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• W2521009968 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." @default.
• W2521009968 created "2016-09-23" @default.
• W2521009968 creator A5039155779 @default.
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• W2521009968 date "2016-09-14" @default.
• W2521009968 modified "2023-09-24" @default.
• W2521009968 title "A comparative study of driving performance in metropolitan regions using large-scale vehicle trajectory data: Implications for sustainable cities" @default.
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• W2521009968 doi "https://doi.org/10.1080/15568318.2016.1230803" @default.
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