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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
27.08.2024
DOI
https://doi.org/10.7307/ptt.v36i4.537
ISSUE
2024 (Vol 36), Issue 4
LICENSE
Copyright (c) 2024 Huangqin HUANG, Jianhua GUO, Xiangyu SHI, Leixiao SHEN
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Data Source Importance Evaluation for Highway Networks: A Complex Network-Based Approach

Authors:Huangqin HUANG, Jianhua GUO, Xiangyu SHI, Leixiao SHEN

Abstract

Data collection technologies or data sources are critical for highway network management. However, due to the limitations on available management resources, determining the importance of these data sources is necessary to allocate these resources reasonably. This study proposes a complex network based method for evaluating the importance of multiple data sources in highway networks. This method includes mainly three steps. First, the business-data source relation will be identified and formulated for the highway network. Second, a business data source complex network is built from the previously identified business-data relationship. Third, an entropy weight method is used to compute and rank the importance of data source nodes by combining three indexes of degree centrality (DC), closeness centrality (CC) and structural holes (SC) computed based on the complex network. The proposed method is applied and illustrated using the highway network of Xuzhou City, Jiangsu Province, China. The results show that among the data sources, the most important data source is the continuous traffic survey station, followed by an automatic gantry-based station and vehicle detectors-based system. Discussions on the limitations, applications and future studies are provided for the proposed approach.

Keywords:highway network operations, data source, complex network, centrality, entropy weight method

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