01744764

Component

https://doi.org/10.1177/0361198120930221

For almost a century, several models have been developed to calibrate the pairwise relationship between traffic flow variables, that is, speed, density, and flow. Multi-regime models are well known for being superior over single-regime models in fitting the speed–density relationship. However, in modeling multi-regime models, breakpoints that separate the regimes are visually established based on the subjective judgment of data characteristics. Thus, this study proposes a data-driven approach to estimate the breakpoints of multi-regime models. It applies the Bayesian model for calibrating multi-regime models (two and three-regime models) for fitting traffic flow fundamental diagram. Furthermore, the analysis presented accounts for the random characteristics associated with the flow. To demonstrate the application of the proposed algorithm, traffic flow data from Interstate 10 (I-10) freeway in Jacksonville, Florida, were used in the analysis. The results demonstrate the potential benefit of using the proposed model in calibrating the fundamental diagram. The proposed approach can also quantify uncertainty and encode prior knowledge about the breakpoints in the model if the model developer wishes.

© National Academy of Sciences: Transportation Research Board 2020.

English

pp 561-574

2020-9

Transportation Research Record: Journal of the Transportation Research Board

Web

References (45)

Algorithms; Bayes' theorem; Calibration; Freeways; Traffic density; Traffic flow; Traffic speed

Jacksonville (Florida)

Highways; Operations and Traffic Management; Planning and Forecasting

TRIS, TRB, ATRI

Jul 1 2020 3:04PM