Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model
The increasing wealth of truck global positioning system (GPS) data has broadened the opportunities for understanding freight logistics activities and enhancing research capabilities to real-world case studies. A very important piece of information from the planning and regulatory perspective concer...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10364749/ |
| _version_ | 1797376370700976128 |
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| author | Mehdi Taghavi Elnaz Irannezhad Carlo G. Prato |
| author_facet | Mehdi Taghavi Elnaz Irannezhad Carlo G. Prato |
| author_sort | Mehdi Taghavi |
| collection | DOAJ |
| description | The increasing wealth of truck global positioning system (GPS) data has broadened the opportunities for understanding freight logistics activities and enhancing research capabilities to real-world case studies. A very important piece of information from the planning and regulatory perspective concerns the occurrence and location of rest stops. In this study, we propose a data-driven unsupervised machine learning method to impute truck stop events by using a Continuous Hidden Markov Model (CHMM). Specifically, we estimate the joint probability distribution of a mixture of multivariate Gaussian densities, whose parameters depend on the latent states of a Markov chain. Each density represents a cluster of stops that are identified not only from their spatial proximity but also from their temporal proximity as the clustering of the rest stops depends on latent states that are conditional on expected times retrieved from the observed data. In this study, we applied the proposed method to a database containing more than 71 million GPS records of Australian trucks, and we particularly aimed to identify rest stops based on a list of features related to the locations and the load of the trucks. The results showed that the CHMM could account for the location proximity for different activities of truck drivers, and they were validated against complementary data on truck loads and land use by using a stratified sampling technique. Validation results indicated that 94.1% of the rest stops were correctly identified, and highlighted the advantage of the proposed approach without any requirement of labelled data, driver logbook or complimentary survey. |
| first_indexed | 2024-03-08T19:37:34Z |
| format | Article |
| id | doaj.art-b25adbc78b144db2a36cf4bc816dfe17 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:37:34Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b25adbc78b144db2a36cf4bc816dfe172023-12-26T00:06:57ZengIEEEIEEE Access2169-35362023-01-011114377114378110.1109/ACCESS.2023.334415610364749Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov ModelMehdi Taghavi0Elnaz Irannezhad1https://orcid.org/0000-0002-6298-6042Carlo G. Prato2https://orcid.org/0000-0002-1218-4922School of Civil Engineering, The University of Queensland, Brisbane, QLD, AustraliaResearch Centre for Integrated Transport Innovations (rCITI), School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, AustraliaSchool of Civil Engineering, University of Leeds, Leeds, U.K.The increasing wealth of truck global positioning system (GPS) data has broadened the opportunities for understanding freight logistics activities and enhancing research capabilities to real-world case studies. A very important piece of information from the planning and regulatory perspective concerns the occurrence and location of rest stops. In this study, we propose a data-driven unsupervised machine learning method to impute truck stop events by using a Continuous Hidden Markov Model (CHMM). Specifically, we estimate the joint probability distribution of a mixture of multivariate Gaussian densities, whose parameters depend on the latent states of a Markov chain. Each density represents a cluster of stops that are identified not only from their spatial proximity but also from their temporal proximity as the clustering of the rest stops depends on latent states that are conditional on expected times retrieved from the observed data. In this study, we applied the proposed method to a database containing more than 71 million GPS records of Australian trucks, and we particularly aimed to identify rest stops based on a list of features related to the locations and the load of the trucks. The results showed that the CHMM could account for the location proximity for different activities of truck drivers, and they were validated against complementary data on truck loads and land use by using a stratified sampling technique. Validation results indicated that 94.1% of the rest stops were correctly identified, and highlighted the advantage of the proposed approach without any requirement of labelled data, driver logbook or complimentary survey.https://ieeexplore.ieee.org/document/10364749/GPS truck data miningtrip segmentationstop identificationContinuous Hidden Markov Modelfreight transportation |
| spellingShingle | Mehdi Taghavi Elnaz Irannezhad Carlo G. Prato Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model IEEE Access GPS truck data mining trip segmentation stop identification Continuous Hidden Markov Model freight transportation |
| title | Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model |
| title_full | Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model |
| title_fullStr | Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model |
| title_full_unstemmed | Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model |
| title_short | Truck Rest Stop Imputation From GPS Data: An Interpretable Activity-Based Continuous Hidden Markov Model |
| title_sort | truck rest stop imputation from gps data an interpretable activity based continuous hidden markov model |
| topic | GPS truck data mining trip segmentation stop identification Continuous Hidden Markov Model freight transportation |
| url | https://ieeexplore.ieee.org/document/10364749/ |
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