Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2017

Abstract

This work proposes a deep neural network approach known as the column-structured deep neural network (COL-DNN-R) for predicting crowd density in an indoor environment using historical Wi-Fi traces of individual visitors. With a structure designed to minimize feature engineering, COL-DNN accepts raw features such as crowd density, opening and closing hours and peak visitor counts for extracting features. The extracted features are used by a regression model R for predicting the crowd densities. Standard regression models such as MLP, RF and SVM can be used as R. Experiments are performed to investigate the effect of feature representation and model structure on the prediction accuracy. Experiment results show the best prediction accuracy is obtained using features extracted by COL-DNN and using MLP as the regression model, i.e., R = MLP.

Keywords

Deep Neural Network, Feature Extraction, Indoor Crowd Prediction

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

PredictGIS 2017: Proceedings of the 1st ACM SIGSPATIAL Workshop on Prediction of Human Mobility, Redondo Beach, CA, November 7-10

First Page

1

Last Page

7

ISBN

9781450355018

Identifier

10.1145/3152341.3152349

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3152341.3152349

Download

Share

COinS