01026221

Component

https://doi.org/10.3141/1965-03

http://scholar.google.com...mert&publication_year=2006

Accurate short-term prediction of traffic parameters is a critical component for many intelligent transportation system applications. Traffic flow is subject to abrupt disturbances because of various unexpected events (e.g., accidents, weather-induced disruption) that may change the underlying dynamics and the stability of the data generation process. Short-term prediction models that do not account for these changes produce biased and less accurate predictions. This paper proposes a new adaptive approach to short-term prediction that explicitly accounts for occasional regime changes by using statistical change-point detection algorithms. In this context, the expectation maximization and the CUSUM (cumulative sum) algorithms are implemented to detect shifts in the mean level of the process in real time. Autoregressive integrated moving average models are used for developing the forecasting models while the process mean is monitored by the two detection algorithms. The intercept of the forecasting models is updated on the basis of the detected shifts in the mean level to adapt to any potential new regimes. The proposed approach is tested on real-world loop data sets. The results show significant improvements in prediction accuracy compared with traditional autoregressive integrated moving average models with fixed parameters.

01037212

English

pp 23-31

2006

Transportation Research Record: Journal of the Transportation Research Board

0309099749

Print

Figures (3) ; References (34) ; Tables (3)

Accuracy; Mathematical models; Traffic flow; Traffic forecasting

Change point detection algorithms; Regime switching; Short-term prediction

Highways; Operations and Traffic Management; I71: Traffic Theory

TRIS, TRB

Mar 3 2006 10:16AM

• Continuum Traffic Model for Freeway with On-and Off-Ramp to Explain Different Traffic-Congested States
• Effect of Time Gaps and Lane Flow Distributions on Freeway Bottleneck Capacity
• Empirics of Multianticipative Car-Following Behavior
• Equilibrium of Lane Flow Distribution on Motorways
• Estimation of Driver Reaction Time from Car-Following Data: Application in Evaluation of General Motor–Type Model
• Estimation of Traffic Dynamics Models with Machine-Learning Methods
• Failure Detection and Diagnosis in Microsimulation Traffic Models
• Feedback Route Guidance Applied to a Large-Scale Express Ring Road
• Impact of Differences in Driver-Desired Speed on Steady-State Traffic Stream Behavior
• Impact of Lane-Change Maneuvers on Congested Freeway Segment Delays: Pilot Study
• Improvement and Evaluation of Cell-Transmission Model for Operational Analysis of Traffic Networks: Freeway Case Study
• Interdriver Differences in Car-Following: A Vehicle Trajectory–Based Study
• Modeling and Simulation of Unconventional Traffic Circles
• Piecewise-Linearized Cell Transmission Model and Parameter Calibration Methodology
• Point Constriction, Interface, and Boundary Conditions for Kinematic-Wave Model
• Preprocessing Volume Input Data for Improved Traffic Simulation
• Probabilistic Breakdown Phenomenon at On-Ramp Bottlenecks in Three-Phase Traffic Theory
• Reproducibility of Flow-Density Relations in Heavily Congested Flow Based on Individual Vehicle Data
• Simple Traffic Model for a Simple Problem: Sizing Travel Time Measurement Devices
• Time-Dependent Analysis of Density Fluctuations and Breakdown Thresholds on Congested Freeways
• Toward Demonstrating Predictability of Bottleneck Activation on German Autobahns