Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction
Abstract
:1. Introduction
- Spatial Correlation Factors: Spatial correlation refers to the association between the demand for bike-sharing and spatial locations at the same time. On one hand, the demand for bike-sharing varies depending on the different Points of Interest (POIs) near bike-sharing stations. For example, during the city’s evening peak hours, the demand for bike-sharing near office buildings is higher compared to residential areas. On the other hand, there exists mutual influence in demand between bike-sharing stations, and this influence is correlated with the distance between stations. The increase in demand at station A, as depicted in Figure 1, leads to a corresponding rise in demand at neighboring station B, whereas the demand at more distant station C remains relatively unchanged.
- Spatial Heterogeneity Factors: Spatial heterogeneity refers to sudden, irregular spatial factors influencing bike-sharing demand. For instance, the addition or closure of stations (referred to collectively as dynamic changes in stations) impacts the demand at surrounding stations. As illustrated in Figure 2, the blue dots represent regular stations, yellow dots represent newly added stations, and purple dots represent stations that have disappeared.
- Temporal Correlation Factors: Temporal correlation refers to fluctuations in demand at the same station over time. For instance, people’s travel needs differ on weekdays and weekends, leading to varying demand at the same station at different times. Additionally, since the outbreak of the COVID-19 pandemic, people’s travel patterns have been significantly affected, resulting in fluctuations in bike-sharing demand following the pattern of each pandemic wave, as shown in Figure 3.
- Temporal Heterogeneity Factors: Temporal heterogeneity factors refer to the influence of various unforeseen elements at different points in time on the demand for the same station, including holidays and adverse weather conditions. As illustrated in Figure 4, on days affected by rainy or snowy weather, the demand decreases.
- In the proposed LGDMGCN model, a local-global spatiotemporal multi-graph convolution module is designed to learn multiscale spatiotemporal correlation features. It fully utilizes the spatial semantic information of stations to construct multiple graphs and employs multi-graph convolution to capture spatial features. Time gate units are used to capture temporal features, and the local-global structure incorporates features both around individual stations and across all stations globally. Additionally, a time attention mechanism is applied to focus on the long-term dependencies in the time series, capturing global relationships.
- In the context of the COVID-19 pandemic, the model integrates multi-source data, including confirmed COVID-19 cases, holidays, and weather conditions, to learn the heterogeneity features of time. Considering the actual scenario of dynamic station changes, a dynamic multi-graph generator is employed to create dynamic multi-graphs. Subsequently, a spatiotemporal dynamic multi-graph convolution module is used to learn spatiotemporal correlation features with spatial heterogeneity.
- The proposed prediction algorithm demonstrated its applicability to real-world scenarios by effectively integrating external factors such as holidays, weather, and the COVID-19 pandemic. Experimental results on real datasets showed that the LGDMGCN model exhibited superior performance in multi-step prediction, considering temporal and spatial heterogeneity factors. The model’s ability to adapt to varying external conditions and capture complex spatiotemporal dependencies makes it a valuable tool for optimizing intelligent transportation systems. Moreover, through methods like virtual station construction, the algorithm holds potential applicability to dockless bike-sharing and regional-level predictions, providing insights for broader intelligent transportation systems.
2. Related Works
2.1. Analysis and Prediction Methods for the BSS
2.2. Modeling Spatiotemporal Dependency Relationships
2.3. External Influencing Factors
3. Symbol Definitions and Problem Description
3.1. Symbols and Relevant Definitions
3.2. Problem Description
4. Local-Global Dynamic Multi-Graph Convolutional Neural Network Model
4.1. Local-Global Dynamic Spatiotemporal Graph Convolution
Algorithm 1. Local-global dynamic ST-GCN algorithm flow | |
Input: | : station distance, and POI |
Output: | : fused feature |
1. | , and POI |
2. | |
3. | Do |
4. | If station change Then |
5. | |
6. | End If |
6. | |
7. | |
8. | |
9. | |
10. | |
11. | |
End For | |
12. | Return fused feature |
4.1.1. Dynamic Multi-Graph Generation Module
- Multi-Graph Construction
- Distance Graph
- Historical Connectivity Graph
- POI Similarity Graph
- 2.
