Hybrid-Data Approach for Estimating Trip Purposes

Trip purpose information plays a significant role in transportation systems. Existing trip purpose information is traditionally collected through human observation. This manual process requires many personnel and a large amount of resources. Because of this high cost, automated trip purpose estimation is more attractive from a data-driven perspective, as it could improve the efficiency of processes and save time. Therefore, a hybrid-data approach using taxi operations data and point-of-interest (POI) data to estimate trip purposes was developed in this research. POI data, an emerging data source, was incorporated because it provides a wealth of additional information for trip purpose estimation. POI data, an open dataset, has the added benefit of being readily accessible from online platforms. Several techniques were developed and compared to incorporate this POI data into the hybrid-data approach to achieve a high level of accuracy. To evaluate the performance of the approach, data from Chengdu, China, were used. The results show that the incorporation of POI information increases the average accuracy of trip purpose estimation by 28% compared with trip purpose estimation not using the POI data. These results indicate that the additional trip attributes provided by POI data can increase the accuracy of trip purpose estimation.

• Record URL:
  https://doi.org/10.1177/03611981211018474
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Xiaoling Luo https://orcid.org/0000-0002-2113-7650 © National Academy of Sciences: Transportation Research Board 2021.
• Authors:
  • Luo, Xiaoling
  • 0000-0002-2113-7650
  • Cottam, Adrian
  • 0000-0001-5654-4347
  • Wu, Yao-Jan
  • 0000-0002-0456-7915
  • Jiang, Yangsheng
• Publication Date: 2021-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 545-553
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2675
  • Issue Number: 11
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Accuracy; Automatic data collection systems; Data files; Estimating; Taxicabs; Trip purpose
• Geographic Terms: Chengdu (China)
• Subject Areas: Highways; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01776734
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: Jul 20 2021 10:41AM