Year: 2025
Type: article
Abstract: With the development of intelligent transportation systems (ITS), individual travel prediction has become a key technology for optimizing urban transportation. However, deep learning models are limited in decision-sensitive scenarios due to their lack of interpretability. To address the shortcomings of existing XAI methods in analyzing the dynamic features of historical travel sequences, this paper introduces an alternative interpretability method based on gradient information, overcoming the interpretability bottleneck of travel prediction models. This method calculates the gradient information of input features relative to the prediction result, breaking through the limitations of traditional interpreters that only analyze static features. It can trace the contribution weights of key time points in historical travel sequences while maintaining low computational cost. The experimental results show that features with higher gradients significantly affect predictions—masking the maximum-gradient feature reduces accuracy by approximately 30%. Descending-order masking strategies exhibit the strongest impact, highlighting nonlinear interactions among features. Contribution maps visualize how gradients capture regular patterns and anomalies. The method proposed in this paper provides a valuable tool for understanding the underlying principles of travel prediction models, bridging the gap in existing methods for temporal sequence analysis.
Source: Applied Sciences
DOI: 10.3390/app15105269
Authors: Ziheng Su, Pengfei Zhang, Xiaohui Song, Yifan Li
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4410243073