GCT: A Granger-Causal Transformer for Multivariate Traffic Analysis in Smart Villages

Abstract

Predicting vehicle traffic optimizes transportation management and urban planning. In this paper, we combine real-timedata from vehicle-detection Internet of Things (IoT) devices with external variables from Google Trends. Integrating suchheterogeneous, complex data streams is challenging for traditional machine learning models that struggle to capture thedynamics of traffic patterns, which are influenced by multiple interdependent factors. To effectively model these complex,interdependent factors, we introduce the Granger-Causal Transformer (GCT), a transformer-based architecture for trafficprediction that integrates an LSTM network with a modified multi-head attention mechanism. This mechanism extendsGranger causality to the spatio-temporal domain to analyze all causality relations between features consistently, whilecapturing long-range dependencies and temporal patterns. Before applying GCT, we generate lagged versions of the GoogleTrends time series to capture lead and lag effects. Tourists usually make searches about their destination weeks beforetraveling, so peaks in search interest occur earlier than peaks in weekly traffic volume. Using lags aligns the predictors withweekly traffic volume and allows the model to use past searches to predict future traffic. We semantically validate the GoogleTrends terms by comparing each term with a reference string describing the study area, using a language model alignedwith the data’s linguistic context. We then apply a dual filtering process comprising Granger noncausality and correlationtests to minimize noise and redundancy. We evaluate our proposed methodology against classical statistical models, deeplearning models, large foundation models, and transformers across two case studies. The results demonstrate consistentlysuperior performance and generalizability, with GCT achieving R^2 improvements between 47% and 68% compared to the bestperforming baselines across both settings, alongside substantial reductions in MAE and MSE.