IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i6p1684-d215712.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/6/1684/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/6/1684/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saeed Vasebi & Yeganeh M. Hayeri, 2021. "Collective Driving to Mitigate Climate Change: Collective-Adaptive Cruise Control," Sustainability, MDPI, vol. 13(16), pages 1-30, August.
    2. Santos, Alberto & Maia, Pedro & Jacob, Rodrigo & Wei, Huang & Callegari, Camila & Oliveira Fiorini, Ana Carolina & Schaeffer, Roberto & Szklo, Alexandre, 2024. "Road conditions and driving patterns on fuel usage: Lessons from an emerging economy," Energy, Elsevier, vol. 295(C).
    3. Mengwei Chen & Dianhai Wang & Yilin Sun & E. Owen D. Waygood & Wentao Yang, 2020. "A comparison of users’ characteristics between station-based bikesharing system and free-floating bikesharing system: case study in Hangzhou, China," Transportation, Springer, vol. 47(2), pages 689-704, April.
    4. Jaller, Miguel & Pahwa, Anmol & Zhang, Michael, 2021. "Cargo Routing and Disadvantaged Communities," Institute of Transportation Studies, Working Paper Series qt9qg2318x, Institute of Transportation Studies, UC Davis.
    5. Bo Yang & Yao Wu & Weihua Zhang & Jie Bao, 2020. "Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    6. Lu Cheng & Zhifu Mi & D’Maris Coffman & Jing Meng & Dining Liu & Dongfeng Chang, 2022. "The Role of Bike Sharing in Promoting Transport Resilience," Networks and Spatial Economics, Springer, vol. 22(3), pages 567-585, September.
    7. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).
    8. Pengfei Lin & Jiancheng Weng & Quan Liang & Dimitrios Alivanistos & Siyong Ma, 2020. "Impact of Weather Conditions and Built Environment on Public Bikesharing Trips in Beijing," Networks and Spatial Economics, Springer, vol. 20(1), pages 1-17, March.
    9. Yang Wang & Alessandra Boggio-Marzet, 2018. "Evaluation of Eco-Driving Training for Fuel Efficiency and Emissions Reduction According to Road Type," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    10. Robaina, Margarita & Neves, Ana, 2021. "Complete decomposition analysis of CO2 emissions intensity in the transport sector in Europe," Research in Transportation Economics, Elsevier, vol. 90(C).
    11. Wojciech Adamski & Krzysztof Brzozowski & Jacek Nowakowski & Tomasz Praszkiewicz & Tomasz Knefel, 2021. "Excess Fuel Consumption Due to Selection of a Lower Than Optimal Gear—Case Study Based on Data Obtained in Real Traffic Conditions," Energies, MDPI, vol. 14(23), pages 1-15, November.
    12. Panagiotis Fafoutellis & Eleni G. Mantouka & Eleni I. Vlahogianni, 2020. "Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    13. Juan Francisco Coloma & Marta García & Gonzalo Fernández & Andrés Monzón, 2021. "Environmental Effects of Eco-Driving on Courier Delivery," Sustainability, MDPI, vol. 13(3), pages 1-21, January.
    14. Bi, Hui & Ye, Zhirui & Hu, Liyang & Zhu, He, 2021. "Why they don't choose bus service? Understanding special online car-hailing behavior near bus stops," Transport Policy, Elsevier, vol. 114(C), pages 280-297.
    15. Kyoungok Kim, 2024. "Discovering spatiotemporal usage patterns of a bike-sharing system by type of pass: a case study from Seoul," Transportation, Springer, vol. 51(4), pages 1373-1407, August.
    16. Xu, Chengcheng & Xu, Shuoyan & Wang, Chen & Li, Jing, 2019. "Investigating the factors affecting secondary crash frequency caused by one primary crash using zero-inflated ordered probit regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 121-129.
    17. Huang, Yuhan & Surawski, Nic C. & Zhuang, Yuan & Zhou, John L. & Hong, Guang, 2021. "Dual injection: An effective and efficient technology to use renewable fuels in spark ignition engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    18. Zhang, Hanyu & Du, Lili, 2023. "Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part I: Modeling and solution algorithm design," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 174-198.
    19. Shah, Nitesh R. & Guo, Jing & Han, Lee D. & Cherry, Christopher R., 2023. "Why do people take e-scooter trips? Insights on temporal and spatial usage patterns of detailed trip data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    20. Alexander García-Mariaca & Eva Llera-Sastresa, 2021. "Review on Carbon Capture in ICE Driven Transport," Energies, MDPI, vol. 14(21), pages 1-30, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1684-:d:215712. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.