Classifying pedestrian trajectories by Machine learning using laser sensor data
Abstract. In the field of facility planning, the analysis of pedestrian trajectories using laser sensor-based behavior monitoring technologies is a proven way to improve our understanding of the behavioral features of foot-travelers. While these technologies can gather large volumes of trajectory data, the analysis of such data is a chaotic and complicated task and creates a large workload if it must be interpreted visually by human analysts. Hence, a method is needed for automatically extracting the features and their separate components from pedestrian trajectories and patterns. This study proposes just such a method based on a Restricted Boltzmann machine, a machine learning tool, to automatically extract and classify the latent features of pedestrian trajectories. Our method was applied to data taken in the outpatient waiting area of a hospital and the machine learning generated results were compared to those of visual classifications by human analysts. It was shown to be functional for classifying trajectories by orientation, stopping location and walking speed, and was considered effective for furnishing rough classifications resembling the intuition-based classifications of a human analyst.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Classifying pedestrian trajectories by Machine learning using laser sensor data ; volume:3 ; year:2022 ; pages:1-10 ; extent:10
AGILE: GIScience series ; 3 (2022), 1-10 (gesamt 10)
- Classification
-
Politik
- Creator
-
Kaneko, Hiroyuki
Osaragi, Toshihiro
- DOI
-
10.5194/agile-giss-3-8-2022
- URN
-
urn:nbn:de:101:1-2022061605340856971674
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:27 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Kaneko, Hiroyuki
- Osaragi, Toshihiro
Other Objects (12)
Classifying spatial trajectories using representation learning
Predicting Pedestrian Counts using Machine Learning
Spatiotemporal Trajectories of Pedestrian Mobility at the Train Station: evidence of 24 million trajectories
Classifying LEGO Bricks with Machine Learning
Analysis of spatio-temporal traffic patterns based on pedestrian trajectories
ANALYSIS OF SPATIO-TEMPORAL TRAFFIC PATTERNS BASED ON PEDESTRIAN TRAJECTORIES
Multi-sensor based indoor vehicle and pedestrian navigation
Classifying grains using behaviour-informed machine learning
Sensor fusion by Bayesian filtering for seamless pedestrian navigation
Classifying kinase conformations using a machine learning approach
Classifying Fake News Articles Using Machine Learning Techniques
Classifying spaces and classifying topoi
Classifying spatial trajectories using representation learning
Predicting Pedestrian Counts using Machine Learning
Spatiotemporal Trajectories of Pedestrian Mobility at the Train Station: evidence of 24 million trajectories
Classifying LEGO Bricks with Machine Learning
Analysis of spatio-temporal traffic patterns based on pedestrian trajectories
ANALYSIS OF SPATIO-TEMPORAL TRAFFIC PATTERNS BASED ON PEDESTRIAN TRAJECTORIES
Multi-sensor based indoor vehicle and pedestrian navigation
Classifying grains using behaviour-informed machine learning
Sensor fusion by Bayesian filtering for seamless pedestrian navigation
Classifying kinase conformations using a machine learning approach
Classifying Fake News Articles Using Machine Learning Techniques
Classifying spaces and classifying topoi
Classifying spatial trajectories using representation learning
Predicting Pedestrian Counts using Machine Learning
Spatiotemporal Trajectories of Pedestrian Mobility at the Train Station: evidence of 24 million trajectories
Classifying LEGO Bricks with Machine Learning
Analysis of spatio-temporal traffic patterns based on pedestrian trajectories
ANALYSIS OF SPATIO-TEMPORAL TRAFFIC PATTERNS BASED ON PEDESTRIAN TRAJECTORIES
Multi-sensor based indoor vehicle and pedestrian navigation
Classifying grains using behaviour-informed machine learning
Sensor fusion by Bayesian filtering for seamless pedestrian navigation
Classifying kinase conformations using a machine learning approach
Classifying Fake News Articles Using Machine Learning Techniques