Work

Optimal Crowd Navigation via Spatio-temporal Multidimensional Collective Data Analysis

Year: 2017

Type: article

Abstract: We introduce technology for predicting the risk of congestion in the near future from real-time observational data of people or automobile flows and for automatically deriving an optimal crowd navigation plan online to avoid that risk.This technology can deal with unforeseen situations by performing simulations based on machine learning.Our aim in establishing this technology is to help construct a safe and secure and more comfortable social infrastructure.

Source: NTT technical review

DOI: 10.53829/ntr201709fa5

Authors: Futoshi Naya, Masaru Miyamoto, Naonori Ueda

Institution: NTT (Japan)

Language: English

Topic: Evacuation and Crowd Dynamics

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: bronze

Funder: Keio University