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A Multi-Dimensional Approach to Human Mobility and Transportation Mode Detection Using GPS Data
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0001-7190-2640
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 [en]
Transport mode detection, Machine learning, Statistical learning, Rule-based method, Data labelling
National Category
Transport Systems and Logistics Computer Sciences
Identifiers
URN: urn:nbn:se:du-48308ISBN: 978-91-88679-62-8 (print)OAI: oai:DiVA.org:du-48308DiVA, id: diva2:1847641
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
List of papers
1. Review and evaluation of methods in transport mode detection based on GPS tracking data
Open this publication in new window or tab >>Review and evaluation of methods in transport mode detection based on GPS tracking data
2021 (English)In: Journal of Traffic and Transportation Engineering (English Edition), ISSN 2095-7564, Vol. 8, no 4, p. 467-482Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Chang'an University, 2021
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:du-37854 (URN)10.1016/j.jtte.2021.04.004 (DOI)000686480100001 ()2-s2.0-85110302232 (Scopus ID)
Available from: 2021-08-05 Created: 2021-08-05 Last updated: 2024-03-28Bibliographically approved
2. A stepwise methodology for transport mode detection in GPS tracking data
Open this publication in new window or tab >>A stepwise methodology for transport mode detection in GPS tracking data
2022 (English)In: Travel Behaviour & Society, ISSN 2214-367X, E-ISSN 2214-3688, Vol. 26, p. 159-167Article in journal (Refereed) Published
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:du-36348 (URN)10.1016/j.tbs.2021.10.004 (DOI)000718159400006 ()2-s2.0-85117267641 (Scopus ID)
Available from: 2021-03-18 Created: 2021-03-18 Last updated: 2024-03-28Bibliographically approved
3. A deep semi-supervised machine learning algorithm for detecting transportation modes based on GPS tracking data
Open this publication in new window or tab >>A deep semi-supervised machine learning algorithm for detecting transportation modes based on GPS tracking data
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
Travel identification, LSTM Autoencoder, Unsupervised learning, Deep learning, GPS tracking data
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:du-48203 (URN)10.1007/s11116-024-10472-x (DOI)001164352600001 ()
Available from: 2024-03-08 Created: 2024-03-08 Last updated: 2024-03-28Bibliographically approved
4. Enhanced Clustering Approach for Transportation Mode Classification using GPS Data and Particle Swarm Optimization
Open this publication in new window or tab >>Enhanced Clustering Approach for Transportation Mode Classification using GPS Data and Particle Swarm Optimization
(English)Manuscript (preprint) (Other academic)
Abstract [en]

The widespread adoption of Global Positioning Systems (GPS) in transportation has significantly contributed to the understanding of human behaviour, enabling the extraction of crucial travel information. However, the exploration of transportation modes using GPS data remains an under-researched domain due to its intricate analytical demands. While various methods, ranging from rule-based approaches to advanced machine learning algorithms, have been employed to identify transportation modes from GPS data, most have been tested on limited labelled datasets. This study introduces an innovative clustering method that combines multi-criteria decision-making, network analysis, and the meta-heuristic algorithm of particle swarm optimization to effectively cluster transportation modes. Pioneering a hybrid approach, the study utilizes elements from the Analytic Network Process (ANP) super matrix in conjunction with transportation modes as variables, harnessing the particle swarm optimization (PSO) algorithm with a fully unlabelled dataset. The compelling findings underscore the model's effectiveness, achieving an impressive accuracy rate exceeding 88% in transportation mode clustering.

Keywords
Multi-criteria decision-making, Particle swarm optimization, Transportation modes, Global positioning systems (GPS), Network analysis
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:du-48389 (URN)
Available from: 2024-04-19 Created: 2024-04-19 Last updated: 2024-04-19Bibliographically approved
5. Testing feasibility of using a hidden Markov model on predicting human mobility based on GPS tracking data
Open this publication in new window or tab >>Testing feasibility of using a hidden Markov model on predicting human mobility based on GPS tracking data
2024 (English)In: Transportmetrica B: Transport Dynamics, ISSN 2168-0566, Vol. 12, no 1, article id 2336037Article in journal (Refereed) Published
Abstract [en]

Human mobility behaviour is far from random and can be predictable. Predicting human mobility behaviour has the potential to improve location selection for facilities, transportation services, urban planning, and can be beneficial in providing more efficient sustainable urban development strategies. However, it is difficult to model urban mobility patterns since incentives for mobility is complex, and influenced by several factors, such as dynamic population, weather conditions. Thus, this paper proposes a prediction-oriented algorithm under the framework of a Hidden Markov Model to predict next-location and time-of-arrival of human mobility. A comprehensive evaluation of these two schemes for the representation of latent and observable variables is discussed. In conclusion, the paper provides a valuable contribution to the field of mobility behaviour prediction by proposing a novel algorithm. The evaluation shows that the proposed algorithm is stable and consistent in predicting the next location of users based on their past trajectories. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Place, publisher, year, edition, pages
Taylor & Francis, 2024
Keywords
GPS movement data, Human behaviour, Markov chain, spatial–temporal prediction, sustainable urban development
National Category
Transport Systems and Logistics Computer Sciences
Identifiers
urn:nbn:se:du-48388 (URN)10.1080/21680566.2024.2336037 (DOI)001196930700001 ()2-s2.0-85189611465 (Scopus ID)
Available from: 2024-04-19 Created: 2024-04-19 Last updated: 2024-06-14Bibliographically approved

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