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ISPRS Int. J. Geo-Inf. 2015, 4(3), 1627-1656; doi:10.3390/ijgi4031627

Walk This Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis

1
Computational Social Science Program, George Mason University, 4400 University Drive, MS 6B2, Fairfax, VA 22030, USA
2
Department of Geography and Geoinformation Science, Center for Geospatial Intelligence, George Mason University, 4400 University Drive, MS 6C3, Fairfax, VA 22030, USA
3
Department of Civil, Environmental & Geomatic Engineering, University College London, Gower Street, London WC1E 6BT, UK
4
Draper Laboratory, 55 Technology Square, Cambridge, MA 02139, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Phaedon Kyriakidis and Wolfgang Kainz
Received: 5 June 2015 / Revised: 20 July 2015 / Accepted: 27 August 2015 / Published: 2 September 2015
(This article belongs to the Special Issue Advances in Spatio-Temporal Data Analysis and Mining)
View Full-Text   |   Download PDF [1124 KB, uploaded 2 September 2015]   |  

Abstract

Pedestrian movement is woven into the fabric of urban regions. With more people living in cities than ever before, there is an increased need to understand and model how pedestrians utilize and move through space for a variety of applications, ranging from urban planning and architecture to security. Pedestrian modeling has been traditionally faced with the challenge of collecting data to calibrate and validate such models of pedestrian movement. With the increased availability of mobility datasets from video surveillance and enhanced geolocation capabilities in consumer mobile devices we are now presented with the opportunity to change the way we build pedestrian models. Within this paper we explore the potential that such information offers for the improvement of agent-based pedestrian models. We introduce a Scene- and Activity-Aware Agent-Based Model (SA2-ABM), a method for harvesting scene activity information in the form of spatiotemporal trajectories, and incorporate this information into our models. In order to assess and evaluate the improvement offered by such information, we carry out a range of experiments using real-world datasets. We demonstrate that the use of real scene information allows us to better inform our model and enhance its predictive capabilities. View Full-Text
Keywords: pedestrian modeling; pedestrian tracking; activity monitoring; spatiotemporal trajectories; agent-based modeling pedestrian modeling; pedestrian tracking; activity monitoring; spatiotemporal trajectories; agent-based modeling
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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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MDPI and ACS Style

Crooks, A.; Croitoru, A.; Lu, X.; Wise, S.; Irvine, J.M.; Stefanidis, A. Walk This Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis. ISPRS Int. J. Geo-Inf. 2015, 4, 1627-1656.

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