Authors :
Sriram Sridhar
Volume/Issue :
Volume 11 - 2026, Issue 1 - January
Google Scholar :
https://tinyurl.com/3fp8vjw8
Scribd :
https://tinyurl.com/4tas52yk
DOI :
https://doi.org/10.38124/ijisrt/26jan1185
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
With growing economic status, many people are buying vehicles which reflects their growing stature. Sadly, this
is not backed by good traffic management systems which are increasingly leading to clogging of roads and unhealthy
environment prevails leading to deterioration of health as well. Maps such as those provided by major players offset some
of the problems faced by including traffic density in the path planning. But it still does not consider the weather conditions
and emission levels in the area while suggesting a route. Also, the experience of past users of the route is not given any
consideration of the route quality. This means that the estimation of traffic congestion goes beyond the accepted parameters
of traffic density alone but is more intrinsically complicated than that. This also implies that there is no clear rule that can
be written to classify the traffic congestion levels of the area.
This means the only solution to estimate the traffic congestion levels of the region is to develop a robust machine
learning model for the task of classifying the traffic congestion levels. By using this machine learning approach, we can use
several feature parameters to map and model the traffic congestion levels to input and for which a rule-based approach fails
as learning from past data is the only solution. This can result in better path planning and infrastructure making commute
healthy, faster resulting in improved quality oflife.In this paper, we therefore apply a few classification models and determine
its performance using the accuracy metric, training time and model stability. We therefore recommend the best fitting model
from among the two models we have applied to this task of multi-class classification. We believe that this will transform the
way transport is planned and pave way for a cleaner and healthier environment in the long run.
Keywords :
Path Planning, Sustainable Infrastructure, Traffic Congestion, Clean Environment.
References :
- S. A. Ali Shah, X. Fernando and R. Kashef, "Improved Vehicular Congestion Classification using Machine Learning for VANETs," 2024 IEEE International Systems Conference (SysCon), Montreal, QC, Canada, 2024, pp. 1-8, doi: 10.1109/SysCon61195.2024.10553553.
- Laaziza Hammoumi, Saad Farah, Mohamed Benayad, Mehdi Maanan, Hassan Rhinane,”Leveraging machine learning to predict traffic jams: Case study of Casablanca, Morocco,”Journal of Urban Management, 2025,ISSN 2226-5856, https://doi.org/10.1016/j.jum.2025.02.004.
- Rafed Muhammad Yasir, Moumita Asad, Dr. Naushin Nower, Dr. Mohammad Shoyaib,” Traffic Congestion Prediction Using Machine Learning Techniques”, arXiv:2206.10983v4 [cs.LG] 16 Apr 2025
- Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew, “Extreme learning machine: Theory and applications”, Neurocomputing,Volume 70, Issues 1–3, 2006, Pages 489- 501,ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2005.12.126
- https://www.kaggle.com/datasets/ziya07/smart-mobility-traffic-dataset
With growing economic status, many people are buying vehicles which reflects their growing stature. Sadly, this
is not backed by good traffic management systems which are increasingly leading to clogging of roads and unhealthy
environment prevails leading to deterioration of health as well. Maps such as those provided by major players offset some
of the problems faced by including traffic density in the path planning. But it still does not consider the weather conditions
and emission levels in the area while suggesting a route. Also, the experience of past users of the route is not given any
consideration of the route quality. This means that the estimation of traffic congestion goes beyond the accepted parameters
of traffic density alone but is more intrinsically complicated than that. This also implies that there is no clear rule that can
be written to classify the traffic congestion levels of the area.
This means the only solution to estimate the traffic congestion levels of the region is to develop a robust machine
learning model for the task of classifying the traffic congestion levels. By using this machine learning approach, we can use
several feature parameters to map and model the traffic congestion levels to input and for which a rule-based approach fails
as learning from past data is the only solution. This can result in better path planning and infrastructure making commute
healthy, faster resulting in improved quality oflife.In this paper, we therefore apply a few classification models and determine
its performance using the accuracy metric, training time and model stability. We therefore recommend the best fitting model
from among the two models we have applied to this task of multi-class classification. We believe that this will transform the
way transport is planned and pave way for a cleaner and healthier environment in the long run.
Keywords :
Path Planning, Sustainable Infrastructure, Traffic Congestion, Clean Environment.
