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A deep semi-supervised machine learning algorithm for detecting transportation modes based on GPS tracking data
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0001-7190-2640
Dalarna Univ, Sch Technol & Business Studies, SE-79188 Falun, Sweden..
Dalarna University, School of Information and Engineering, Microdata Analysis. Dalarna Univ, Sch Technol & Business Studies, SE-79188 Falun, Sweden..ORCID iD: 0000-0002-7512-5321
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0003-4871-833X
2024 (English)In: Transportation, ISSN 0049-4488, E-ISSN 1572-9435Article in journal (Refereed) Epub ahead of print
Abstract [en]

Transportation research has benefited from GPS tracking devices since a higher volume of data can be acquired. Trip information such as travel speed, time, and most visited locations can be easily extracted from raw GPS tracking data. However, transportation modes cannot be extracted directly and require more complex analytical processes. Common approaches for detecting travel modes heavily depend on manual labelling of trajectories with accurate trip information, which is inefficient in many aspects. This paper proposes a method of semi-supervised machine learning by using minimal labelled data. The method can accept GPS trajectory with adjustable length and extract latent information with long short-term memory (LSTM) Autoencoder. The method adopts a deep neural network architecture with three hidden layers to map the latent information to detect transportation mode. The proposed method is assessed by applying it to the case study where an accuracy of 93.94% can be achieved, which significantly outperforms similar studies.

Place, publisher, year, edition, pages
Springer, 2024.
Keywords [en]
Travel identification, LSTM Autoencoder, Unsupervised learning, Deep learning, GPS tracking data
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:du-48203DOI: 10.1007/s11116-024-10472-xISI: 001164352600001OAI: oai:DiVA.org:du-48203DiVA, id: diva2:1843284
Available from: 2024-03-08 Created: 2024-03-08 Last updated: 2024-03-28Bibliographically approved
In thesis
1. A Multi-Dimensional Approach to Human Mobility and Transportation Mode Detection Using GPS Data
Open this publication in new window or tab >>A Multi-Dimensional Approach to Human Mobility and Transportation Mode Detection Using GPS Data
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

GPS tracking data is an essential resource for analyzing human travel patterns and evaluating the effects on transportation systems. The primary challenge, however, is to accurately identify the modes of transportation within unlabeled GPS data. These approaches range from simple rule-based systems to advanced machine-learning techniques. This dissertation aims to bridge this gap by examining the critical features and techniques of these methods and proposing a novel approach for detecting transportation modes in GPS tracking data. To achieve this goal, a comprehensive understanding of individual journeys is crucial. Thus, this research adopts a microdata analytic approach, encompassing data collection, processing, analysis, and decision-making stages. Doing so contributes to advancing human mobility research and transportation mode detection. 

Paper I undertook a systematic review of transport mode detection methodologies to fill the research gap, emphasizing the predominance of supervised learning algorithms and highlighting the need for further research to address the limitations of small datasets. Paper II introduced a stepwise methodology, integrating unsupervised learning, GIS, and supervised algorithms to detect transport modes while minimizing reliance on labelled data. The Random Forest algorithm emerged as a precise but time-intensive solution. Paper III showcased a novel approach to transport mode detection using deep learning models, outperforming traditional machine learning methods. This paper signals the potential of deep learning in the field and demonstrates the importance of raw GPS data in enhancing accuracy. Paper V addressed the challenge of predicting human mobility patterns under the Hidden Markov Model (HMM) framework, highlighting the applicability of HMMs to understanding and predicting complex mobility behaviour. This paper emphasized the need for GPS tracking data in developing advanced mobility models. Paper IV ventured into hybrid methodology by combining K-means clustering with the ANP-PSO algorithm to enhance transportation mode classification. This pioneering approach improved classification accuracy while reducing dependence on labelled datasets. 

Collectively, these papers underscore the opportunities and limitations in human mobility research, offering insights into future directions for mitigating data quality issues and improving the accuracy of transportation mode detection. These innovative methodologies have practical implications for transportation planning, resource allocation, and intelligent transportation system development, ultimately shaping the future of transportation research and decision-making. Standardized data collection, processing, and labelling methods are crucial and need attention in future research. Future research can focus on developing such benchmarks and validation protocols to enhance the reliability and comparability of results.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2024
Series
Dalarna Doctoral Dissertations ; 33
Keywords
Transport mode detection, Machine learning, Statistical learning, Rule-based method, Data labelling
National Category
Transport Systems and Logistics Computer Sciences
Identifiers
urn:nbn:se:du-48308 (URN)978-91-88679-62-8 (ISBN)
Public defence
2024-05-31, room Clas Ohlson, 13:00 (English)
Opponent
Supervisors
Available from: 2024-04-19 Created: 2024-03-28 Last updated: 2024-04-19Bibliographically approved

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Sadeghian, PariaZhao, XiaoyunHåkansson, Johan

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CiteExportLink to record
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Citation style
  • apa
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