- Dynamic Multi-Graph Generation
4.1.2. Local Spatiotemporal Module
4.1.3. Global Spatiotemporal Module
4.1.4. Fusion Module
4.2. Temporal Attention Decoder
4.2.1. Temporal Attention Module
4.2.2. Multi-Source Feature Fusion
4.2.3. Gated Recurrent Unit (GRU)
5. Experiment
5.1. Multi-Source Data Acquisition and Preprocessing
5.2. Experiment Settings and Parameters
5.3. Evaluation Metrics
5.4. Experimental Results and Analysis
5.4.1. Comparative Experiment
- LSTM (Long Short-Term Memory): An improved RNN model commonly used for time series prediction [45].
- STGCN (Spatiotemporal Graph Convolutional Network): A spatiotemporal prediction model with a “gated TCN + GCN+ gated TCN” structure of spatiotemporal graph convolution modules. This model innovatively employs a combination of Graph Convolution and Gated Causal Convolution, eliminating the reliance on RNN for prediction. It is particularly effective in modeling relationships between stations based on the road network structure and has shown good prediction results in road speed prediction problems [40].
- DGCRN (Dynamic Graph Convolutional Recurrent Network): This model is based on an encoder-decoder structure, considering the impact of dynamics on traffic prediction, and defines the computation of dynamic graph convolution. It uses a hyper-network to dynamically generate an adjacency matrix at each step of the RNN, allowing the model to adaptively capture changing spatial relationships. The generated dynamic matrix is merged with the original road network matrix to provide a richer spatial representation [31].
- AttConvLSTM (Attention Convolutional LSTM): An encoder-decoder framework, utilizing convolutional and ConvLSTM units, is employed to identify complex features, capturing spatiotemporal characteristics and the impact of pick-up and drop-off interactions on citywide passenger demand. The model embeds an attention mechanism to depict the influence of underlying citywide travel patterns [46].
- STSeq2Seq (Spatiotemporal Sequence-to-Sequence): A model that captures dynamic relationships between time series data points by modeling non-local spatial correlations between traffic sections. It utilizes an encoder-decoder architecture where the encoder processes the input sequence and the decoder generates the output sequence. It also introduces an attention mechanism in the decoder to model dependencies between different time steps [38].
- The LSTM model, which solely considers temporal relationships between time series, lacks spatial correlations, resulting in overall inferior performance compared to models that incorporate spatiotemporal relationships.
- The AttConvLSTM model, by combining Convolutional and LSTM layers and incorporating an attention mechanism, demonstrates the effectiveness of modeling spatiotemporal dependencies and attention mechanisms for bike-sharing station prediction compared to LSTM.
- The STGCN model, which constructs stations into graphs and utilizes graph convolution for spatial modeling, performs well in capturing the spatial structure of bike-sharing stations. By employing TCN modules for temporal modeling, it achieves good performance in 4 h predictions. However, as STGCN is a single-step prediction model, its performance decreases significantly in multi-step predictions.
- Both the STSeq2Seq and DGCRN models capture the spatiotemporal characteristics of bike-sharing station demand through sequence-to-sequence model structures, exhibiting good performance in multi-step predictions. With increasing prediction steps, their performance remains relatively stable. Additionally, both models consider spatiotemporal dynamics. The STSeq2Seq model dynamically adjusts the weights of adjacency matrices in graph convolution based on the dynamic temporal information of input sequences, while the DGCRN model generates dynamic graphs through Hyper Network modules and node embedding features. Experimental results indicate that the STSeq2Seq model outperforms DGCRN, possibly because of its adoption of spatiotemporal graph convolution modules in the encoder, benefiting from the good performance of STGCN in single-step prediction, allowing it to better capture spatiotemporal correlations. It is also possible that the dynamic graph generation approach of the STSeq2Seq model is more suitable for bike-sharing station demand prediction than that of the DGCRN model.
