TraceBERT-A Feasibility Study on Reconstructing Spatial-Temporal Gaps from Incomplete Motion Trajectories via BERT Training Process on Discrete Location Sequences

Crivellari, Alessandro1; Resch, Bernd2,3; Shi, Yuhui1

2022-02-01

10.3390/s22041682

SENSORS   影响因子和分区

1424-8220

Trajectory data represent an essential source of information on travel behaviors and human mobility patterns, assuming a central role in a wide range of services related to transportation planning, personalized recommendation strategies, and resource management plans. The main issue when dealing with trajectory recordings, however, is characterized by temporary losses in the data collection, causing possible spatial-temporal gaps and missing trajectory segments. This is especially critical in those use cases based on non-repetitive individual motion traces, when the user's missing information cannot be directly reconstructed due to the absence of historical individual repetitive routes. Inserted in the context of location-based trajectory modeling, we tackle the problem by proposing a technical parallelism with the natural language processing domain. Specifically, we introduce the use of the Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language representation model, into the trajectory processing research field. By training deep bidirectional representations from unlabeled location sequences, jointly conditioned on both left and right context, we derive an explicit predicted estimation of the missing locations along the trace. The proposed framework, named TraceBERT, was tested on a real-world large-scale trajectory dataset of short-term tourists, exploring an effective attempt of adapting advanced language modeling approaches into mobility-based applications and demonstrating a prominent potential on trajectory reconstruction over traditional statistical approaches.

BERT neural networks trajectories human mobility spatial-temporal gaps

SCI ; EI

英语

第一 ; 通讯

Chemistry ; Engineering ; Instruments & Instrumentation

Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation

WOS:000769415300001

MDPI

20220811691021

Behavioral research ; Information management ; Large dataset ; Location ; Modeling languages ; Trajectories

Ergonomics and Human Factors Engineering:461.4 ; Data Processing and Image Processing:723.2 ; Social Sciences:971

CHEMISTRY

Web of Science

1.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
2.Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria
3.Harvard Univ, Ctr Geog Anal, Cambridge, MA 02138 USA

Crivellari, Alessandro,Resch, Bernd,Shi, Yuhui. TraceBERT-A Feasibility Study on Reconstructing Spatial-Temporal Gaps from Incomplete Motion Trajectories via BERT Training Process on Discrete Location Sequences[J]. SENSORS,2022,22(4).

Crivellari, Alessandro,Resch, Bernd,&Shi, Yuhui.(2022).TraceBERT-A Feasibility Study on Reconstructing Spatial-Temporal Gaps from Incomplete Motion Trajectories via BERT Training Process on Discrete Location Sequences.SENSORS,22(4).

Crivellari, Alessandro,et al."TraceBERT-A Feasibility Study on Reconstructing Spatial-Temporal Gaps from Incomplete Motion Trajectories via BERT Training Process on Discrete Location Sequences".SENSORS 22.4(2022).