Journal ArticleDOI
A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction
Xiang Fei,Chung-Cheng Lu,Ke Liu +2 more
TLDR
The experiment results suggest that the proposed Bayesian inference-based dynamic linear model to predict online short-term travel time on a freeway stretch is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.Abstract:
This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.read more
Citations
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Journal ArticleDOI
LSTM network: a deep learning approach for short-term traffic forecast
TL;DR: A novel traffic forecast model based on long short-term memory (LSTM) network is proposed, which considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units.
Journal ArticleDOI
Short-term traffic forecasting: Where we are and where we’re going
TL;DR: In this article, the authors present a review of the existing literature on short-term traffic forecasting and offer suggestions for future work, focusing on 10 challenging, yet relatively under researched, directions.
Journal ArticleDOI
Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach
TL;DR: This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations and the FCL-Net achieves the better predictive performance than traditional approaches.
Journal ArticleDOI
A gradient boosting method to improve travel time prediction
Yanru Zhang,Ali Haghani +1 more
TL;DR: The gradient boosting tree method strategically combines additional trees by correcting mistakes made by its previous base models, therefore, potentially improves prediction accuracy and model interpretability in freeway travel time prediction.
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.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Book
Bayesian Forecasting and Dynamic Models
Mike West,Jeff Harrison +1 more
TL;DR: In this article, the authors propose a model called the Dynamic Regression Model (DRM) which is an extension of the First-Order Polynomial Model (FOPM) and the Dynamic Linear Model (DLM).
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
Real-time freeway traffic state estimation based on extended Kalman filter: a general approach
Yibing Wang,Markos Papageorgiou +1 more
TL;DR: A general approach to the real-time estimation of the complete traffic state in freeway stretches is developed based on the extended Kalman filter, based on which a traffic state estimator is designed by use of the extended-Kalman-filtering method.
Journal ArticleDOI
A multivariate state space approach for urban traffic flow modeling and prediction
TL;DR: Using 3-min volume measurements from urban arterial streets near downtown Athens, models were developed that feed on data from upstream detectors to improve on the predictions of downstream locations and it appears that the use of multivariate state space models improves on the prediction accuracy over univariate time series ones.