Gaussian dual adjacency graph based spatial correlated and temporal time dependent traffic prediction in Bangalore City

Sathish Kumar Ravichandran, Chin‐Shiuh Shieh, Mong-Fong Horng, Arulmurugan Ramu, Archana Sasi

Scientific Reports, 2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.1038/s41598-025-25075-4
Data confidence 70 medium
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Abstract

With the rapid increase in the population, transportation systems are challenged by several issues. Traffic congestion is customary and traffic accidents occur frequently deteriorating traffic environments. To take the edge off these issues and enhance transportation efficiency, accurate traffic forecasting is critical. Accurate temporal time time-dependent traffic predictions are essential for ensuring the safety and efficiency of an intelligent traffic management system. Nevertheless, owing to the intrinsic spatial and temporal dependencies of traffic flow it is still a challenging problem. To solve this, some methods are proposed taking into consideration the detailed traffic patterns across major roads and intersections, while complicated spatiotemporal dynamics and interdependencies between traffic flows are not taken into account. In this work, a method called Gaussian Dual Adjacency Graph-based Spatial Correlated and Temporal Time-dependent (GDAG-SCTT) traffic prediction in Bangalore city is proposed. Initially with the raw traffic patterns obtained from Bangalore's traffic pulse dataset as input are subjected to three different processes, namely, pre-processing and feature extraction. First, Local-Global Invariant Inter Quartile and Min-Max Normalization based Traffic Data Pre-processing is applied to the Bangalore's traffic pulse dataset. Next, the extraction of spatial and temporal features is done by using a Gaussian Kernel Dynamic Adjacency based Spatial Correlated and Temporal Time-dependency based feature extraction model. By applying this pre-processing outliers are removed and finally normalized pre-processed results are obtained. Followed by which, using Spatial Correlated Graph Convolutional Neural Network spatial features are extracted and using Temporal Long Short Term Time-dependency Memory temporal features are extracted. To evaluate the GDAG-SCTT method's performance, classification metrics like precision, recall and accuracy along with regression metrics like root mean square error, training time are validated and analyzed. The GDAG-SCTT achieved higher performance compared to other state-of-the-art methods on our collected Bangalore's traffic pulse dataset demonstrating the efficiency in reducing root mean square error by 28% while improving overall accuracy by 25% in an extensive manner.