BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data
The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into acc...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2023/8962283 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849308564243349504 |
|---|---|
| author | Abdullah Alourani Farzeen Ashfaq N. Z. Jhanjhi Navid Ali Khan |
| author_facet | Abdullah Alourani Farzeen Ashfaq N. Z. Jhanjhi Navid Ali Khan |
| author_sort | Abdullah Alourani |
| collection | DOAJ |
| description | The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road. |
| format | Article |
| id | doaj-art-043924aa18f64ecb99a2eac0eb35fc40 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-043924aa18f64ecb99a2eac0eb35fc402025-08-20T03:54:25ZengWileyJournal of Advanced Transportation2042-31952023-01-01202310.1155/2023/8962283BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather DataAbdullah Alourani0Farzeen Ashfaq1N. Z. Jhanjhi2Navid Ali Khan3Department of Computer Science and InformationSchool of Computer ScienceSchool of Computer ScienceSchool of Computer ScienceThe timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road.http://dx.doi.org/10.1155/2023/8962283 |
| spellingShingle | Abdullah Alourani Farzeen Ashfaq N. Z. Jhanjhi Navid Ali Khan BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data Journal of Advanced Transportation |
| title | BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data |
| title_full | BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data |
| title_fullStr | BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data |
| title_full_unstemmed | BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data |
| title_short | BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data |
| title_sort | bilstm and gnn based spatiotemporal traffic flow forecasting with correlated weather data |
| url | http://dx.doi.org/10.1155/2023/8962283 |
| work_keys_str_mv | AT abdullahalourani bilstmandgnnbasedspatiotemporaltrafficflowforecastingwithcorrelatedweatherdata AT farzeenashfaq bilstmandgnnbasedspatiotemporaltrafficflowforecastingwithcorrelatedweatherdata AT nzjhanjhi bilstmandgnnbasedspatiotemporaltrafficflowforecastingwithcorrelatedweatherdata AT navidalikhan bilstmandgnnbasedspatiotemporaltrafficflowforecastingwithcorrelatedweatherdata |