Optimizing taxi route planning based on taxi trajectory data analysis

Paper

Yang, Xinyi, Chen, Zhi and Luo, Yadan. 2023. "Optimizing taxi route planning based on taxi trajectory data analysis." 34th Australasian Database Conference (ADC 2023). Melbourne, Australia 01 202 - 03 Nov 2023 Switzerland . Springer. https://doi.org/10.1007/978-3-031-47843-7_4
Paper/Presentation Title

Optimizing taxi route planning based on taxi trajectory data analysis

Presentation TypePaper
AuthorsYang, Xinyi, Chen, Zhi and Luo, Yadan
Journal or Proceedings TitleProceedings of the 34th Australasian Database Conference (ADC 2023)
Journal Citation14386, pp. 44-45
Number of Pages12
Year2023
PublisherSpringer
Place of PublicationSwitzerland
ISSN1611-3349
0302-9743
ISBN9783031478420
9783031478437
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-031-47843-7_4
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-3-031-47843-7_4
Web Address (URL) of Conference Proceedingshttps://link.springer.com/book/10.1007/978-3-031-47843-7
Conference/Event34th Australasian Database Conference (ADC 2023)
Event Details
34th Australasian Database Conference (ADC 2023)
Parent
Australasian Database Conference
Delivery
In person
Event Date
01 Nov 0202 to end of 03 Nov 2023
Event Location
Melbourne, Australia
Abstract

In daily life, taxis have become one of the most common and convenient ways of public transportation. With the advancement of positioning and mobility technologies, a large amount of taxi trajectory data has been collected, providing valuable data resources for urban planning, traffic management, and personalized route recommendations. However, these huge datasets also pose computational and processing challenges. This study uses the annual taxi trajectory data of Porto City obtained from the Kaggle platform, containing more than 1.7 million records, to study data query and analysis in a big data environment. We focus on comparing the efficiency and overhead of two spatial index structures, K-d tree and R-tree, in handling such large-scale datasets. Experimental results show that the K-d tree has a time-efficiency advantage in K-nearest neighbours query tasks, while the R-tree performs better in complex spatial query tasks. These findings provide important references for taxi route planning and other big data applications, especially in scenarios requiring efficient and accurate data retrieval.

Keywordstaxi trajectory data; K-nearest neighbours; Python; PostgreSQL; R-tree; K-d tree; route planning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
Public Notes

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Volume14386
SeriesLecture Notes in Computer Science
Byline AffiliationsUniversity of Queensland
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