- The LGDMGCN model exhibits excellent performance across all prediction time steps, with lower MAE and RMSE compared to other models. This indicates the effectiveness of the LGDMGCN model in multi-step bike-sharing demand prediction. Moreover, the LGDMGCN model demonstrates relative stability in performance across different prediction time steps, indicating good robustness. It effectively captures complex relationships between spatiotemporal data, thereby improving prediction accuracy and reliability. In addition to considering spatiotemporal correlations, the LGDMGCN model also accounts for spatiotemporal heterogeneity, resulting in further performance improvements compared to the STSeq2Seq model.
5.4.2. Ablation Experiment
5.4.3. Time Granularity Analysis
5.4.4. Spatial Correlation and Heterogeneity Effects Analysis
5.4.5. Analysis of Temporal Correlation and Heterogeneity Effects
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
Abbreviation | Full Term |
LGDMGCN | Local-Global Dynamic Multi-Graph Convolutional Neural Network |
COVID-19 | Coronavirus Disease 2019 |
POI | Points of Interest |
GRU | Gated Recurrent Unit |
BSS | Bike-Sharing System |
Global-local Dynamic ST-GCN | Global-Local Dynamic Spatiotemporal Graph Convolution Network |
TA | Temporal Attention |
TFF | Time Feature Fusion |
Local ST Block | Local Spatiotemporal Module |
Gated TCN | Gated Temporal Convolutions |
Dynamic GCN | Dynamic Graph Convolution |
LDGCN | Local Dynamic Graph Convolutional Neural |
LDST | Local Dynamic Spatiotemporal |
GDST | Global Spatiotemporal |
References
- Zi, W.; Xiong, W.; Chen, H.; Chen, L. TAGCN: Station-Level Demand Prediction for Bike-Sharing System via a Temporal Attention Graph Convolution Network. Inf. Sci. 2021, 561, 274–285. [Google Scholar] [CrossRef]
- Ma, X.; Yin, Y.; Jin, Y.; He, M.; Zhu, M. Short-Term Prediction of Bike-Sharing Demand Using Multi-Source Data: A Spatial-Temporal Graph Attentional LSTM Approach. Appl. Sci. 2022, 12, 1161. [Google Scholar] [CrossRef]
- Lin, L.; He, Z.; Peeta, S. Predicting Station-Level Hourly Demand in a Large-Scale Bike-Sharing Network: A Graph Convolutional Neural Network Approach. Transp. Res. Part C Emerg. Technol. 2018, 97, 258–276. [Google Scholar] [CrossRef]
- Yi, P.; Huang, F.; Peng, J. A Fine-Grained Graph-Based Spatiotemporal Network for Bike Flow Prediction in Bike-Sharing Systems. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), SIAM, Virtual, 29 April–1 May 2021; pp. 513–521. [Google Scholar]
- Chen, L.; Zhang, D.; Wang, L.; Yang, D.; Ma, X.; Li, S.; Wu, Z.; Pan, G.; Nguyen, T.-M.-T.; Jakubowicz, J. Dynamic Cluster-Based over-Demand Prediction in Bike Sharing Systems. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany, 12–16 September 2016; pp. 841–852. [Google Scholar]
- Gallop, C.; Tse, C.; Zhao, J. A Seasonal Autoregressive Model of Vancouver Bicycle Traffic Using Weather Variables. I-Manag. J. Civ. Eng. 2011, 1, 9. [Google Scholar] [CrossRef]
- Yoon, J.W.; Pinelli, F.; Calabrese, F. Cityride: A Predictive Bike Sharing Journey Advisor. In Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management, Bengaluru, India, 23–26 July 2012; IEEE: New York, NY, USA, 2012; pp. 306–311. [Google Scholar]
