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Evaluating the Effects of Household Characteristics on Household Daily Traffic Emissions Based on Household Travel Survey Data

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  • Chengcheng Xu

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, Si Pai Lou #2, Nanjing 210096, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Si Pai Lou #2, Nanjing 210096, China
    School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China)

  • Shuyue Wu

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, Si Pai Lou #2, Nanjing 210096, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Si Pai Lou #2, Nanjing 210096, China
    School of Transportation, Southeast University, Si Pai Lou #2, Nanjing 210096, China)

Abstract

This study aimed to investigate the effects of household characteristics on household traffic emissions. The household travel survey data conducted in the Jiangning District of Nanjing City, China were used. The vehicle emissions of household members’ trips were calculated using average emission factors by average speed and vehicle category. Descriptive statistics analysis showed that the average daily traffic emissions of CO, NO x and PM 2.5 per household are 8.66 g, 0.55 g and 0.04 g respectively. The household traffic emissions of these three pollutants were found to have imbalanced distributions across households. The top 20% highest-emission households accounted for nearly two thirds of the total emissions. Based on the one-way ANOVA tests, the means of CO, NO x and PM 2.5 emissions were found to be significantly different over households with different member numbers, automobile numbers, annual income and access to the subway. Finally, the household daily traffic emissions were linked with household characteristics based on multiple linear regressions. The contributing factors are slightly different among the three different emissions. The number of private vehicles, number of motorcycles, and household income significantly affect all three emissions. More specifically, the number of private vehicles has positive effects on CO and PM 2.5 emissions, but negative effect on NOx emissions. The number of motorcycles and the household income have positive effects on all three emissions.

Suggested Citation

  • Chengcheng Xu & Shuyue Wu, 2019. "Evaluating the Effects of Household Characteristics on Household Daily Traffic Emissions Based on Household Travel Survey Data," Sustainability, MDPI, vol. 11(6), pages 1-12, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1684-:d:215712
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    References listed on IDEAS

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    1. Xu, Chengcheng & Wang, Yong & Liu, Pan & Wang, Wei & Bao, Jie, 2018. "Quantitative risk assessment of freeway crash casualty using high-resolution traffic data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 299-311.
    2. Huang, Yuhan & Ng, Elvin C.Y. & Zhou, John L. & Surawski, Nic C. & Chan, Edward F.C. & Hong, Guang, 2018. "Eco-driving technology for sustainable road transport: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 596-609.
    3. Jie Bao & Chengcheng Xu & Pan Liu & Wei Wang, 2017. "Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests," Networks and Spatial Economics, Springer, vol. 17(4), pages 1231-1253, December.
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    Cited by:

    1. Shen Zhao & Yong Xu, 2019. "Exploring the Spatial Variation Characteristics and Influencing Factors of PM 2.5 Pollution in China: Evidence from 289 Chinese Cities," Sustainability, MDPI, vol. 11(17), pages 1-17, August.
    2. Wenhui Zhang & Hao Chen & Hongzhuo Zhou & Changhang Wu & Ziwen Song, 2023. "Exploring the Characteristics of Green Travel and the Satisfaction It Provides in Cities Located in Cold Regions of China: An Empirical Study in Heilongjiang Province," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    3. Vasile Dogaru & Claudiu Brandas & Marian Cristescu, 2019. "An Urban System Optimization Model Based on CO 2 Sequestration Index: A Big Data Analytics Approach," Sustainability, MDPI, vol. 11(18), pages 1-14, September.

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