A deep-learning approach for modelling pedestrian movement uncertainty in large- scale indoor areas
Modelling pedestrian movement uncertainty in complex urban environments is regarded as a meaningful and challenging task regarding the promotion of geospatial data mining and analysis. However, the traditional uncertainty prediction model only takes the movement distance or speed into consideration...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222002539 |
| _version_ | 1797989980550725632 |
|---|---|
| author | Wenzhong Shi Yue Yu Zhewei Liu Ruizhi Chen Liang Chen |
| author_facet | Wenzhong Shi Yue Yu Zhewei Liu Ruizhi Chen Liang Chen |
| author_sort | Wenzhong Shi |
| collection | DOAJ |
| description | Modelling pedestrian movement uncertainty in complex urban environments is regarded as a meaningful and challenging task regarding the promotion of geospatial data mining and analysis. However, the traditional uncertainty prediction model only takes the movement distance or speed into consideration and is not able to adapt well to time-varying measurement errors. In this paper, a deep-learning framework is proposed for modelling pedestrian movement uncertainty in large-scale indoor areas, in which a hybrid deep-learning model combines a one-dimensional Convolutional Neural Network (1D-CNN) with a long short-term memory (LSTM) network is proposed for enhancing feature extraction performance and reducing time correlation errors. The proposed framework takes human motion related measurement features into consideration, in which the moving step-length and heading information during a time period are also reconstructed and modelled as the input to the deep-learning model. Compared with state-of-art algorithms applied to different real-world trajectory datasets, the proposed deep-learning approach demonstrates much better performance of uncertainty region prediction, including the different indexes (Euclidean error distance, completeness and density) This study has leaded to the provision of an effective and practical framework for modelling trajectory uncertainty of the pedestrian in challenging urban environments, and which is expected to benefit smart city and spatial perception related applications. |
| first_indexed | 2024-04-11T08:29:18Z |
| format | Article |
| id | doaj.art-afe073361a2a471bb112f8274eeab5bd |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-11T08:29:18Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-afe073361a2a471bb112f8274eeab5bd2022-12-22T04:34:37ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-11-01114103065A deep-learning approach for modelling pedestrian movement uncertainty in large- scale indoor areasWenzhong Shi0Yue Yu1Zhewei Liu2Ruizhi Chen3Liang Chen4The Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, 999077, Hong Kong, ChinaThe Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, 999077, Hong Kong, China; Corresponding author.The Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, 999077, Hong Kong, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430000, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430000, ChinaModelling pedestrian movement uncertainty in complex urban environments is regarded as a meaningful and challenging task regarding the promotion of geospatial data mining and analysis. However, the traditional uncertainty prediction model only takes the movement distance or speed into consideration and is not able to adapt well to time-varying measurement errors. In this paper, a deep-learning framework is proposed for modelling pedestrian movement uncertainty in large-scale indoor areas, in which a hybrid deep-learning model combines a one-dimensional Convolutional Neural Network (1D-CNN) with a long short-term memory (LSTM) network is proposed for enhancing feature extraction performance and reducing time correlation errors. The proposed framework takes human motion related measurement features into consideration, in which the moving step-length and heading information during a time period are also reconstructed and modelled as the input to the deep-learning model. Compared with state-of-art algorithms applied to different real-world trajectory datasets, the proposed deep-learning approach demonstrates much better performance of uncertainty region prediction, including the different indexes (Euclidean error distance, completeness and density) This study has leaded to the provision of an effective and practical framework for modelling trajectory uncertainty of the pedestrian in challenging urban environments, and which is expected to benefit smart city and spatial perception related applications.http://www.sciencedirect.com/science/article/pii/S1569843222002539Deep-learningPedestrian movement uncertaintyMeasurement errors1D-CNNLSTM |
| spellingShingle | Wenzhong Shi Yue Yu Zhewei Liu Ruizhi Chen Liang Chen A deep-learning approach for modelling pedestrian movement uncertainty in large- scale indoor areas International Journal of Applied Earth Observations and Geoinformation Deep-learning Pedestrian movement uncertainty Measurement errors 1D-CNN LSTM |
| title | A deep-learning approach for modelling pedestrian movement uncertainty in large- scale indoor areas |
| title_full | A deep-learning approach for modelling pedestrian movement uncertainty in large- scale indoor areas |
| title_fullStr | A deep-learning approach for modelling pedestrian movement uncertainty in large- scale indoor areas |
| title_full_unstemmed | A deep-learning approach for modelling pedestrian movement uncertainty in large- scale indoor areas |
| title_short | A deep-learning approach for modelling pedestrian movement uncertainty in large- scale indoor areas |
| title_sort | deep learning approach for modelling pedestrian movement uncertainty in large scale indoor areas |
| topic | Deep-learning Pedestrian movement uncertainty Measurement errors 1D-CNN LSTM |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222002539 |
| work_keys_str_mv | AT wenzhongshi adeeplearningapproachformodellingpedestrianmovementuncertaintyinlargescaleindoorareas AT yueyu adeeplearningapproachformodellingpedestrianmovementuncertaintyinlargescaleindoorareas AT zheweiliu adeeplearningapproachformodellingpedestrianmovementuncertaintyinlargescaleindoorareas AT ruizhichen adeeplearningapproachformodellingpedestrianmovementuncertaintyinlargescaleindoorareas AT liangchen adeeplearningapproachformodellingpedestrianmovementuncertaintyinlargescaleindoorareas AT wenzhongshi deeplearningapproachformodellingpedestrianmovementuncertaintyinlargescaleindoorareas AT yueyu deeplearningapproachformodellingpedestrianmovementuncertaintyinlargescaleindoorareas AT zheweiliu deeplearningapproachformodellingpedestrianmovementuncertaintyinlargescaleindoorareas AT ruizhichen deeplearningapproachformodellingpedestrianmovementuncertaintyinlargescaleindoorareas AT liangchen deeplearningapproachformodellingpedestrianmovementuncertaintyinlargescaleindoorareas |