FEN-MRMGCN: A Frontend-Enhanced Network Based on Multi-Relational Modeling GCN for Bus Arrival Time Prediction

Accurate bus arrival time prediction is crucial for enhancing passenger experience and optimizing smart city transit systems. Existing methods, typically based on single-route, sparse stop data, struggle with the complex spatiotemporal interactions present in dense stop areas and multi-route networks, resulting in lower prediction accuracy. In this paper, we propose a frontend-enhanced time-series prediction network, in which the Multi-Relational Modeling Graph Convolution (MRMGCN) as the frontend-enhanced module, called FEN-MRMGCN. The proposed module captures spatial relationships in dense, multi-route areas by using graph convolution layers based on multi-relational modeling to aggregate spatial information. The network then uses a conventional time-series model to capture temporal dynamics. Our approach effectively combines external factors, such as traffic congestion and weather conditions, particularly in dense bus route areas, thereby significantly enhancing bus arrival time prediction accuracy. Moreover, we compile and analyze a comprehensive dataset comprising passenger flow, traffic conditions, weather information, and arrival times for both densely populated bus stop areas and regular areas in Macao. Experimental results demonstrate that our frontend-enhanced network achieves a reduction in the Mean Absolute Percentage Error (MAPE) by 15.36%, 13.44%, and 19.07% compared to traditional time series forecasting models like CNN, LSTM, and Transformer, respectively. Future research will focus on leveraging additional data sources and exploring advanced graph convolutional architectures to further elevate prediction accuracy.