Modeling Sustainable Traffic Behavior: Avoiding Congestion at a Stationary Bottleneck
Auteur(s): |
Imran Badshah
Zawar H. Khan T. Aaron Gulliver Khurram S. Khattak Syed Saad |
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Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Civil Engineering Journal, 1 novembre 2022, n. 11, v. 8 |
Page(s): | 2378-2390 |
DOI: | 10.28991/cej-2022-08-11-02 |
Abstrait: |
Sustainable traffic behaviour is increasing in importance as traffic volume rises due to population growth. In this paper, a model for traffic flow at a stationary bottleneck is developed to determine the parameters that cause congestion. Towards this goal, traffic density, speed, and delay were acquired during peak and off-peak periods in the morning and afternoon at a stationary bottleneck in Peshawar, KPK, Pakistan. The morning and afternoon peak periods have high densities, low speeds, and considerable delays. Regression models are developed using this data. These results indicate that there is a linear relationship between density and time at the stationary bottleneck and a negative linear relationship between density and speed. Thus, an increase in density increases the time delay and reduces the speed. I comprehensive traffic delay model is characterized by a stationary bottleneck. The Kolmogorov-Smirnov (KS) test and P-values were used to identify the best-fit distribution for speed and density. The binomial and generalized extreme values are considered the best fits for density and speed. The results presented can be used to develop accurate simulation models for stationary bottlenecks to reduce congestion. |
Copyright: | © 2022 Imran Badshah, Zawar H. Khan, T. Aaron Gulliver, Khurram S. Khattak, Syed Saad |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
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10702871 - Publié(e) le:
10.12.2022 - Modifié(e) le:
15.02.2023