Image: Darren Baker / Adobe Stock

Image: Darren Baker / Adobe Stock

Development of surrogate safety measures based on connected vehicles (CV) data


Road fatalities continue to be a global public health crisis. Studies have proven that inappropriate driving behaviours (e.g., speeding, hard-braking) are tightly related to traffic risks, which potentially can be used to generate surrogate safety measures (SSMs) to identify high-risk road segments before crashes occur. However, large-scale assessment of driving behaviours’ relations with crash risks is still underexplored due to data acquisition difficulties. With connected vehicles (CVs) becoming a reality, massive volumes of driving behavioural data can be directly collected from vehicular sensors and become accessible to researchers. This project aims to assess the effectiveness and reliability of passively crowdsourced CV data for road safety assessment. The expected outcomes will include a proposal for the ESRC/EPSRC new investigator grant, two academic papers, and two workshops with stakeholders for promoting this project and building collaborations with other leading researchers across UK universities.

In brief


2022 - 2023


John Fell Fund

TSU Principal Investigator

Dr Xiao Li


Dr Xiao Li