[Calculation and Evolution of Traffic Carbon Emission in a Mixed Traffic Environment].

With the extensive use of electric vehicles, it is of great significance to measure traffic carbon emissions and analyze its evolution law under the mixed traffic environment in order to effectively achieve traffic carbon reduction. Utilizing taxi GPS data from 2016, 2018, 2020, and 2022, we assessed the carbon emission levels of taxis in Xi'an and matched the results to a grid using map matching. The K-means algorithm was used to analyze the spatial clustering and spatial-temporal distribution of carbon emissions, and the gradient boosting iterative decision tree model （GBDT） was used to explore the influence of built environments on carbon emissions.

Monitoring overloaded trucks with infrared thermal imaging of tire sidewall.

Overloaded trucks have long posed a threat to the road safety. To assess truck payload more effectively, this study focus on tire temperature data obtained through infrared thermal imaging. It is feasible to analyse the payload by monitoring one single representative tire.

Can job stress, health status and risky driving behaviours predict the crash risk level of taxi drivers? New evidence from China.

Despite statistics indicating that China has the world's largest taxi industry, there exists limited research about the relationship between workplace health hazards and taxi driver occupational crashes. In this paper, a cross-sectional survey of taxi drivers in four typical Chinese cities was conducted, and data on their self-reported job stress, health status, and daily risky driving behaviours, together with crash involvement experience in the two years before the survey was collected. Three hypotheses were then developed, and they were verified multivariate analysis of variance (MANOVA) that the seriousness of drivers' health problems and the frequency of their daily risky driving behaviours could be the accurate predictor of their crash risk of taxi drivers.