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Predicting the crowd behavior in complex environments is a key requirement for crowd and disaster management, architectural design, and urban planning. Given a crowd’s immediate state, current approaches must be successively repeated over multiple time-steps for long-term predictions, leading to compute expensive and error-prone results. However, most applications require the ability to accurately predict hundreds of possible simulation outcomes (e.g., under different environment and crowd situations) at real-time rates, for which these approaches are prohibitively expensive. We propose the first deep framework to instantly predict the long-term flow of crowds in arbitrarily large, realistic environments. Central to our approach are a novel representation CAGE, which efficiently encodes crowd scenarios into compact, fixed-size representations that losslessly represent the environment, and a modified SegNet architecture for instant long-term crowd flow prediction. We conduct comprehensive experiments on novel synthetic and real datasets. Our results indicate that our approach is able to capture the essence of real crowd movement over very long time periods, while generalizing to never-before-seen environments and crowd contexts. The associated Supplementary Material, models, and datasets are available at github.com/SSSohn/LTCF.
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Generation of crowd arrival and destination locations/times in complex transit facilities
In order to simulate virtual agents in the replica of a real facility across a long time span, a crowd simulation engine needs a list of agent arrival and destination locations and times that reflect those seen in the actual facility. Working together with a major metropolitan transportation authority, we propose a specification that can be used to procedurally generate this information. This specification is both uniquely compact and expressive—compact enough to mirror the mental model of building managers and expressive enough to handle the wide variety of crowds seen in real urban environments. We also propose a procedural algorithm for generating tens of thousands of high-level agent paths from this specification. This algorithm allows our specification to be used with traditional crowd simulation obstacle avoidance algorithms while still maintaining the realism required for the complex, real-world simulations of a transit facility. Our evaluation with industry professionals shows that our approach is intuitive and provides controls at the right level of detail to be used in large facilities (200,000+ people/day).
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- PAR ID:
- 10128080
- Date Published:
- Journal Name:
- The Visual Computer
- ISSN:
- 0178-2789
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s00371-019-01761-z
