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A Comparison of ML Models for Predicting Congestion in Urban Cities

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International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

This study predicts traffic congestion in four US cities using various machine learning models. The research utilizes different regression-based models to predict congestion. Results from various models are presented, emphasizing their individual performances in terms of common evaluation metrics. XGBoost is applied with detrending and optimization to enhance predictive accuracy for MSE, MAE, and the coefficient of determination The proposed model demonstrates the lowest MAE (6.39), and achieves the lowest RMSE (10.99), showcasing the variability in model effectiveness. Additionally, we explore the sensitivity of selected features, shedding light on their impact on model results and contributing to a better understanding of our predictive approach. Coefficients of determination across all models indicate diverse predictive capabilities. Analysis is conducted on the performances of various models, providing a robust showcase of their individual strengths and weaknesses across commonly used evaluation metrics. The implicit problem background highlights the inadequacies in existing traffic flow prediction methodologies, prompting the introduction of innovative approaches. These findings are the first of their kind for traffic flow prediction in urban freeways of four US cities using this approach. The research holds significance for intelligent transportation management systems and urban planning. This research introduces an innovative prediction approach and showcases performance variations among different models. The practical applications extend to reducing travel times, addressing environmental concerns, and aiding decision-making for congestion alleviation and improved overall traffic flow. In conclusion, this research bridges critical gaps in traffic prediction methodologies, offering valuable insights and applications for urban planning and intelligent transportation systems.

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Availability of Data and Materials

The data and materials supporting the findings of this study are available upon reasonable request to the corresponding author.

Abbreviations

AI:

Artificial Intelligence

RMSE:

Root Mean Square Error

MAPE:

Mean Absolute Percent Error

PR:

Penetration Rate

LASSO:

Least Absolute Shrinkage and Selection Operator

MAD:

Mean Absolute Deviation

MRE:

Mean Relative Error

OLS:

Ordinary Least Squares

MSE:

Mean Square Error

ML:

Machine Learning

XGBoost :

EXtreme Gradient Boosting

SUMO:

Simulation of Urban Mobility

MAE:

Mean Absolute Error

LR:

Linear Regressor

NN:

Neural Network

Adaboost:

Adaptive boosting

DL:

Deep Learning

DT:

Decision Tree

PCA:

Principal Component Analysis

GB:

Gradient Boosting

RF:

Random Forest

CATboost:

CATegorical boost

SVM:

Support Vector Machine

MARE:

Mean Absolute Relative Error

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, DCRUST, Murthal, Sonepat, 131039, Haryana, India

    Deepika & Gitanjali Pandove

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  1. Deepika
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  2. Gitanjali Pandove
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Contributions

The study’s conception and design were a collective endeavor, with all authors (D and GP) contributing their insights and expertise. Material preparation, data collection, and rigorous analysis were skillfully executed by D and GP exemplifying their dedicated contributions to the research process. All authors read and approved the final version of the manuscript.

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Correspondence to Deepika.

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Deepika, Pandove, G. A Comparison of ML Models for Predicting Congestion in Urban Cities. Int. J. ITS Res. 22, 171–188 (2024). https://doi.org/10.1007/s13177-024-00387-3

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