B
Billy M. Williams
Researcher at North Carolina State University
Publications - 71
Citations - 4894
Billy M. Williams is an academic researcher from North Carolina State University. The author has contributed to research in topics: Traffic flow & Autoregressive integrated moving average. The author has an hindex of 21, co-authored 70 publications receiving 3869 citations. Previous affiliations of Billy M. Williams include Korea Transport Institute & University of Nebraska–Lincoln.
Papers
More filters
Journal ArticleDOI
Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results
Billy M. Williams,Lester A Hoel +1 more
TL;DR: The theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes as well as empirical results using actual intelligent transportation system data are presented and found to be consistent with the theoretical hypothesis.
Journal ArticleDOI
Comparison of parametric and nonparametric models for traffic flow forecasting
TL;DR: This research effort seeks to examine the theoretical foundation of nonparametric regression and to answer the question of whether non parametric regression based on heuristically improved forecast generation methods approach the single interval traffic flow prediction performance of seasonal ARIMA models.
Journal ArticleDOI
Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification
TL;DR: Empirical comparisons using real world traffic flow data aggregated at 15-min interval showed that the adaptive Kalman filter approach can generate workable level forecasts and prediction intervals and demonstrates improved adaptability when traffic is highly volatile.
Journal ArticleDOI
Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models
TL;DR: In this paper, the application of seasonal time series models to the single-interval traffic flow forecasting problem for urban freeways is addressed and the best-fit Winters exponential smoothing models are also developed for each site.
Journal ArticleDOI
Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling
TL;DR: The results indicate that ARIMAX models provide improved forecast performance over univariate forecast models, but further research is needed to investigate model extensions and refinements to provide a generalizable, self-tuning multivariate forecasting model that is easily implemented and that effectively models varying upstream to downstream correlations.