01697461

Component

Eco-driving has been gaining increasing interest the last decade not only as means to reduce fuel consumption, but also as a global strategy to reduce emissions and improve air quality. The scope of this paper is to assess the impact of eco driving on traffic flow and emissions in large scale urban networks. To this end, a methodological approach based on data science and simulation is proposed, which, first targets to reveal the prevailing driving profiles using real world driving data gathered from smartphones, and, second, to simulate the effect of controlling the observed profiles to traffic and emission using a microscopically simulated scenarios of different driving profiles of the road network of the city of Luxembourg. The available database contains a total of 4156 urban trips originating from 100 distinct drivers, for whom detailed driving analytics exist. A k-means algorithm is implemented to extract the driving profiles based on speed and acceleration data. Results show that eco driving leads to a statistically significant reduction in the emissions of the most important air pollutants. Moreover, limiting the acceleration to a narrower range, as it occurs in the eco profile, lead to an increase in the output of vehicles in comparison to the other profiles. This finding may prove particularly important in the future as autonomous, interconnected vehicles through a c-ITS may be able to adjust to one another’s kinematic characteristics and increase the capacity of existing roads, although with limitations concerning the application of this principle in different traffic conditions.

This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.

19-04158

English

7p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References; Tables

Ecodriving; Networks; Pollutants; Traffic flow; Urban highways

Environment; Highways; Operations and Traffic Management

Transportation Research Board Annual Meeting 2019 Paper #19-04158

TRIS, TRB, ATRI

Dec 7 2018 9:28AM