Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory Data
There has been a recent push towards using opportunistic sensing data collected from sources like automatic vehicle location (AVL) systems, mobile phone networks, and global positioning system (GPS) tracking to construct origin-destination (O-D) matrices, which are an effective alternative to expens...
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| Format: | Article |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8735796/ |
| _version_ | 1818855733090844672 |
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| author | Werabhat Mungthanya Santi Phithakkitnukoon Merkebe Getachew Demissie Lina Kattan Marco Veloso Carlos Bento Carlo Ratti |
| author_facet | Werabhat Mungthanya Santi Phithakkitnukoon Merkebe Getachew Demissie Lina Kattan Marco Veloso Carlos Bento Carlo Ratti |
| author_sort | Werabhat Mungthanya |
| collection | DOAJ |
| description | There has been a recent push towards using opportunistic sensing data collected from sources like automatic vehicle location (AVL) systems, mobile phone networks, and global positioning system (GPS) tracking to construct origin-destination (O-D) matrices, which are an effective alternative to expensive and time-consuming traditional travel surveys. These data have numerous drawbacks: they may have inadequate detail about the journey, may lack spatial and temporal granularity, or may be limited due to privacy regulations. Taxi trajectory data is an opportunistic sensing data type that can be effectively used for O-D matrix construction because it addresses the issues that plague other data sources. This paper presents a new approach for using taxi trajectory data to construct a taxi O-D matrix that is dynamic in both space and time. The model's origin and destination zone sizes and locations are not fixed, allowing the dimensions to vary from one matrix to another. Comparisons between these spatiotemporal-varying O-D matrices cannot be made using a traditional method like matrix subtraction. Therefore, this paper introduces a new measure of similarity. Our proposed approaches are applied to the taxi trajectory data collected from Lisbon, Portugal as a case study. The results reveal the periods in which taxi travel demand is the highest and lowest, as well as the periods in which the highest and lowest regular taxi travel demand patterns take shape. This information about taxi travel demand patterns is essential for informed taxi service operations management. |
| first_indexed | 2024-12-19T08:13:17Z |
| format | Article |
| id | doaj.art-a34b64120dc0457095d0146c56956c3b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:13:17Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a34b64120dc0457095d0146c56956c3b2022-12-21T20:29:35ZengIEEEIEEE Access2169-35362019-01-017777237773710.1109/ACCESS.2019.29222108735796Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory DataWerabhat Mungthanya0Santi Phithakkitnukoon1https://orcid.org/0000-0002-5716-9363Merkebe Getachew Demissie2Lina Kattan3Marco Veloso4Carlos Bento5Carlo Ratti6Department of Computer Engineering, Chiang Mai University, Chiang Mai, ThailandDepartment of Computer Engineering, Chiang Mai University, Chiang Mai, ThailandDepartment of Civil Engineering, University of Calgary, Calgary, AB, CanadaDepartment of Civil Engineering, University of Calgary, Calgary, AB, CanadaDepartment of Informatics Engineering, Center for Informatics and Systems, University of Coimbra, Coimbra, PortugalDepartment of Informatics Engineering, Center for Informatics and Systems, University of Coimbra, Coimbra, PortugalSENSEable City Laboratory, Massachusetts Institute of Technology, MA, USAThere has been a recent push towards using opportunistic sensing data collected from sources like automatic vehicle location (AVL) systems, mobile phone networks, and global positioning system (GPS) tracking to construct origin-destination (O-D) matrices, which are an effective alternative to expensive and time-consuming traditional travel surveys. These data have numerous drawbacks: they may have inadequate detail about the journey, may lack spatial and temporal granularity, or may be limited due to privacy regulations. Taxi trajectory data is an opportunistic sensing data type that can be effectively used for O-D matrix construction because it addresses the issues that plague other data sources. This paper presents a new approach for using taxi trajectory data to construct a taxi O-D matrix that is dynamic in both space and time. The model's origin and destination zone sizes and locations are not fixed, allowing the dimensions to vary from one matrix to another. Comparisons between these spatiotemporal-varying O-D matrices cannot be made using a traditional method like matrix subtraction. Therefore, this paper introduces a new measure of similarity. Our proposed approaches are applied to the taxi trajectory data collected from Lisbon, Portugal as a case study. The results reveal the periods in which taxi travel demand is the highest and lowest, as well as the periods in which the highest and lowest regular taxi travel demand patterns take shape. This information about taxi travel demand patterns is essential for informed taxi service operations management.https://ieeexplore.ieee.org/document/8735796/Dynamic origin-destination matrixadaptive zoning schemeorigin-destination matrix similarity measuretaxi trajectory datataxi travel demand |
| spellingShingle | Werabhat Mungthanya Santi Phithakkitnukoon Merkebe Getachew Demissie Lina Kattan Marco Veloso Carlos Bento Carlo Ratti Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory Data IEEE Access Dynamic origin-destination matrix adaptive zoning scheme origin-destination matrix similarity measure taxi trajectory data taxi travel demand |
| title | Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory Data |
| title_full | Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory Data |
| title_fullStr | Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory Data |
| title_full_unstemmed | Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory Data |
| title_short | Constructing Time-Dependent Origin-Destination Matrices With Adaptive Zoning Scheme and Measuring Their Similarities With Taxi Trajectory Data |
| title_sort | constructing time dependent origin destination matrices with adaptive zoning scheme and measuring their similarities with taxi trajectory data |
| topic | Dynamic origin-destination matrix adaptive zoning scheme origin-destination matrix similarity measure taxi trajectory data taxi travel demand |
| url | https://ieeexplore.ieee.org/document/8735796/ |
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