Congestion and Pollutant Emission Analysis of Urban Road Networks Based on Floating Vehicle Data

Abstract

Global warming caused by greenhouse gas (GHG) is receiving increasingly attention from all over the world, and urban transportation is a significant source of greenhouse gas and pollutant emission. However, the research on traffic state of urban road networks (URNs) based on sparse floating vehicle data (FVD) is insufficient. Therefore, we mainly utilize big data techniques to explore the congestion and pollutant emission of URN with FVD. Firstly, the location of vehicles is identified and matched with the URN. We then grid the FVD and city maps to more accurately identify areas of congestion and emission in later section. Following this, we use the congestion index and K-means clustering algorithm to evaluate the traffic state over time, pollutant emission is calculated based on emission calculation standards and carbon emission is estimated by using the fuel consumption-speed model. The results indicate that congestion and emission are very severe during peak hours (e.g., 8:00 a.m.), particularly in some transportation hub areas, such as high-speed rail stations. During off-peak hours (e.g., 11:00 p.m.), congestion and emission are relatively lower. The negative correlation between congestion index and emission is also revealed. This study provides some practical approaches to more accurately estimate the overall urban traffic state by using sparse traffic data, and may offer support to urban traffic managers in managing traffic congestion and pollutant emissions.