International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Urban traffic congestion poses a major, worldwide problem, resulting in substantial financial losses, increased air pollution, and a decline in the overall quality of life. Current monitoring methods, such as embedded loop sensors and cameras positioned on roadsides, are often restricted in their coverage, expensive to maintain, and suffer from insufficient data in areas that lack adequate instrumentation. This study introduces a new, easily scalable strategy for detecting traffic congestion in real-time and predicting it in the short term. This method relies on analyzing the density of aggregated and anonymous mobile device location data. We exploit the widespread availability of mobile phones as ubiquitous, cost-effective sensors to gather precise spatial and temporal data about how vehicles are moving. The proposed technique involves dividing the urban area into a uniform grid, calculating a constantly changing mobile device density for each section, and combining this with estimated average vehicle speeds to produce a comprehensive Congestion Index.
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We tested and compared three sophisticated Artificial Intelligence (AI) models; Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks; to classify current traffic jam levels and anticipate future conditions. The results, based on a vast, anonymous dataset from a major urban area, clearly show that the LSTM model's time-series forecasting capability is superior to the tree-based ensemble methods for short-term prediction. It achieved an impressive score of 0.94 and a minimal Mean Absolute Error (MAE) of 0.05 when predicting congestion. This method, which relies on density analysis, presents a reliable, economical, and easily scalable replacement for expensive traditional infrastructure, offering city planners and traffic managers immediate, practical information to effectively reduce traffic jams.

Keywords: Mobile location data, Traffic congestion detection, Artificial intelligence, Smart cities, Density analysis

Downloads: | DOI: 10.17148/IJARCCE.2026.15202