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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
28.06.2023
DOI
https://doi.org/10.7307/ptt.v35i3.104
ISSUE
2023 (Vol 35), Issue 3
LICENSE
Copyright (c) 2024 Yueer Gao, Yanqing Liao
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Urban Tourism Traffic Analysis Zone Division Based on Floating Car Data

Authors:Yueer Gao, Yanqing Liao

Abstract

Tourism traffic has a considerable influence on the state of urban traffic in tourist cities. To consider tourism traffic demand in the division of conventional traffic analysis zones (TAZ), a spatial analysis method combining dynamic traffic state features with static land use and road network characteristics is proposed to define tourism traffic analysis zones (TTAZs). Taking Xiamen Island as an example, first, point of interest (POI) data for the tourism elements on Xiamen Island and kernel density estimation (KDE) are applied to determine the core zones impacted by tourism traffic. Second, within the impacted zones, this paper studies the dynamic distribution of the tourism traffic for peak hours during holidays and non-tourism period by employing spatial autocorrelation method based on floating car data (FCD) and determines the area of slow traffic agglomeration of tourism traffic. In view of the distribution of tourism infrastructure, land use, tourism traffic state distribution and road network, this study identified the characteristics of slow traffic agglomeration in the area near Siming Road and divided four TAZs into six TTAZs. By further dividing the urban TTAZs, this paper hopes to provide a reference for urban traffic planning and management, tourism planning and land use planning.

Keywords:tourism analysis, traffic analysis zones division, spatial autocorrelation, FCD, POI data

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