Crowd Density Prediction Using Kalman Filtering Technique

Gayathri, H and Karthika, PS and Verma, A (2022) Crowd Density Prediction Using Kalman Filtering Technique. In: 5th International Conference of Transportation Research Group of India, CTRG 2019, 18 - 21 December 2019, Bhopal, pp. 161-170.

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Abstract

Crowd density is a significant facet of crowd dynamics that can be used to make strategic decisions for managing large number of people. Mass gathering events can potentially lead to severe disaster on crowd. A combination of high crowd density with restrictions on access points, poor crowd control measures, and having little knowledge about the areas and activities can lead to situations of disaster (Gayathri et al. in Int J Disaster Risk Reduct 25:82–91, 2017 [1]). From a macroscopic standpoint, density plays a major role in determining the risk levels at various locations. CCTV footage of the three locations collected during May 9, 2016, Kumbh Mela-Ujjain, is used for this study. This paper compares the concentration of densities at three different locations, either of prime importance where people perform certain activities, or cross the sections to reach prime activity locations and predicts densities using Kalman Filtering Technique (KFT). Mean absolute percentage error (MAPE) is used to corroborate the results obtained from observed and predicted density values.

Item Type:	Conference Paper
Publication:	Lecture Notes in Civil Engineering
Publisher:	Springer Science and Business Media Deutschland GmbH
Additional Information:	The copyright for this article belongs to the Springer Science and Business Media Deutschland GmbH.
Keywords:	Disasters; Location, Crowd; Crowd density; Crowd dynamics; Density pattern; Density prediction; India; Kalman filtering techniques; Kumbh mela; Number of peoples; Strategic decisions, Kalman filters
Department/Centre:	Division of Mechanical Sciences > Civil Engineering
Date Deposited:	05 Jul 2022 12:09
Last Modified:	07 Jul 2022 05:08
URI:	https://eprints.iisc.ac.in/id/eprint/74225

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