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Title: Intensity and daily pattern of passenger vehicle use by region and class in China: estimation and implications for energy use and electrification

Abstract

Given the explosive growth of the passenger vehicle market and energy demands in China, research on vehicle-use intensity and driver-travel patterns is critical for better assessing travel demand and its implications for alternative fuel vehicles, energy security, and environmental policies. This study attempts to estimate annual vehicle kilometers traveled (AVKT) per privately-owned passenger vehicle and their daily distance patterns by vehicle class and geographic region. The data sample from a survey consists of 169,292 privately owned passenger vehicles, made by 177 car manufacturers during 2003–2018, running in 82 cities from 27 provincial regions. The log-transformed average AVKT is estimated to be 12,377 km with 95% probability ranging from 5490 to 28,579 km. The investigation reveals that vehicles from South China have the highest AVKT at 13,320 km. Generally, vehicles in small cities have higher AVKT than in big cities, except AVKT of tier 1 cities being higher. Another trend is that more expensive or larger vehicles tend to be driven more. A model is fitted for estimating AVKT based on region, city type, automaker, price range, and certain vehicle features including class and age. Data of daily commuting distances in recent years are also analyzed. The average daily commuting distancesmore » typically range from 21 to 28 km. Using the validated Gamma distribution method, daily distance distributions are specified for different regions. It is found that 99% of the daily driving distance is no more than 88.0–112.0 km, depending on region. Utility factors of plug-in electric vehicles are also estimated to be much higher than those based on driving data in the USA. Finally, these findings suggest global mitigations strategies on vehicle fuel use, electrification, and greenhouse gases should consider vehicle-use intensity at the regional level.« less

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1];  [2];  [3];  [3];  [3]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. China Automotive Technology and Research Center, Tianjin (China)
  3. Aramco Services Company, Detroit, MI (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1607214
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Mitigation and Adaptation Strategies for Global Change
Additional Journal Information:
Journal Volume: 24; Journal ID: ISSN 1381-2386
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
33 ADVANCED PROPULSION SYSTEMS; vehicle kilometers traveled; daily commuting distance; vehicle-use intensity; China's vehicle market; utility factor

Citation Formats

Ou, Shawn, Yu, Rujie, Lin, Zhenhong, Ren, Huanhuan, He, Xin, Przesmitzki, Steven V., and Bouchard, Jessey. Intensity and daily pattern of passenger vehicle use by region and class in China: estimation and implications for energy use and electrification. United States: N. p., 2019. Web. https://doi.org/10.1007/s11027-019-09887-0.
Ou, Shawn, Yu, Rujie, Lin, Zhenhong, Ren, Huanhuan, He, Xin, Przesmitzki, Steven V., & Bouchard, Jessey. Intensity and daily pattern of passenger vehicle use by region and class in China: estimation and implications for energy use and electrification. United States. https://doi.org/10.1007/s11027-019-09887-0
Ou, Shawn, Yu, Rujie, Lin, Zhenhong, Ren, Huanhuan, He, Xin, Przesmitzki, Steven V., and Bouchard, Jessey. Tue . "Intensity and daily pattern of passenger vehicle use by region and class in China: estimation and implications for energy use and electrification". United States. https://doi.org/10.1007/s11027-019-09887-0. https://www.osti.gov/servlets/purl/1607214.
@article{osti_1607214,
title = {Intensity and daily pattern of passenger vehicle use by region and class in China: estimation and implications for energy use and electrification},
author = {Ou, Shawn and Yu, Rujie and Lin, Zhenhong and Ren, Huanhuan and He, Xin and Przesmitzki, Steven V. and Bouchard, Jessey},
abstractNote = {Given the explosive growth of the passenger vehicle market and energy demands in China, research on vehicle-use intensity and driver-travel patterns is critical for better assessing travel demand and its implications for alternative fuel vehicles, energy security, and environmental policies. This study attempts to estimate annual vehicle kilometers traveled (AVKT) per privately-owned passenger vehicle and their daily distance patterns by vehicle class and geographic region. The data sample from a survey consists of 169,292 privately owned passenger vehicles, made by 177 car manufacturers during 2003–2018, running in 82 cities from 27 provincial regions. The log-transformed average AVKT is estimated to be 12,377 km with 95% probability ranging from 5490 to 28,579 km. The investigation reveals that vehicles from South China have the highest AVKT at 13,320 km. Generally, vehicles in small cities have higher AVKT than in big cities, except AVKT of tier 1 cities being higher. Another trend is that more expensive or larger vehicles tend to be driven more. A model is fitted for estimating AVKT based on region, city type, automaker, price range, and certain vehicle features including class and age. Data of daily commuting distances in recent years are also analyzed. The average daily commuting distances typically range from 21 to 28 km. Using the validated Gamma distribution method, daily distance distributions are specified for different regions. It is found that 99% of the daily driving distance is no more than 88.0–112.0 km, depending on region. Utility factors of plug-in electric vehicles are also estimated to be much higher than those based on driving data in the USA. Finally, these findings suggest global mitigations strategies on vehicle fuel use, electrification, and greenhouse gases should consider vehicle-use intensity at the regional level.},
doi = {10.1007/s11027-019-09887-0},
journal = {Mitigation and Adaptation Strategies for Global Change},
number = ,
volume = 24,
place = {United States},
year = {2019},
month = {10}
}

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