- Zheng, Z.; Zhou, Y.; Sun, L. A Multiple Factor Bike Usage Prediction Model in Bike-Sharing System. In Proceedings of the Green, Pervasive, and Cloud Computing: 13th International Conference, GPC 2018, Hangzhou, China, 11–13 May 2018; Revised Selected Papers 13. Springer: Berlin/Heidelberg, Germany, 2019; pp. 390–405. [Google Scholar]
- Chen, C.; Ma, J.; Susilo, Y.; Liu, Y.; Wang, M. The Promises of Big Data and Small Data for Travel Behavior (Aka Human Mobility) Analysis. Transp. Res. Part C Emerg. Technol. 2016, 68, 285–299. [Google Scholar] [CrossRef]
- Yang, Z.; Hu, J.; Shu, Y.; Cheng, P.; Chen, J.; Moscibroda, T. Mobility Modeling and Prediction in Bike-Sharing Systems. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services, Singapore, 26–30 June 2016; pp. 165–178. [Google Scholar]
- Li, Y.; Zheng, Y.; Zhang, H.; Chen, L. Traffic Prediction in a Bike-Sharing System. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, USA, 3–6 November 2015; pp. 1–10. [Google Scholar]
- Froehlich, J.E.; Neumann, J.; Oliver, N. Sensing and Predicting the Pulse of the City through Shared Bicycling. In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, Pasadena, CA, USA, 11–17 July 2009. [Google Scholar]
- Bao, J.; Yu, H.; Wu, J. Short-Term FFBS Demand Prediction with Multi-Source Data in a Hybrid Deep Learning Framework. IET Intell. Transp. Syst. 2019, 13, 1340–1347. [Google Scholar] [CrossRef]
- Feng, S.; Chen, H.; Du, C.; Li, J.; Jing, N. A Hierarchical Demand Prediction Method with Station Clustering for Bike Sharing System. In Proceedings of the 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), Guangzhou, China, 18–21 June 2018; IEEE: New York, NY, USA, 2018; pp. 829–836. [Google Scholar]
- Erman, J.; Arlitt, M.; Mahanti, A. Traffic Classification Using Clustering Algorithms. In Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, Pisa, Italy, 15 September 2006; pp. 281–286. [Google Scholar]
- Gu, J.; Zhou, Q.; Yang, J.; Liu, Y.; Zhuang, F.; Zhao, Y.; Xiong, H. Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems. IEEE Trans. Knowl. Data Eng. 2020, 34, 640–652. [Google Scholar] [CrossRef]
- Blondel, V.D.; Guillaume, J.-L.; Lambiotte, R.; Lefebvre, E. Fast Unfolding of Communities in Large Networks. J. Stat. Mech. Theory Exp. 2008, 2008, P10008. [Google Scholar] [CrossRef]
- Mehdizadeh Dastjerdi, A.; Morency, C. Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning. Sensors 2022, 22, 1060. [Google Scholar] [CrossRef]
- Chai, D.; Wang, L.; Yang, Q. Bike Flow Prediction with Multi-Graph Convolutional Networks. In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, USA, 6–9 November 2018; pp. 397–400. [Google Scholar]
- Lee, S.-H.; Ku, H.-C. A Dual Attention-Based Recurrent Neural Network for Short-Term Bike Sharing Usage Demand Prediction. IEEE Trans. Intell. Transp. Syst. 2022, 24, 4621–4630. [Google Scholar] [CrossRef]
- Chen, P.-C.; Hsieh, H.-Y.; Su, K.-W.; Sigalingging, X.K.; Chen, Y.-R.; Leu, J.-S. Predicting Station Level Demand in a Bike-Sharing System Using Recurrent Neural Networks. IET Intell. Transp. Syst. 2020, 14, 554–561. [Google Scholar] [CrossRef]
- Wang, B.; Kim, I. Short-Term Prediction for Bike-Sharing Service Using Machine Learning. Transp. Res. Procedia 2018, 34, 171–178. [Google Scholar] [CrossRef]
- Zhang, C.; Zhang, L.; Liu, Y.; Yang, X. Short-Term Prediction of Bike-Sharing Usage Considering Public Transport: A LSTM Approach. In Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4–7 November 2018; IEEE: New York, NY, USA, 2018; pp. 1564–1571. [Google Scholar]
