IDEAS home Printed from https://ideas.repec.org/a/abq/ijist1/v3y2022i4p210-222.html
   My bibliography  Save this article

Visualizing Impact of Weather on Traffic Congestion Prediction: A Quantitative Study

Author

Listed:
  • Muhammad Salman Chaudhry

    (Dept. of Computer Science, FCC University Pakistan.)

  • Shahrukh Hussain

    (Dept. of Computer Science, FCC University Pakistan.)

  • Usama Munir

    (Dept. of Computer Science, FCC University Pakistan.)

Abstract

A substantial amount of research has been done to develop improved Intelligent Transportation Systems (ITS) to alleviate traffic congestion problems. These include methods that incorporate the indirect impact on traffic flow such as weather. In this paper, we studied the impact of weather conditions on traffic congestion along with more spatial and temporal factors, such as weekdays/time and location, which is a different approach to this problem. The proposed solution uses all these indicators to estimate the flow of traffic. We evaluate the level of congestion (LOC) based on the traffic volume grouped in certain regions of the city. The index for the defined LOC indicates the traffic flow from “free -flowing” to “traffic jam”. The data for the traffic volume count is collected from the Department of Transportation (DOT) for NYMTC. Weather conditions along with special and temporal information have an essential role in predicting the congestion level. We used supervised machine learning for this purpose. The prediction models are based on certain factors such as the volume count of the traffic at the entry and exit point of each street pair, particular days of the week, timestamp, geographical location, and weather parameters. The study is done on the major roadways of each of the four prominent boroughs in New York. The results of the traffic prediction model were established by using the Gradient Boosting Regression Tree (GBRT) which showed an accuracy of 97.12%. Moreover, the calculation speed was relatively fast, and it has stronger applicability to the prediction of congestion conditions.

Suggested Citation

  • Muhammad Salman Chaudhry & Shahrukh Hussain & Usama Munir, 2022. "Visualizing Impact of Weather on Traffic Congestion Prediction: A Quantitative Study," International Journal of Innovations in Science & Technology, 50sea, vol. 3(4), pages 210-222, february.
  • Handle: RePEc:abq:ijist1:v:3:y:2022:i:4:p:210-222
    DOI: 10.33411/IJIST/2021030517
    as

    Download full text from publisher

    File URL: https://journal.50sea.com/index.php/IJIST/Visualizing-Impact-of-Weather-on-Traffic
    Download Restriction: no

    File URL: https://journal.50sea.com/index.php/IJIST/article/view/125
    Download Restriction: no

    File URL: https://libkey.io/10.33411/IJIST/2021030517?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ge Shi & Jie Shan & Liang Ding & Peng Ye & Yang Li & Nan Jiang, 2019. "Urban Road Network Expansion and Its Driving Variables: A Case Study of Nanjing City," IJERPH, MDPI, vol. 16(13), pages 1-16, June.
    Full references (including those not matched with items on IDEAS)

    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. Ming Li & Guojun Zhang & Ying Liu & Yongwang Cao & Chunshan Zhou, 2019. "Determinants of Urban Expansion and Spatial Heterogeneity in China," IJERPH, MDPI, vol. 16(19), pages 1-19, October.
    2. Zezhou Wu & Danting Zhang & Shenghan Li & Jianbo Fei & Changhong Chen & Bin Tian & Maxwell Fordjour Antwi-Afari, 2022. "Visualizing and Understanding Shrinking Cities and Towns (SCT) Research: A Network Analysis," IJERPH, MDPI, vol. 19(18), pages 1-14, September.
    3. Ayodele Adekunle Faiyetole & Victor Ayodeji Adewumi, 2024. "Urban expansion and transportation interaction: Evidence from Akure, southwestern Nigeria," Environment and Planning B, , vol. 51(1), pages 57-74, January.
    4. Min Zhu & Wenbo Zhou & Min Hu & Juan Du & Tengfei Yuan, 2023. "Evaluating the Renewal Degree for Expressway Regeneration Projects Based on a Model Integrating the Fuzzy Delphi Method, the Fuzzy AHP Method, and the TOPSIS Method," Sustainability, MDPI, vol. 15(4), pages 1-27, February.
    5. Euis Puspita Dewi & Siti Sujatini & Fitri Suryani & ST. Trikariastoto & Ari Wijaya, 2022. "Canals To Streets: Postcolonial Studies On The Urban Transformation Of Colonial Batavia," Engineering Heritage Journal (GWK), Zibeline International Publishing, vol. 6(1), pages 25-30, April.

    More about this item

    Keywords

    Gradient Boosting; Decision Tree Algorithm; Supervised Machine Learning; Traffic Congestion;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    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:abq:ijist1:v:3:y:2022:i:4:p:210-222. 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: Hafiz Haroon Ahmad, Iqra Nazeer (email available below). General contact details of provider: .

    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.