the paper "Quantifying Uncertainty in Short-term Traffic Prediction and its Application to Optimal Staffing Plan Development" submitted to Transportation Research Record Part C: Emerging Technologies.
Model:
pso.m - particle swarm optimization algorithm
elm_pi.m - extreme learning machine algorithm to provide prediction intervals
pso_elm.m - optimize elm parameters with pso for interval predictions
Improved_pso_elm.m - on-line version of pso-elm model
ARMA.R - Simple ARMA model
KalmanFilter.R - Kalman Filter model
spectral_arima_gjr_garch.R - model based on paper "Zhang, Y., Zhang, Y., Haghani, A., 2014. A hybrid short-term traffic flow forecasting meth-od based on spectral analysis and statistical volatility model."
Adaptive_kf.R - model based on paper "Guo, J., Huang, W., Williams, B.M., 2014. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification"
adaptivekalmanfilter_arma_memory.py - adaptive kalman filter of ARMA component for sensitivity analysis
adaptivekalmanfilter_garch_memory.py - adaptive kalman filter of GARCH component for sensitivity analysis
Results:
ARMA.csv - results using ARMA model
Zhang_2014.csv - results based on paper "Zhang, Y., Zhang, Y., Haghani, A., 2014. A hybrid short-term traffic flow forecasting meth-od based on spectral analysis and statistical volatility model."
Kalman.csv - results using Kalman Filter model
Guo_2014.csv - results based on paper "Guo, J., Huang, W., Williams, B.M., 2014. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification"
PSO_ELM.csv - results using PSO-ELM model
Improved_PSO_ELM.csv - results using Improved PSO-ELM model