A channel-independent transformer approach for ride-hailing demand prediction with internet sentiment and transport capacity

Ride-hailing demand prediction poses significant challenges due to its inherent dynamism. Although deep learning models, notably Transformer, have shown impressive predictive capabilities, their accuracy needs further improvement, especially when confronted with multiple realistic factors in industrial practice, such as societal and consumer sentiment trends, and dynamic fluctuations in transport capacity. To tackle this challenge, this paper presents an enhanced Channel-independent Transformer (CiFormer) approach for ride-hailing demand prediction that incorporates Internet sentiment. Specifically, we propose an integrated model that combines a BERT-based architecture with BiLSTM and an attention mechanism to effectively extract Internet sentiment from textual data. This enhanced sentiment analysis is subsequently integrated into a customized CiFormer model, which concurrently incorporates multiple exogenous features, specifically transport capacity, to improve prediction accuracy. We validate our approach on a real-world dataset from the Beijing West Railway Station. Experimental results demonstrate that our approach outperforms benchmark models across multiple prediction metrics. Furthermore, the crucial impact of Internet sentiment and transport capacity on ride-hailing demand prediction is highlighted.