Tracking and Simulating Pedestrian Movements at Intersections Using Unmanned Aerial Vehicles
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| Veröffentlicht in: | Remote Sensing vol. 11, no. 8 (Feb 2019), p. n/a |
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MDPI AG
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|---|---|---|---|
| 001 | 2304028424 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs11080925 |2 doi | |
| 035 | |a 2304028424 | ||
| 045 | 2 | |b d20190201 |b d20190228 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Zhu, Jiasong | |
| 245 | 1 | |a Tracking and Simulating Pedestrian Movements at Intersections Using Unmanned Aerial Vehicles | |
| 260 | |b MDPI AG |c Feb 2019 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 651 | 4 | |a France | |
| 651 | 4 | |a China | |
| 653 | |a International conferences | ||
| 653 | |a Pedestrians | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Roads & highways | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Tracking | ||
| 653 | |a Distance learning | ||
| 653 | |a Traffic congestion | ||
| 653 | |a Traffic conflicts | ||
| 653 | |a Pedestrian traffic flow | ||
| 653 | |a Computer simulation | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Vehicles | ||
| 653 | |a Bicycling | ||
| 653 | |a Global positioning systems--GPS | ||
| 653 | |a Traffic flow | ||
| 653 | |a Pedestrian safety | ||
| 653 | |a Social organization | ||
| 653 | |a Traffic signals | ||
| 653 | |a Traffic accidents & safety | ||
| 653 | |a Target recognition | ||
| 653 | |a Neural networks | ||
| 653 | |a Moving targets | ||
| 653 | |a Maximum likelihood estimation | ||
| 653 | |a Methods | ||
| 653 | |a Transit time | ||
| 653 | |a Algorithms | ||
| 653 | |a Movement | ||
| 653 | |a Informatics | ||
| 653 | |a Traffic control | ||
| 653 | |a Urban transportation | ||
| 653 | |a Internet of Things | ||
| 653 | |a Traffic intersections | ||
| 653 | |a Laboratories | ||
| 700 | 1 | |a Chen, Siyuan | |
| 700 | 1 | |a Tu, Wei | |
| 700 | 1 | |a Sun, Ke | |
| 773 | 0 | |t Remote Sensing |g vol. 11, no. 8 (Feb 2019), p. n/a | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2304028424/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2304028424/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2304028424/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |