Using genetic programming on GPS trajectories for travel mode detection
Abstract The widespread and increased use of smartphones, equipped with the global positioning system (GPS), has facilitated the automation of travel data collection. Most studies on travel mode detection that used GPS data have employed hand‐crafted features that may not have the capabilities to de...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12132 |
| _version_ | 1811266947213426688 |
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| author | Farnoosh Namdarpour Mahmoud Mesbah Amir H. Gandomi Behrang Assemi |
| author_facet | Farnoosh Namdarpour Mahmoud Mesbah Amir H. Gandomi Behrang Assemi |
| author_sort | Farnoosh Namdarpour |
| collection | DOAJ |
| description | Abstract The widespread and increased use of smartphones, equipped with the global positioning system (GPS), has facilitated the automation of travel data collection. Most studies on travel mode detection that used GPS data have employed hand‐crafted features that may not have the capabilities to detect all complex travel behaviours since their performance is highly dependent on the skills of domain experts and may limit the performance of classifiers. In this study, a genetic programming (GP) approach is proposed to select and construct features for GPS trajectories. GP increased the macro‐average of the F1‐score from 77.3 to 80.0 in feature construction when applied to the GeoLife dataset. It could transform the decision tree into a competitive classifier with support vector machines (SVMs) and neural networks that are both able to extract high‐level features. Simplicity, interpretability, and a relatively lower risk of overfitting allow the proposed model to be readily used for passive travel data collection even on smartphones with limited computational capacities. The model is validated by a second dataset from Australia and New Zealand, which indicated that a decision tree with the GP constructed features as its input has a considerably higher transferability than SVMs and neural networks. |
| first_indexed | 2024-04-12T20:52:28Z |
| format | Article |
| id | doaj.art-6ef1d1a3b49f445e81e3f6c423ecfb03 |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-04-12T20:52:28Z |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-6ef1d1a3b49f445e81e3f6c423ecfb032022-12-22T03:17:05ZengWileyIET Intelligent Transport Systems1751-956X1751-95782022-01-011619911310.1049/itr2.12132Using genetic programming on GPS trajectories for travel mode detectionFarnoosh Namdarpour0Mahmoud Mesbah1Amir H. Gandomi2Behrang Assemi3Department of Civil and Environmental Engineering Amirkabir University of Technology Tehran IranDepartment of Civil and Environmental Engineering Amirkabir University of Technology Tehran IranFaculty of Engineering & Information Technology University of Technology Sydney Ultimo AustraliaSchool of Built Environment Queensland University of Technology (QUT) Brisbane AustraliaAbstract The widespread and increased use of smartphones, equipped with the global positioning system (GPS), has facilitated the automation of travel data collection. Most studies on travel mode detection that used GPS data have employed hand‐crafted features that may not have the capabilities to detect all complex travel behaviours since their performance is highly dependent on the skills of domain experts and may limit the performance of classifiers. In this study, a genetic programming (GP) approach is proposed to select and construct features for GPS trajectories. GP increased the macro‐average of the F1‐score from 77.3 to 80.0 in feature construction when applied to the GeoLife dataset. It could transform the decision tree into a competitive classifier with support vector machines (SVMs) and neural networks that are both able to extract high‐level features. Simplicity, interpretability, and a relatively lower risk of overfitting allow the proposed model to be readily used for passive travel data collection even on smartphones with limited computational capacities. The model is validated by a second dataset from Australia and New Zealand, which indicated that a decision tree with the GP constructed features as its input has a considerably higher transferability than SVMs and neural networks.https://doi.org/10.1049/itr2.12132Optimisation techniquesData handling techniquesAdministration of other service industriesOther topics in statisticsCombinatorial mathematicsNeural nets |
| spellingShingle | Farnoosh Namdarpour Mahmoud Mesbah Amir H. Gandomi Behrang Assemi Using genetic programming on GPS trajectories for travel mode detection IET Intelligent Transport Systems Optimisation techniques Data handling techniques Administration of other service industries Other topics in statistics Combinatorial mathematics Neural nets |
| title | Using genetic programming on GPS trajectories for travel mode detection |
| title_full | Using genetic programming on GPS trajectories for travel mode detection |
| title_fullStr | Using genetic programming on GPS trajectories for travel mode detection |
| title_full_unstemmed | Using genetic programming on GPS trajectories for travel mode detection |
| title_short | Using genetic programming on GPS trajectories for travel mode detection |
| title_sort | using genetic programming on gps trajectories for travel mode detection |
| topic | Optimisation techniques Data handling techniques Administration of other service industries Other topics in statistics Combinatorial mathematics Neural nets |
| url | https://doi.org/10.1049/itr2.12132 |
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