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Pedestrian Self-organization Modeling with Behavioral Variables at a Signalized Intersection
https://orcid.org/http://orcid.org/0000-0001-5137-3673
https://orcid.org/http://orcid.org/0000-0003-1615-9553
This paper outlines pedestrians' movement at a signalized intersection of Tehran, Iran's capital. The presented model considers the effects of specific behavior variables derived from Iranian culture and local laws. The social force model has been built and calibrated by considering the crosswalk's geometric and heterogeneous (based on gender) conditions by implementing a genetic algorithm. A fundamental diagram has been used to validate the calibrated model and increase the model's accuracy in estimating pedestrians' density. In this end, the Voronoi algorithm has been used as the most up-to-date method to increase the accuracy in calculating the density. The result shows the normalized root mean square error in density is 0.18, indicating the model's high accuracy. Finally, the comparison of the fundamental diagram resulting from the simulated social force model and reality shows the proper performance of the social force model by a correlation coefficient of 0.74.
Pedestrian Self-organization Modeling with Behavioral Variables at a Signalized Intersection
https://orcid.org/http://orcid.org/0000-0001-5137-3673
https://orcid.org/http://orcid.org/0000-0003-1615-9553
This paper outlines pedestrians' movement at a signalized intersection of Tehran, Iran's capital. The presented model considers the effects of specific behavior variables derived from Iranian culture and local laws. The social force model has been built and calibrated by considering the crosswalk's geometric and heterogeneous (based on gender) conditions by implementing a genetic algorithm. A fundamental diagram has been used to validate the calibrated model and increase the model's accuracy in estimating pedestrians' density. In this end, the Voronoi algorithm has been used as the most up-to-date method to increase the accuracy in calculating the density. The result shows the normalized root mean square error in density is 0.18, indicating the model's high accuracy. Finally, the comparison of the fundamental diagram resulting from the simulated social force model and reality shows the proper performance of the social force model by a correlation coefficient of 0.74.
Pedestrian Self-organization Modeling with Behavioral Variables at a Signalized Intersection
https://orcid.org/http://orcid.org/0000-0001-5137-3673
https://orcid.org/http://orcid.org/0000-0003-1615-9553
This paper outlines pedestrians' movement at a signalized intersection of Tehran, Iran's capital. The presented model considers the effects of specific behavior variables derived from Iranian culture and local laws. The social force model has been built and calibrated by considering the crosswalk's geometric and heterogeneous (based on gender) conditions by implementing a genetic algorithm. A fundamental diagram has been used to validate the calibrated model and increase the model's accuracy in estimating pedestrians' density. In this end, the Voronoi algorithm has been used as the most up-to-date method to increase the accuracy in calculating the density. The result shows the normalized root mean square error in density is 0.18, indicating the model's high accuracy. Finally, the comparison of the fundamental diagram resulting from the simulated social force model and reality shows the proper performance of the social force model by a correlation coefficient of 0.74.
Pedestrian Self-organization Modeling with Behavioral Variables at a Signalized Intersection
Iran J Sci Technol Trans Civ Eng
Afandizadeh, Shahriar (author) / Esmailzadeh Kivi, Sanam (author) / Mirzahossein, Hamid (author)
2022-12-01
14 pages
Article (Journal)
Electronic Resource
English
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