Few-shot machine learning attempts to predict outputs given only a very small number of training examples. The key idea behind most few-shot learning approaches is to pre-train the model with a large number of instances from a different but related class of data, classes for which a large number of instances are available for training. Few-shot learning has been most successfully demonstrated for classification problems using Siamese deep learning neural networks. Few-shot learning is less extensively applied to time-series forecasting. Few-shot forecasting is the task of predicting future values of a time-series even when only a small set of historic time-series is available. Few-shot forecasting has applications in domains where a long history of data is not available. This work describes deep neural network architectures for few-shot forecasting. All the architectures use a Siamese twin network approach to learn a difference function between pairs of time-series, rather than directly forecasting based on historical data as seen in traditional forecasting models. The networks are built using Long short-term memory units (LSTM). During forecasting, a model is able to forecast time-series types that were never seen in the training data by using the few available instances of the new time-series type as reference inputs. The proposed architectures are evaluated on Vehicular traffic data collected in California from the Caltrans Performance Measurement System (PeMS). The models were trained with traffic flow data collected at specific locations and then are evaluated by predicting traffic at different locations at different time horizons (0 to 12 hours). The Mean Absolute Error (MAE) was used as the evaluation metric and also as the loss function for training. The proposed architectures show lower prediction error than a baseline nearest neighbor forecast model. The prediction error increases at longer time horizons.
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Traffic Volume Prediction with Automated Signal Performance Measures (ATSPM) Data
Predicting short-term traffic volume is essential to improve transportation systems management and operations (TSMO) and the overall efficiency of traffic networks. The
real-time data, collected from Internet of Things (IoT) devices, can be used to predict traffic volume. More specifically, the Automated Traffic Signal Performance Measures (ATSPM) data
contain high-fidelity traffic data at multiple intersections and can reveal the spatio-temporal patterns of traffic volume for each signal. In this study, we have developed a machine learningbased approach using the data collected from ATSPM sensors of a corridor in Orlando, FL to predict future hourly traffic. The hourly predictions are calculated based on the previous six hours volume seen at the selected intersections. Additional factors that play an important role in traffic fluctuations include peak hours, day of the week, holidays, among others. Multiple machine learning models are applied to the dataset to determine the model with the best performance. Random Forest, XGBoost, and LSTM models show the best performance in predicting hourly traffic volumes.
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- Award ID(s):
- 1852002
- NSF-PAR ID:
- 10410229
- Date Published:
- Journal Name:
- IEEE Symposium on Computers and Communications (ISCC)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISCC55528.2022.9912469
