• Corpus ID: 204769452

Traffic Speed Forecasting Considering Multiple Periodicities and Complex Patterns

@inproceedings{Chen2016TrafficSF,
  title={Traffic Speed Forecasting Considering Multiple Periodicities and Complex Patterns},
  author={A. Chen and Hsing-Kuo Kenneth Pao},
  year={2016}
}
Intelligent Transportation Systems (ITS) has been developed to aid drivers and other road-users to make a better travel decision. In recent years, many research efforts have been devoted in this field. Being one kind of time-series data, we can analyze the traffic data following the general aspects of studying time-series, which contains the analysis of periodicity of many kinds. This work highlights the study on the (long-term) multiple periodicities that could be found in traffic data while… 

References

SHOWING 1-10 OF 19 REFERENCES
Prediction of Road Traffic using a Neural Network Approach
  • R. Yasdi
  • Computer Science, Business
    Neural Computing & Applications
  • 1999
TLDR
Recurrent Jordan networks, popular in the modelling of time series, is examined in this study and results demonstrate that learning with this type of architecture has a good generalisation ability.
Efficient traffic speed forecasting based on massive heterogenous historical data
TLDR
This work aims at using a Hadoop framework for the prediction given heterogeneous data including traffic data such as speed, flow, occupancy, and weather data and integrates the kNN and Gaussian process regression for efficient and robust traffic speed prediction.
Short-term traffic volume prediction using classification and regression trees
TLDR
A novel nonparametric-model-based method to predict the short-term traffic volume using the CART model, a classification and regression trees model, which outperforms the k-NN and Kalman filter methods in both the mean absolute percentage error and the meanabsolute scaled error.
Predicting traffic speed in urban transportation subnetworks for multiple horizons
TLDR
This study develops various matrix and tensor based models that can simultaneously forecast traffic conditions for multiple road segments and prediction-horizons and analyzes the performance of these models by performing multi-horizon prediction for an urban subnetwork in Singapore.
Urban traffic flow prediction using a fuzzy-neural approach
Real-Time Short-Term Traffic Speed Level Forecasting and Uncertainty Quantification Using Layered Kalman Filters
TLDR
Results based on real-world station-by-station traffic speed data showed that the proposed online algorithm can generate workable short-term traffic speed level forecasts and associated prediction confidence intervals.
Short-time traffic flow prediction with ARIMA-GARCH model
TLDR
The results show that the introduction of conditional heteroscedasticity cannot bring satisfactory improvement to prediction accuracy, in some cases the general GARCH(1,1) model may even deteriorate the performance.
Short-Term Prediction of Traffic Volume in Urban Arterials
TLDR
The Box-Jenkins autoregressive integrated moving average (ARIMA) model of order (0, 1, 1) turned out to be the most adequate model in reproducing all original time series in forecasting traffic volume in urban arterials.
...
1
2
...