A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

Xiang Fei, Chung-Cheng Lu*, Ke Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

167 Scopus citations

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.

Original languageEnglish
Pages (from-to)1306-1318
Number of pages13
JournalTransportation Research Part C: Emerging Technologies
Volume19
Issue number6
DOIs
StatePublished - 1 Jan 2011

Keywords

  • Adaptive control
  • Advanced Traveler Information Systems
  • Bayesian inference
  • Prediction confidence intervals
  • Real-time travel time prediction

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