- Pan, Y.; Zheng, R.C.; Zhang, J.; Yao, X. Predicting Bike Sharing Demand Using Recurrent Neural Networks. Procedia Comput. Sci. 2019, 147, 562–566. [Google Scholar] [CrossRef]
- Yang, H.; Xie, K.; Ozbay, K.; Ma, Y.; Wang, Z. Use of Deep Learning to Predict Daily Usage of Bike Sharing Systems. Transp. Res. Rec. 2018, 2672, 92–102. [Google Scholar] [CrossRef]
- Li, X.; Xu, Y.; Chen, Q.; Wang, L.; Zhang, X.; Shi, W. Short-Term Forecast of Bicycle Usage in Bike Sharing Systems: A Spatial-Temporal Memory Network. IEEE Trans. Intell. Transp. Syst. 2021, 23, 10923–10934. [Google Scholar] [CrossRef]
- Guo, R.; Jiang, Z.; Huang, J.; Tao, J.; Wang, C.; Li, J.; Chen, L. BikeNet: Accurate Bike Demand Prediction Using Graph Neural Networks for Station Rebalancing. In Proceedings of the 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, UK, 19–23 August 2019; IEEE: New York, NY, USA, 2019; pp. 686–693. [Google Scholar]
- Lv, M.; Hong, Z.; Chen, L.; Chen, T.; Zhu, T.; Ji, S. Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction. IEEE Trans. Intell. Transport. Syst. 2021, 22, 3337–3348. [Google Scholar] [CrossRef]
- Liang, Y.; Ke, S.; Zhang, J.; Yi, X.; Zheng, Y. GeoMAN: Multi-Level Attention Networks for Geo-Sensory Time Series Prediction. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence; International Joint Conferences on Artificial Intelligence Organization: Stockholm, Sweden, July 2018; pp. 3428–3434. [Google Scholar]
- Liu, L.; Qiu, Z.; Li, G.; Wang, Q.; Ouyang, W.; Lin, L. Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction. IEEE Trans. Intell. Transp. Syst. 2019, 20, 3875–3887. [Google Scholar] [CrossRef]
- Li, F.; Feng, J.; Yan, H.; Jin, G.; Jin, D.; Li, Y. Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution. arXiv 2021, arXiv:2104.14917. [Google Scholar] [CrossRef]
- Liu, L.; Zhen, J.; Li, G.; Zhan, G.; He, Z.; Du, B.; Lin, L. Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction. IEEE Trans. Intell. Transp. Syst. 2021, 22, 7169–7183. [Google Scholar] [CrossRef]
- Pan, Z.; Liang, Y.; Wang, W.; Yu, Y.; Zheng, Y.; Zhang, J. Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 4–8 August 2019; ACM: Anchorage AK USA, 2019; pp. 1720–1730. [Google Scholar]
- Wang, Z.; Jiang, R.; Xue, H.; Salim, F.D.; Song, X.; Shibasaki, R. Event-Aware Multimodal Mobility Nowcasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 22 February–1 March 2022. [Google Scholar]
- Essien, A.; Petrounias, I.; Sampaio, P.; Sampaio, S. A Deep-Learning Model for Urban Traffic Flow Prediction with Traffic Events Mined from Twitter. World Wide Web 2021, 24, 1345–1368. [Google Scholar] [CrossRef]
- Jiao, F.; Huang, L.; Song, R.; Huang, H. An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic. Sensors 2021, 21, 5950. [Google Scholar] [CrossRef] [PubMed]
- Tu, Q.; Zhang, Q.; Zhang, Z.J.; Gong, D.; Tang, M.C. A Deep Spatio-Temporal Fuzzy Neural Network for Subway Passenger Flow Prediction with COVID-19 Search Engine Data. IEEE Trans. Fuzzy Syst. 2022, 31, 394–406. [Google Scholar] [CrossRef]
- Wang, X.; Guan, X.; Cao, J.; Zhang, N.; Wu, H. Forecast Network-Wide Traffic States for Multiple Steps Ahead: A Deep Learning Approach Considering Dynamic Non-Local Spatial Correlation and Non-Stationary Temporal Dependency. Transp. Res. Part C: Emerg. Technol. 2020, 119, 102763. [Google Scholar] [CrossRef]
