United Prediction of Travel Modes and Purposes in Travel Chains Based on Multitask Learning Deep Neural Networks

Predicting and analyzing travel mode choices and purposes are significant to improve urban travel mobility and transportation planning. Previous research has ignored the interconnection between travel mode choices and purposes and thus overlooked their potential contributions to predictions. Using individual travel chain data collected in South Korea, this study proposes a Multi-Task Learning Deep Neural Network (MTLDNN) framework, integrating RFM (Recency, Frequency, Monetary) to achieve a joint prediction of travel mode choices and purposes. The MTLDNN is constructed to share a common hidden layer that extracts general features from the input data, while task-specific output layers are dedicated to predicting travel modes and purposes separately. This structure allows for efficient learning of shared representations while maintaining the capacity to model task-specific relationships. RFM is then integrated to optimize the extraction of users’ behavioral features, which helps in better understanding the temporal and financial patterns of users’ travel activities. The results show that the MTLDNN demonstrates consistent input variable replacement modes and selection probabilities in generating behavioral replacement patterns. Compared to the multinomial logit model (MNL), the MTLDNN achieves lower cross-entropy loss and higher prediction accuracy. The proposed framework could enhance transportation planning, efficiency, and user satisfaction by enabling more accurate predictions.