Real-Time Prediction of Near-Future Traffic States on Freeways Using a Markov Model
Bibliographic record
Abstract
A method is proposed to predict the state of traffic for the near future. Traffic conditions are assumed to follow a stochastic process. A Markov model is developed to characterize the transition between traffic states. Unlike previous models in the literature, the state transition probability matrix is assumed to be a function of traffic variables; therefore, the proposed Markov model considers time-varying covariates. The base transition matrix and the effect of each covariate are calibrated to a data set for an urban expressway in Toronto, Ontario, Canada, by using maximum likelihood estimation. Using the transition probabilities of the Markov model, the proposed procedure constructs the empirical distribution of travel speed. The procedure, which can be applied in real time, uses both the empirical distribution of travel speed for different traffic conditions and the predicted transition matrix for the near future. Therefore, the proposed method enables the prediction of both the expected speed value and its distribution for the near future. Finally, a procedure is proposed to improve the prediction results of any travel time prediction method. This procedure uses a short-memory time series model by incorporating the predicted transition probabilities of the proposed Markov model. An evaluation using field data demonstrates this improvement for a simple time series model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".