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Traffic forecasting plays an important role in urban planning. Deep learning methods outperform traditional traffic flow forecasting models because of their ability to capture spatiotemporal characteristics of traffic conditions. However, these methods require high-quality historical traffic data, which can be both difficult to acquire and non-comprehensive, making it hard to predict traffic flows at the city scale. To resolve this problem, we implemented a deep learning method, SceneGCN, to forecast traffic speed at the city scale. The model involves two steps: firstly, scene features are extracted from Google Street View (GSV) images for each road segment using pretrained Resnet18 models. Then, the extracted features are entered into a graph convolutional neural network to predict traffic speed at different hours of the day. Our results show that the accuracy of the model can reach up to 86.5% and the Resnet18 model pretrained by Places365 is the best choice to extract scene features for traffic forecasting tasks. Finally, we conclude that the proposed model can predict traffic speed efficiently at the city scale and GSV images have the potential to capture information about human activities.
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LOCAL IOT WIRELESS NETWORK FOR ROAD SURFACE TEMPERATURE MODELING AND FORECASTING USING DEEP LEARNING METHODS
Winter road safety procedures are crucial for maintaining safe operating conditions and daily transportation activities without impedance or risk to the population. Typically, road surface salting mitigates ice build-up; however, road surface temperature (RST) forecasting with mathematical models performs poorly where the geographic location and climate cannot be generalized or described models trained with data from sensors in unrepresentative geographic locations. Additionally, modeling interactions among meteorological, geographical, and physical road characteristics can prove challenging. This study proposes using deep neural networks to model the nonlinear interactions of the above features, thereby creating a better model for forecasting RST by up to twelve hours into the future.
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- Award ID(s):
- 1914751
- PAR ID:
- 10332219
- Date Published:
- Journal Name:
- Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition IMECE2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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