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

ISPRS International Journal of Geo-Information

Andrew Crooks³  Arie Croitoru⁴  Xu Lu⁴  Sarah Wise¹  John M. Irvine²  Anthony Stefanidis⁴  Phaedon Kyriakidis⁵ 

[1] Department of Civil, Environmental & Geomatic Engineering, University College London, Gower Street, London WC1E 6BT, UK; E-Mail:;Draper Laboratory, 55 Technology Square, Cambridge, MA 02139, USA; E-Mail:;Computational Social Science Program, George Mason University, 4400 University Drive, MS 6B2, Fairfax, VA 22030, USA;Department of Geography and Geoinformation Science, Center for Geospatial Intelligence, George Mason University, 4400 University Drive, MS 6C3, Fairfax, VA 22030, USA; E-Mails:;id="af1-ijgi-04-01627">1Computational Social Science Program, George Mason University, 4400 University Drive, MS 6B2, Fairfax, VA 22030, U

关键词: pedestrian modeling;    pedestrian tracking;    activity monitoring;    spatiotemporal trajectories;    agent-based modeling;   

DOI  :  10.3390/ijgi4031627

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.