SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction
Large-scale crowd flow prediction is a critical task in urban management and public safety. However, achieving accurate and efficient prediction remains challenging. Most existing models overlook spatial heterogeneity, employing unified parameters to fit diverse crowd flow patterns across different...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/10/1686 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850126508395331584 |
|---|---|
| author | Xiaoyong Tan Kaiqi Chen Min Deng Baoju Liu Zhiyuan Zhao Youjun Tu Sheng Wu |
| author_facet | Xiaoyong Tan Kaiqi Chen Min Deng Baoju Liu Zhiyuan Zhao Youjun Tu Sheng Wu |
| author_sort | Xiaoyong Tan |
| collection | DOAJ |
| description | Large-scale crowd flow prediction is a critical task in urban management and public safety. However, achieving accurate and efficient prediction remains challenging. Most existing models overlook spatial heterogeneity, employing unified parameters to fit diverse crowd flow patterns across different spatial units, which limits their accuracy. Meanwhile, the massive spatial units significantly increase the computational cost, limiting model efficiency. To address these limitations, we propose a novel model for large-scale crowd flow prediction, namely the Stratified Compressive Sensing Network (SCS-Net). First, we develop a spatially stratified module that posterior adaptively extracts the underlying spatially stratified structure, effectively modeling spatial heterogeneity. Then, we develop compressive sensing modules to compress redundant information from massive spatial units and learn shared crowd flow patterns, enabling efficient prediction. Finally, we conduct experiments on a large-scale real-world dataset. The results demonstrate that SCS-Net outperforms deep learning baseline models by 35.25–139.2% in MAE and 26.3–112.4% in RMSE while reducing GFLOPs by 53–1067 times and shortening training time by 3.1–83.2 times compared to prevalent spatio-temporal prediction models. Moreover, the spatially stratified structure extracted by SCS-Net offers valuable interpretability for spatial heterogeneity in crowd flow patterns, providing deeper insights into urban functional layouts. |
| format | Article |
| id | doaj-art-98cf5fced30b433b9500f2e057b1b624 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-98cf5fced30b433b9500f2e057b1b6242025-08-20T02:33:55ZengMDPI AGMathematics2227-73902025-05-011310168610.3390/math13101686SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow PredictionXiaoyong Tan0Kaiqi Chen1Min Deng2Baoju Liu3Zhiyuan Zhao4Youjun Tu5Sheng Wu6Department of Geo-Informatics, School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaDepartment of Geo-Informatics, School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaDepartment of Geo-Informatics, School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaDepartment of Geo-Informatics, School of Geosciences and Info-Physics, Central South University, Changsha 410083, ChinaAcademy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, ChinaAcademy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, ChinaAcademy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, ChinaLarge-scale crowd flow prediction is a critical task in urban management and public safety. However, achieving accurate and efficient prediction remains challenging. Most existing models overlook spatial heterogeneity, employing unified parameters to fit diverse crowd flow patterns across different spatial units, which limits their accuracy. Meanwhile, the massive spatial units significantly increase the computational cost, limiting model efficiency. To address these limitations, we propose a novel model for large-scale crowd flow prediction, namely the Stratified Compressive Sensing Network (SCS-Net). First, we develop a spatially stratified module that posterior adaptively extracts the underlying spatially stratified structure, effectively modeling spatial heterogeneity. Then, we develop compressive sensing modules to compress redundant information from massive spatial units and learn shared crowd flow patterns, enabling efficient prediction. Finally, we conduct experiments on a large-scale real-world dataset. The results demonstrate that SCS-Net outperforms deep learning baseline models by 35.25–139.2% in MAE and 26.3–112.4% in RMSE while reducing GFLOPs by 53–1067 times and shortening training time by 3.1–83.2 times compared to prevalent spatio-temporal prediction models. Moreover, the spatially stratified structure extracted by SCS-Net offers valuable interpretability for spatial heterogeneity in crowd flow patterns, providing deeper insights into urban functional layouts.https://www.mdpi.com/2227-7390/13/10/1686crowd flow predictionlarge scalecompressive sensingspatial heterogeneityspatially stratified structure |
| spellingShingle | Xiaoyong Tan Kaiqi Chen Min Deng Baoju Liu Zhiyuan Zhao Youjun Tu Sheng Wu SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction Mathematics crowd flow prediction large scale compressive sensing spatial heterogeneity spatially stratified structure |
| title | SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction |
| title_full | SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction |
| title_fullStr | SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction |
| title_full_unstemmed | SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction |
| title_short | SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction |
| title_sort | scs net stratified compressive sensing network for large scale crowd flow prediction |
| topic | crowd flow prediction large scale compressive sensing spatial heterogeneity spatially stratified structure |
| url | https://www.mdpi.com/2227-7390/13/10/1686 |
| work_keys_str_mv | AT xiaoyongtan scsnetstratifiedcompressivesensingnetworkforlargescalecrowdflowprediction AT kaiqichen scsnetstratifiedcompressivesensingnetworkforlargescalecrowdflowprediction AT mindeng scsnetstratifiedcompressivesensingnetworkforlargescalecrowdflowprediction AT baojuliu scsnetstratifiedcompressivesensingnetworkforlargescalecrowdflowprediction AT zhiyuanzhao scsnetstratifiedcompressivesensingnetworkforlargescalecrowdflowprediction AT youjuntu scsnetstratifiedcompressivesensingnetworkforlargescalecrowdflowprediction AT shengwu scsnetstratifiedcompressivesensingnetworkforlargescalecrowdflowprediction |