Version 1
: Received: 26 February 2024 / Approved: 27 February 2024 / Online: 27 February 2024 (12:33:19 CET)
How to cite:
Raufi, P.; maniat, M. Novel Calibration Approach for Freeway Traffic Models in Single and Multi-Regimes Fundamental Diagram. Preprints2024, 2024021540. https://doi.org/10.20944/preprints202402.1540.v1
Raufi, P.; maniat, M. Novel Calibration Approach for Freeway Traffic Models in Single and Multi-Regimes Fundamental Diagram. Preprints 2024, 2024021540. https://doi.org/10.20944/preprints202402.1540.v1
Raufi, P.; maniat, M. Novel Calibration Approach for Freeway Traffic Models in Single and Multi-Regimes Fundamental Diagram. Preprints2024, 2024021540. https://doi.org/10.20944/preprints202402.1540.v1
APA Style
Raufi, P., & maniat, M. (2024). Novel Calibration Approach for Freeway Traffic Models in Single and Multi-Regimes Fundamental Diagram. Preprints. https://doi.org/10.20944/preprints202402.1540.v1
Chicago/Turabian Style
Raufi, P. and mohammad maniat. 2024 "Novel Calibration Approach for Freeway Traffic Models in Single and Multi-Regimes Fundamental Diagram" Preprints. https://doi.org/10.20944/preprints202402.1540.v1
Abstract
The speed-density or flow-density relationship is a fundamental aspect of traffic flow theory. Single-regime models often face challenges in consistently fitting empirical data under diverse traffic conditions. This paper addresses the inaccuracies of single-regime models, attributing them not only to their functional forms but also to data dispersion and sample selection bias. To mitigate these issues, the data is segmented into day and night periods, leading to the creation of separate models for each. Calibration results, conducted on data from two freeways, Tehran-Karaj and Tehran-Qom, reveal distinct challenges, with the former exhibiting high dispersion and the latter showing low dispersion. Surprisingly, single-regime models demonstrate notable effectiveness when applied to distinct day and night data. Further analysis, categorizing data into day and night, results in reduced errors (20-60% improvement in R-Square), emphasizing the potential for enhanced accuracy by considering distinct regimes. Overall, the study underscores the significance of regime-specific considerations for accurate traffic flow modeling. Upon thorough evaluation, the study highlights the superiority of the neural network Multi-layer Perceptron (MLP) over traditional models. The research discusses the implications of incorporating day and night parameters into the models, emphasizing their potential for accuracy enhancement.
Engineering, Transportation Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
