Deep adaptive learning for safe and efficient navigation of pedestrian dynamics. Issue 4 (1st March 2021)

• Record Type: Journal Article
• Authors: Pugh, Nigel; Park, Hyoshin; Derjany, Pierrot; Liu, Dahai; Namilae, Sirish
• Is Part Of: IET intelligent transport systems. Volume 15:Issue 4(2021)
• Page Start: 538
• Page End: 548
• Publication Date: 2021-03-01
• Subjects: Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/itr2.12043
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract: An efficient and safe evacuation of passengers is important during emergencies. Overcapacity on a route can cause an increased evacuation time. Decision making is essential to optimally guide and distribute pedestrians to multiple routes while ensuring safety. Developing an optimal pedestrian path planning route while considering learning dynamics and uncertainties in the environment generated from pedestrian behaviour is challenging. While previous evacuation planning studies have focused on either simulation of realistic behaviours or simple route planning, the best route decisions with several intermediate decision‐points, especially under real‐time changing environments, have not been considered. This paper develops an optimal navigation model providing more navigation guidance for evacuation emergencies to minimize the total evacuation time while considering the influence of other passengers based on the social‐force model. The integration of the optimal navigation model was ultimately able to reduce the overall evacuation time of multiple scenarios presented with two different overall pedestrian totals. The overall maximum evacuation time savings presented was 10.6%.