- Xu, H.; Zou, T.; Liu, M.; Qiao, Y.; Wang, J.; Li, X. Adaptive Spatiotemporal Dependence Learning for Multi-Mode Transportation Demand Prediction. IEEE Trans. Intell. Transp. Syst. 2022, 23, 18632–18642. [Google Scholar] [CrossRef]
- Yu, B.; Yin, H.; Zhu, Z. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. arXiv 2017, arXiv:1709.04875. [Google Scholar]
- Sparck Jones, K. A Statistical Interpretation of Term Specificity and Its Application in Retrieval. J. Doc. 1972, 28, 11–21. [Google Scholar] [CrossRef]
- Kipf, T.N.; Welling, M. Semi-Supervised Classification with Graph Convolutional Networks. arXiv 2016, arXiv:1609.02907. [Google Scholar]
- Defferrard, M.; Bresson, X.; Vandergheynst, P. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. Adv. Neural Inf. Process. Syst. 2016, 29, 3844–3852. [Google Scholar]
- Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv 2014, arXiv:1412.3555. [Google Scholar]
- Tian, Y.; Pan, L. Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network. In Proceedings of the 2015 IEEE International Conference on Smart city/SocialCom/SustainCom (SmartCity), Chengdu, China, 19–21 December 2015; IEEE: New York, NY, USA, 2015; pp. 153–158. [Google Scholar]
- Zhou, X.; Shen, Y.; Zhu, Y.; Huang, L. Predicting Multi-Step Citywide Passenger Demands Using Attention-Based Neural Networks. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Los Angeles, CA, USA, 5–9 February 2018; pp. 736–744. [Google Scholar]
Symbol | Definition |
---|---|
Time step | |
Total time length | |
Total number of days | |
Total number of stations | |
Static graph | |
Adjacency matrix | |
Static multi-graph | |
Dynamic multi-graph | |
Bike-sharing flow | |
Daily confirmed COVID-19 cases | |
Time features for each day | |
Weather conditions for each time period | |
Historical time steps | |
Future time steps to be predicted | |
Distance threshold | |
Number of features |
Pred_Step | 4 h | 8 h | 12 h | 16 h | ||||
---|---|---|---|---|---|---|---|---|
Model | MAE | RMSE | MAE | RMSE | MAE | RMSE | MAE | RMSE |
LSTM | 0.4099 | 0.4525 | 0.4141 | 0.5410 | 0.4534 | 0.5983 | 0.5167 | 0.7246 |
STGCN | 0.0380 | 0.2342 | 0.0506 | 0.2564 | 0.0560 | 0.2675 | 0.0765 | 0.2760 |
DGCRN | 0.0915 | 0.2679 | 0.0951 | 0.3322 | 0.0976 | 0.3520 | 0.0985 | 0.3718 |
AttConvLSTM | 0.0959 | 0.2548 | 0.1016 | 0.2682 | 0.1044 | 0.2690 | 0.1069 | 0.2702 |
STSeq2Seq | 0.0398 | 0.2474 | 0.0408 | 0.2629 | 0.0432 | 0.2686 | 0.0498 | 0.2761 |
LGDMGCN | 0.0350 | 0.2476 | 0.0367 | 0.2628 | 0.0370 | 0.2629 | 0.0371 | 0.2679 |
Model | MAE (16 h) | RMSE (16 h) |
---|---|---|
LGDMGCN w/o At | 0.0483 | 0.2896 |
LGDMGCN-Global | 0.0373 | 0.2957 |
LGDMGCN-Local | 0.0395 | 0.2841 |
LGDMGCN w/o Attn | 0.0410 | 0.2754 |
LGDMGCN w/o Ext | 0.0378 | 0.2849 |
LGDMGCN | 0.0371 | 0.2679 |
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Chen, J.; Huang, R. Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction. Algorithms 2024, 17, 384. https://doi.org/10.3390/a17090384
Chen J, Huang R. Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction. Algorithms. 2024; 17(9):384. https://doi.org/10.3390/a17090384
Chicago/Turabian StyleChen, Juan, and Rui Huang. 2024. "Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction" Algorithms 17, no. 9: 384. https://doi.org/10.3390/a17090384