A comparative study of driving performance in metropolitan regions using large-scale vehicle trajectory data: Implications for sustainable cities

ABSTRACT

Volatile driving, characterized by hard accelerations and braking, can contribute substantially to higher energy consumption, tailpipe emissions, and crash risks. Drivers’ decisions to maintain speed, accelerate, brake rapidly, or jerk their vehicle are largely constrained by their unique regional and metropolitan contexts. These contexts may be characterized by their geography, roadway structure, traffic management, driving population, etc. This study captures how people generally drive in a region using large-scale vehicle trajectory data, implying how energy is consumed and how emissions are produced in regional transportation systems. Specifically, driving performance in four U.S. metropolitan areas (Los Angeles, San Francisco, Sacramento, and Atlanta) is compared, taking advantage of large-scale behavioral data (78.7 million seconds of speed records), collected by in-vehicle global positioning systems (GPSs) as part of regional surveys. Comparative analysis shows significant regional differences in terms of volatile driving and time spent to accelerate, brake, and jerk the vehicle during daily trips. Correlates of higher volatility are also explored, e.g., battery electric vehicles show low volatility, as expected. This study proposes a novel way to compare regional driving performance by successfully turning GPS driving data into valuable knowledge that can be applied in practice by developing regional driving performance indices. The new indices can also be used to compare regional performance over time and to imply the levels of sustainability of regional transportation systems. This study contributes by proposing a way to extract useful information from large-scale driving data.

KEYWORDS:

• Driving indices
• driving volatility
• large-scale data
• metropolitan region
• mixed-effects model
• sustainability

Acknowledgments

The National Renewable Energy Laboratory provided the data used in this study. The software MATLAB was used for data processing and visualization. Statistical software STATA was used for modeling. Special thanks are extended to the following entities for their support at the University of Tennessee: Transportation Engineering and Science Program and Initiative for Sustainable Mobility.

Disclosure statement

The views expressed in this paper are those of the authors, who are responsible for the facts and accuracy of information presented herein.

Funding

The research was supported through the TranLIVE University Transportation Center (US DOT grant number DTRT12-G-UTC17) and Southeastern Transportation Center (US DOT grant number DTRT13-G-UTC34), both sponsored by the Office of the Secretary of Transportation, U.S. Department of Transportation.