A trip detection model for individual smartphone-based GPS records with a novel evaluation method
Personal travel pattern is significant to transportation analysis and modeling, and the rapid development of in-depth application of location-based services makes it possible to obtain large-scale positioning data. So, it is crucial to develop proper algorithm to identify trips/trip-segments from in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2017-06-01
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| Series: | Advances in Mechanical Engineering |
| Online Access: | https://doi.org/10.1177/1687814017705066 |
| _version_ | 1818059585998553088 |
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| author | Bao Wang Linjie Gao Zhicai Juan |
| author_facet | Bao Wang Linjie Gao Zhicai Juan |
| author_sort | Bao Wang |
| collection | DOAJ |
| description | Personal travel pattern is significant to transportation analysis and modeling, and the rapid development of in-depth application of location-based services makes it possible to obtain large-scale positioning data. So, it is crucial to develop proper algorithm to identify trips/trip-segments from individual positioning records. This article presents an automatic trips/trip-segment detection method based on instantaneous Global Positioning System records collected by smartphones. The method consists of a series of procedures including data cleaning and pre-processing, inferring and removing pseudo trip ends, as well as trip combination. The result of the model has been compared with the “ground truth” collected and verified by volunteers. Finally, 1954 trips from 125 volunteers were identified and the overall detection accuracy is between 97.5% and 98.7% with a 95% confidence level. Besides, purity was introduced to evaluate the performance of the proposed method. In addition, the integration of instantaneous speed over time shows an excellent performance in calculating the trip distance. |
| first_indexed | 2024-12-10T13:18:52Z |
| format | Article |
| id | doaj.art-26e0e68ef896488bb65a06a193cc5e9d |
| institution | Directory Open Access Journal |
| issn | 1687-8140 |
| language | English |
| last_indexed | 2024-12-10T13:18:52Z |
| publishDate | 2017-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Advances in Mechanical Engineering |
| spelling | doaj.art-26e0e68ef896488bb65a06a193cc5e9d2022-12-22T01:47:25ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402017-06-01910.1177/1687814017705066A trip detection model for individual smartphone-based GPS records with a novel evaluation methodBao Wang0Linjie Gao1Zhicai Juan2China Institute of Urban Govemance, Shanghai Jiao Tong University, Shanghai, ChinaChina Institute of Urban Govemance, Shanghai Jiao Tong University, Shanghai, ChinaAntai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, ChinaPersonal travel pattern is significant to transportation analysis and modeling, and the rapid development of in-depth application of location-based services makes it possible to obtain large-scale positioning data. So, it is crucial to develop proper algorithm to identify trips/trip-segments from individual positioning records. This article presents an automatic trips/trip-segment detection method based on instantaneous Global Positioning System records collected by smartphones. The method consists of a series of procedures including data cleaning and pre-processing, inferring and removing pseudo trip ends, as well as trip combination. The result of the model has been compared with the “ground truth” collected and verified by volunteers. Finally, 1954 trips from 125 volunteers were identified and the overall detection accuracy is between 97.5% and 98.7% with a 95% confidence level. Besides, purity was introduced to evaluate the performance of the proposed method. In addition, the integration of instantaneous speed over time shows an excellent performance in calculating the trip distance.https://doi.org/10.1177/1687814017705066 |
| spellingShingle | Bao Wang Linjie Gao Zhicai Juan A trip detection model for individual smartphone-based GPS records with a novel evaluation method Advances in Mechanical Engineering |
| title | A trip detection model for individual smartphone-based GPS records with a novel evaluation method |
| title_full | A trip detection model for individual smartphone-based GPS records with a novel evaluation method |
| title_fullStr | A trip detection model for individual smartphone-based GPS records with a novel evaluation method |
| title_full_unstemmed | A trip detection model for individual smartphone-based GPS records with a novel evaluation method |
| title_short | A trip detection model for individual smartphone-based GPS records with a novel evaluation method |
| title_sort | trip detection model for individual smartphone based gps records with a novel evaluation method |
| url | https://doi.org/10.1177/1687814017705066 |
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