Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes. Issue 7 (3rd July 2021)

• Record Type: Journal Article
• Authors: Yu, Qingying; Luo, Yonglong; Wang, Dongxia; Chen, Chuanming; Sun, Liping; Zhang, Yawen
• Is Part Of: International journal of geographical information science. Volume 35:Issue 7(2021)
• Page Start: 1346
• Page End: 1373
• Publication Date: 2021-07-03
• Subjects: Trajectory data -- transportation-mode identification -- location-feature extraction -- information entropy -- multi-layer neural network; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2021.1901904
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Residents' trajectory data denote their instantaneous locations along their movements. Mobility research that applies trajectory mining techniques to identify the transportation modes of these movements can inform urban transportation planning. Herein, we propose a five-step approach with information entropy and a multi-layer neural network to identify transportation modes from trajectory data. First, this approach extracts the motion features at each time-stamped location based on foundation geospatial data and spatiotemporal trajectory data, including the speed, acceleration, change of direction, rate of change in direction, and distance from each basic transportation facility. The second step uses information entropy to identify the features that play key roles in identifying transportation modes. The third step weighs each attribute in the feature vector consisting of the selected features and normalizes it to prepare it as input data. The fourth step constructs, trains, and tests a multi-layer neural network with seven-fold cross-validation. The final step includes a post-processing method to optimize the identification result. We use F-measure metric to evaluate the performance. Experimental results on a real trajectory dataset show that the proposed approach can identify the transportation mode at each time-stamped location and outperforms existing transportation-mode identification methods in terms of accuracy and stability.