety. We evaluated our method by leveraging real-world datasets collected from Xiamen Island. Results showed that our framework could accurately detect traffic high-risk behaviors with average F1-scores of 86.5% and assessed the travel safety risk with R2 of 0.85 outperforming various baseline methods.