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Optimal Distributed Generator Sizing and Placement by Modified Particle Swarm Optimization (MPOS) Algorithm for Minimization of Distribution Power Loss

Anshu Sharma Rajesh Kumar Rai Ghizal Ansari
Reseach Article

Road Traffic Congestion Prediction using AI/ML and Spatiotemporal Data: Literature Review

by Mohammad Alam
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Journal of Advanced Artificial Intelligence
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 4
Year of Publication: 2025
Authors: Mohammad Alam
10.5120/jaai202411

Mohammad Alam . Road Traffic Congestion Prediction using AI/ML and Spatiotemporal Data: Literature Review. Journal of Advanced Artificial Intelligence. 1, 4 ( Jan 2025), 1-7. DOI=10.5120/jaai202411

@article{ 10.5120/jaai202411,
author = { Mohammad Alam },
title = { Road Traffic Congestion Prediction using AI/ML and Spatiotemporal Data: Literature Review },
journal = { Journal of Advanced Artificial Intelligence },
issue_date = { Jan 2025 },
volume = { 1 },
number = { 4 },
month = { Jan },
year = { 2025 },
pages = { 1-7 },
numpages = {9},
url = { https://jaaionline.phdfocus.com/archives/volume1/number4/road-traffic-congestion-prediction-using-aiml-and-spatiotemporal-data-literature-review/ },
doi = { 10.5120/jaai202411 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-02-01T00:09:13+05:30
%A Mohammad Alam
%T Road Traffic Congestion Prediction using AI/ML and Spatiotemporal Data: Literature Review
%J Journal of Advanced Artificial Intelligence
%V 1
%N 4
%P 1-7
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Road traffic congestion is a critical issue that impacts urban mobility, environmental sustainability, and economic productivity. The advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies, combined with spatiotemporal data, offers promising solutions for predicting and managing traffic congestion more effectively. This literature review explores the current state of research on AI/ML models for traffic congestion prediction, focusing on integrating spatiotemporal data, including traffic flow, weather conditions, road networks, and temporal patterns. It examines various predictive modeling techniques, such as supervised, deep, and reinforcement learning. The review also highlights the challenges, such as data quality, model generalization, and computational efficiency, while discussing the potential of hybrid approaches that combine multiple modeling strategies. Furthermore, it presents a summary of key findings, trends, and future directions for the development of intelligent traffic management systems.

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Index Terms

Computer Science
Information Sciences

Keywords

Traffic Congestion Prediction Artificial Intelligence (AI) Machine Learning (ML) Spatiotemporal Data Predictive Analytics

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