Interpretability Study of Gradient Information in Individual Travel Prediction
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/10/5269 |
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| author | Ziheng Su Pengfei Zhang Xiaohui Song Yifan Li |
| author_facet | Ziheng Su Pengfei Zhang Xiaohui Song Yifan Li |
| author_sort | Ziheng Su |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9b921762e08f4e3880b98b6b47583e3d |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-9b921762e08f4e3880b98b6b47583e3d2025-08-20T03:14:38ZengMDPI AGApplied Sciences2076-34172025-05-011510526910.3390/app15105269Interpretability Study of Gradient Information in Individual Travel PredictionZiheng Su0Pengfei Zhang1Xiaohui Song2Yifan Li3Institute of Physics, Henan Academy of Sciences, Zhengzhou 450046, ChinaInstitute of Physics, Henan Academy of Sciences, Zhengzhou 450046, ChinaInstitute of Physics, Henan Academy of Sciences, Zhengzhou 450046, ChinaInstitute of Physics, Henan Academy of Sciences, Zhengzhou 450046, ChinaWith 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.https://www.mdpi.com/2076-3417/15/10/5269interpretabilitygradient informationindividual travel predictiondeep learning model |
| spellingShingle | Ziheng Su Pengfei Zhang Xiaohui Song Yifan Li Interpretability Study of Gradient Information in Individual Travel Prediction Applied Sciences interpretability gradient information individual travel prediction deep learning model |
| title | Interpretability Study of Gradient Information in Individual Travel Prediction |
| title_full | Interpretability Study of Gradient Information in Individual Travel Prediction |
| title_fullStr | Interpretability Study of Gradient Information in Individual Travel Prediction |
| title_full_unstemmed | Interpretability Study of Gradient Information in Individual Travel Prediction |
| title_short | Interpretability Study of Gradient Information in Individual Travel Prediction |
| title_sort | interpretability study of gradient information in individual travel prediction |
| topic | interpretability gradient information individual travel prediction deep learning model |
| url | https://www.mdpi.com/2076-3417/15/10/5269 |
| work_keys_str_mv | AT zihengsu interpretabilitystudyofgradientinformationinindividualtravelprediction AT pengfeizhang interpretabilitystudyofgradientinformationinindividualtravelprediction AT xiaohuisong interpretabilitystudyofgradientinformationinindividualtravelprediction AT yifanli interpretabilitystudyofgradientinformationinindividualtravelprediction |