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Remote Sens. 2019, 11(8), 925; https://doi.org/10.3390/rs11080925

Tracking and Simulating Pedestrian Movements at Intersections Using Unmanned Aerial Vehicles

1,2,3, 1, 2,3,4,* and 1
1
College of Civil Engineering, Shenzhen University, Shenzhen 518060, China
2
Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Smart Sensing and Services, and Research Institute of Smart Cities, Shenzhen University, Shenzhen 518060, China
3
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of Ministry of Natural Resources, Shenzhen University, Shenzhen 518060, China
4
Department of Urban Informatics, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
*
Author to whom correspondence should be addressed.
Received: 5 March 2019 / Revised: 8 April 2019 / Accepted: 12 April 2019 / Published: 16 April 2019
(This article belongs to the Section Urban Remote Sensing)
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Abstract

For a city to be livable and walkable is the ultimate goal of future cities. However, conflicts among pedestrians, vehicles, and cyclists at traffic intersections are becoming severe in high-density urban transportation areas, especially in China. Correspondingly, the transit time at intersections is becoming prolonged, and pedestrian safety is becoming endangered. Simulating pedestrian movements at complex traffic intersections is necessary to optimize the traffic organization. We propose an unmanned aerial vehicle (UAV)-based method for tracking and simulating pedestrian movements at intersections. Specifically, high-resolution videos acquired by a UAV are used to recognize and position moving targets, including pedestrians, cyclists, and vehicles, using the convolutional neural network. An improved social force-based motion model is proposed, considering the conflicts among pedestrians, cyclists, and vehicles. In addition, maximum likelihood estimation is performed to calibrate an improved social force model. UAV videos of intersections in Shenzhen are analyzed to demonstrate the performance of the presented approach. The results demonstrate that the proposed social force-based motion model can effectively simulate the movement of pedestrians and cyclists at road intersections. The presented approach provides an alternative method to track and simulate pedestrian movements, thus benefitting the organization of pedestrian flow and traffic signals controlling the intersections. View Full-Text
Keywords: pedestrian simulation; social force model; intersection; UAV; convolutional neural network; deep learning pedestrian simulation; social force model; intersection; UAV; convolutional neural network; deep learning
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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 (CC BY 4.0).
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Zhu, J.; Chen, S.; Tu, W.; Sun, K. Tracking and Simulating Pedestrian Movements at Intersections Using Unmanned Aerial Vehicles. Remote Sens. 2019, 11, 925.

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