土地利用与交通综合模型中的高频数据:来源与应用综述

Thivya P. Amalan , Bo Du , Amal S. Kumarage , Rohan Wickramasuriya , Pascal Perez
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引用次数: 0

摘要

城市的快速城市化及其相关的复杂性要求使用LUTI模型等复杂的决策支持工具来帮助交通和土地利用的平衡和可持续发展。从文献研究中可以明显看出,大多数LUTI模型需要大量的数据,这使得它们既昂贵又耗时,这对发展中国家来说将是一个挑战。另一方面,随着信息和通信技术的出现,高频数据的可用性正在增加,这些数据可以经常以很少或没有成本的方式在分类级别收集数据。本研究的主要重点是试图弥合这类数据的研究与它在实际环境中的后续应用之间存在的差距。在此基础上,本文着重于(1)总结现有的LUTI模型及其相应的数据需求;(2) 解释LUTI建模中HF数据的来源;以及(3)讨论在LUTI建模中实现这些数据的应用和挑战。这篇综述确定了最近的技术发展和HFD的可用性可以填补LUTI数据可用性的空白,许多作者在文献中对此进行了讨论。此外,这种新的数据来源可以增加LUTI模型的潜力,特别是在发展中国家,这些国家的土地利用和运输模式正在迅速变化,传统形式的数据收集成本高昂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High frequency data in Land use and transport integrated model: A review of sources and application

The rapid urbanisation in cities, and its associated complexities demand that sophisticated decision support tools such as the LUTI models be employed to assist the balanced and sustainable development of transport and land use. It is evident from literature studies that the majority of LUTI models need extensive data, making them expensive and time-consuming, which will be a challenge for developing countries. On the other hand, with the advent of information and communication technology, the availability of high frequency (HF) data is increasing which can be collected at disaggregated level data frequently at little or no cost. The main focus of this research is trying to bridge the gap that exists between research with this type of data and it subsequent application in real context. On the basis of this, the paper focuses on (1) summarizing existing LUTI models and their corresponding data requirements; (2) explaining the sources of HF data in LUTI modelling; and (3) discussing the applications and challenges in implementation of such data in LUTI modelling. This review identifies the recent development of technology and availability of HFD can fill the gap of data availability for LUTI which has been discussed by a great number of authors in literature. Furthermore this kind of novel source of data can increase the potential of LUTI model particularly in developing countries, where land use and transport patterns are changing rapidly and where traditional forms of data are expensive to collect.

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