gtfs2net: Extraction of General Transit Feed Specification Data Sets to Abstract Networks and Their Analysis.

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Big Data Pub Date : 2025-02-01 Epub Date: 2023-04-24 DOI:10.1089/big.2022.0269

Gergely Kocsis, Imre Varga

引用次数: 0

Abstract

Mass transportation networks of cities or regions are interesting and important to be studied to get a picture of the properties of a somehow better topology and system of transportation. One way to do this lies on the basis of spatial information of stations and routes. As we show however interesting findings can be gained also if one studies the abstract network topologies of these systems. To get these abstract types of networks, we have developed a tool that can extract a network of connected stops from General Transit Feed Specification feeds. As we found during the development, service providers do not follow the specification in coherent ways, so as a kind of postprocessing we have introduced virtual stations to the abstract networks that gather close stops together. We analyze the effect of these new stations on the abstract map as well.

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gtfs2net:抽象网络中通用传输馈电规范数据集的提取及其分析。

城市或地区的大众交通网络是一个有趣且重要的研究对象，它可以帮助我们了解更好的交通拓扑和交通系统的特性。其中一种方法是基于车站和路线的空间信息。然而，正如我们所展示的，如果研究这些系统的抽象网络拓扑结构，也可以获得有趣的发现。为了获得这些抽象类型的网络，我们开发了一个工具，可以从通用运输馈送规范馈送中提取连接站点的网络。我们在开发过程中发现，服务提供商没有以连贯的方式遵循规范，因此作为一种后处理，我们将虚拟站点引入到将紧密站点聚集在一起的抽象网络中。我们还分析了这些新站点对抽象地图的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

9.10

自引率

2.20%

发文量

期刊介绍： Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.