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Predictive Analysis for Road Safety Enhancement in Chicago County
1
Department of Business Analytics, Dublin Business School, Dublin, Ireland
2
School of Computing, National College of Ireland, Dublin, Ireland
3
School of Computing, Indian Institute of Technology (BHU) Varanasi, India
* Corresponding Author:
Teerath Kumar,
[email protected]
Volume 2, Issue 1
2025
Received: 16 October 2024
Accepted: 01 November 2024
Published: 07 December 2024
Article Information
Abstract
With the increasing incidents of fatal road injuries, there is an urgent need for developing effective road safety management systems. The study aims to develop predictive models based on machine learning to forecast the likelihood of road collisions depending on factors like weather, road condition, time, and driver behaviour in Chicago, USA. A machine learning approach has been applied to the crash dataset to evaluate the factors affecting the prevalence of road accidents. Python programming and the Jupyter Notebook platform have been used for performing descriptive statistics, correlation and three classification algorithms (Random Forest, KNN, Decision Tree and MLP Classification). Obtained accuracy of the KNN classifier is slightly higher than the other two classification models. The research explored insights into collision patterns related to roads, locations, and intersections. The study helps to increase road safety through targeted interventions with resource prioritisation, reducing the frequency and severity of traffic incidents by leveraging historical accident data with diverse spatial analysis techniques.
Graphical Abstract
Keywords
traffic crashes
machine learning
predictive modeling
road safety
crash severity
Funding
This work was supported without any funding.
References
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APA Style
Shaik, R., Raj, K., Singh, A., & Kumar, T. (2025). Predictive Analysis for Road Safety Enhancement in Chicago County. ICCK Transactions on Computer Science, 2(1), 1–9. https://doi.org/10.62762/TCS.2024.766854
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TY - JOUR AU - Shaik, Reshma AU - Raj, Kislay AU - Singh, Aditya AU - Kumar, Teerath PY - 2024 DA - 2024/12/07 TI - Predictive Analysis for Road Safety Enhancement in Chicago County JO - ICCK Transactions on Computer Science T2 - ICCK Transactions on Computer Science JF - ICCK Transactions on Computer Science VL - 2 IS - 1 SP - 1 EP - 9 DO - 10.62762/TCS.2024.766854 UR - https://www.icck.org/article/abs/TCS.2024.766854 KW - traffic crashes KW - machine learning KW - predictive modeling KW - road safety KW - crash severity AB - With the increasing incidents of fatal road injuries, there is an urgent need for developing effective road safety management systems. The study aims to develop predictive models based on machine learning to forecast the likelihood of road collisions depending on factors like weather, road condition, time, and driver behaviour in Chicago, USA. A machine learning approach has been applied to the crash dataset to evaluate the factors affecting the prevalence of road accidents. Python programming and the Jupyter Notebook platform have been used for performing descriptive statistics, correlation and three classification algorithms (Random Forest, KNN, Decision Tree and MLP Classification). Obtained accuracy of the KNN classifier is slightly higher than the other two classification models. The research explored insights into collision patterns related to roads, locations, and intersections. The study helps to increase road safety through targeted interventions with resource prioritisation, reducing the frequency and severity of traffic incidents by leveraging historical accident data with diverse spatial analysis techniques. SN - request pending PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Shaik2024Predictive,
author = {Reshma Shaik and Kislay Raj and Aditya Singh and Teerath Kumar},
title = {Predictive Analysis for Road Safety Enhancement in Chicago County},
journal = {ICCK Transactions on Computer Science},
year = {2024},
volume = {2},
number = {1},
pages = {1-9},
doi = {10.62762/TCS.2024.766854},
url = {https://www.icck.org/article/abs/TCS.2024.766854},
abstract = {With the increasing incidents of fatal road injuries, there is an urgent need for developing effective road safety management systems. The study aims to develop predictive models based on machine learning to forecast the likelihood of road collisions depending on factors like weather, road condition, time, and driver behaviour in Chicago, USA. A machine learning approach has been applied to the crash dataset to evaluate the factors affecting the prevalence of road accidents. Python programming and the Jupyter Notebook platform have been used for performing descriptive statistics, correlation and three classification algorithms (Random Forest, KNN, Decision Tree and MLP Classification). Obtained accuracy of the KNN classifier is slightly higher than the other two classification models. The research explored insights into collision patterns related to roads, locations, and intersections. The study helps to increase road safety through targeted interventions with resource prioritisation, reducing the frequency and severity of traffic incidents by leveraging historical accident data with diverse spatial analysis techniques.},
keywords = {traffic crashes, machine learning, predictive modeling, road safety, crash severity},
issn = {request pending},
publisher = {Institute of Central Computation and Knowledge}
}
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