English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Pedestrian modeling is a good way to predict pedestrian movement and thus canbe used for controlling pedestrian crowds and guiding evacuations inemergencies. In this paper, we propose a pedestrian movement model based onartificial neural network. In the model, the pedestrian velocity vectors arepredicted with two sub models, Semicircular Forward Space Based submodel(SFSB-submodel) and Rectangular Forward Space Based submodel (RFSB-submodel),respectively. Both unidirectional and bidirectional pedestrian flow at straightcorridors are investigated by comparing the simulation and the correspondingexperimental results. It is shown that the pedestrian trajectories and thefundamental diagrams from the model are all consistent with that fromexperiments. And the typical lane-formation phenomena are observed inbidirectional flow simulation. In addition, to quantitatively evaluate theprecision of the prediction, the mean destination error (MDE) and meantrajectory error (MTE) are defined and calculated to be approximately 0.2m and0.12m in unidirectional flow scenario. In bidirectional flow, relative distanceerror (RDE) is about 0.15m. The findings indicate that the proposed model isreasonable and capable of simulating the unidirectional and bidirectionalpedestrian flow illustrated in this paper.

Artificial Neural Network Based Modeling on Unidirectional and Bidirectional Pedestrian Flow at Straight Corridors