Enriching smart card data with the trip purpose attribute
Planning public transport highly relies on the availability, quantity and quality of travel demand data of passengers. In the last two decades, smart card data has provided the opportunity to create comprehensive travel demand data as a byproduct of a fare-collecting system. One important attribute...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-01-01
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| Series: | Journal of Public Transportation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1077291X23000334 |
| _version_ | 1797647650827272192 |
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| author | Hamed Faroqi Alireza Saadatmand Mahmoud Mesbah Ali Khodaii |
| author_facet | Hamed Faroqi Alireza Saadatmand Mahmoud Mesbah Ali Khodaii |
| author_sort | Hamed Faroqi |
| collection | DOAJ |
| description | Planning public transport highly relies on the availability, quantity and quality of travel demand data of passengers. In the last two decades, smart card data has provided the opportunity to create comprehensive travel demand data as a byproduct of a fare-collecting system. One important attribute for the planning is the purpose of the trips, which is missing from the smart card data. This research study proposes and formulates a novel method to infer trip purpose in smart card data. Previous methods either lack the concept of trip chains or did not consider both spatial and temporal perspectives of a trip. Firstly, this method discovers relations between the sequence and temporal attributes of trips with their trip purpose attribute by running a clustering method on a rich travel survey dataset (This study only uses public transit records.) that contains all attributes. Secondly, the discovered clusters are labelled and transferred to the smart card data by calculating the closeness of the trip chain of each individual in the smart card data to the clusters. Thirdly, the proportion of relevant land use types near the destination of each trip is utilized to enhance the previously calculated closeness. The proposed method is implemented on datasets from South East Queensland, Australia. Also, two recently published methods were replicated and run on the same datasets to evaluate the proposed method. The results show improvements in the proposed method compared to the existing methods of the literature. |
| first_indexed | 2024-03-11T15:19:36Z |
| format | Article |
| id | doaj.art-e0becbd98f9e4d06bb633a560399c1a9 |
| institution | Directory Open Access Journal |
| issn | 2375-0901 |
| language | English |
| last_indexed | 2024-03-11T15:19:36Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Public Transportation |
| spelling | doaj.art-e0becbd98f9e4d06bb633a560399c1a92023-10-29T04:19:19ZengElsevierJournal of Public Transportation2375-09012023-01-0125100072Enriching smart card data with the trip purpose attributeHamed Faroqi0Alireza Saadatmand1Mahmoud Mesbah2Ali Khodaii3School of Civil Engineering, The University of Kurdistan, Iran; Department of Civil and Environmental Engineering, Amirkabir University of Technology, Iran; Corresponding author at: School of Civil Engineering, The University of Kurdistan, Iran.Department of Civil and Environmental Engineering, Amirkabir University of Technology, IranDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, Iran; School of Civil Engineering, The University of Queensland, AustraliaDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, IranPlanning public transport highly relies on the availability, quantity and quality of travel demand data of passengers. In the last two decades, smart card data has provided the opportunity to create comprehensive travel demand data as a byproduct of a fare-collecting system. One important attribute for the planning is the purpose of the trips, which is missing from the smart card data. This research study proposes and formulates a novel method to infer trip purpose in smart card data. Previous methods either lack the concept of trip chains or did not consider both spatial and temporal perspectives of a trip. Firstly, this method discovers relations between the sequence and temporal attributes of trips with their trip purpose attribute by running a clustering method on a rich travel survey dataset (This study only uses public transit records.) that contains all attributes. Secondly, the discovered clusters are labelled and transferred to the smart card data by calculating the closeness of the trip chain of each individual in the smart card data to the clusters. Thirdly, the proportion of relevant land use types near the destination of each trip is utilized to enhance the previously calculated closeness. The proposed method is implemented on datasets from South East Queensland, Australia. Also, two recently published methods were replicated and run on the same datasets to evaluate the proposed method. The results show improvements in the proposed method compared to the existing methods of the literature.http://www.sciencedirect.com/science/article/pii/S1077291X23000334Data fusionPublic transportClusteringBig dataMobility data |
| spellingShingle | Hamed Faroqi Alireza Saadatmand Mahmoud Mesbah Ali Khodaii Enriching smart card data with the trip purpose attribute Journal of Public Transportation Data fusion Public transport Clustering Big data Mobility data |
| title | Enriching smart card data with the trip purpose attribute |
| title_full | Enriching smart card data with the trip purpose attribute |
| title_fullStr | Enriching smart card data with the trip purpose attribute |
| title_full_unstemmed | Enriching smart card data with the trip purpose attribute |
| title_short | Enriching smart card data with the trip purpose attribute |
| title_sort | enriching smart card data with the trip purpose attribute |
| topic | Data fusion Public transport Clustering Big data Mobility data |
| url | http://www.sciencedirect.com/science/article/pii/S1077291X23000334 |
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