Dynamic Grid-Based Spatial Density Visualization and Rail Transit Station Prediction

Autor: Zhi Cai, Meilin Ji, Qing Mi, Bowen Yang, Xing Su, Limin Guo, Zhiming Ding
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: ISPRS International Journal of Geo-Information, Vol 10, Iss 12, p 804 (2021)
Druh dokumentu: article
ISSN: 2220-9964
DOI: 10.3390/ijgi10120804
Popis: The urban rail transit stations are an important part of an urban transit system. Scientific and reasonable location of rail transit station can greatly alleviate traffic pressure. The number of people in the surrounding area of a rail transit station is an important factor for site selection. However, it is difficult to obtain the spatial distribution of population, which brings great difficulties in terms of site selection. Due to the large-scale popularization of AP (Access Point) in China, the spatial distribution of AP is used instead of population distribution to assist site selection. Therefore, a density visualization method based on a dynamic grid is proposed, which can help decision-makers intuitively see the AP density of the uncovered grid of rail transit stations, and then cluster the AP density of the uncovered area to predict the location of new rail transit stations. The validity of the proposed method is demonstrated by using the AP dataset and rail transit data of Beijing in 2013. The results show that our method has high accuracy in predicting the location of rail transit stations. It can provide data support for urban traffic development and management.
Databáze: Directory of Open Access Journals