Effects of controlling aggressive driving behavior on network-wide traffic flow and emissions

Abstract

This paper assesses the impact of adopting smooth driving habits on traffic and emissions in large scale urban networks. An integrated data driven and simulation methodology is proposed, which, first tries to reveal the prevailing driving profiles using real world driving behavior data gathered from smartphones, and second, to simulate the effect of controlling the observed profiles on traffic and emissions using microscopic simulation. The available database contains a total of 4156 urban trips originating from 100 distinct drivers, for who detailed driving behavior data exist. A k-means clustering algorithm is implemented to extract the driving profiles based on speed and acceleration data. The simulation results show that smooth driving leads to a statistically significant reduction in the emissions of the most important air pollutants. Moreover, limiting the variability of the acceleration to a narrower range, as it occurs in the eco profile, leads to an increase in the output of vehicles in comparison to the other profiles.

Keywords

• Driving profiles
• Clustering
• Simulation
• Traffic flow
• Emissions