Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression

Some studies on the impact of traditional land use factors on traffic crashes do not take into account the limitations of spatial heterogeneity and spatial scale. To overcome these limitations this study presents a systematic method based on multi-scale geographically weighted regression (MGWR), whi...

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Autores principales:	Xinyu Qu, Xinyan Zhu, Xiongwu Xiao, Huayi Wu, Bingxuan Guo, Deren Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	weekday crashes weekend crashes point of interest multi-scale geographically weighted regression road traffic safety Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/6b64edba63054c388940f327ce0cab8d
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spelling	oai:doaj.org-article:6b64edba63054c388940f327ce0cab8d2021-11-25T17:53:17ZExploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression10.3390/ijgi101107912220-9964https://doaj.org/article/6b64edba63054c388940f327ce0cab8d2021-11-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/791https://doaj.org/toc/2220-9964Some studies on the impact of traditional land use factors on traffic crashes do not take into account the limitations of spatial heterogeneity and spatial scale. To overcome these limitations this study presents a systematic method based on multi-scale geographically weighted regression (MGWR), which considers spatial heterogeneity and spatial scale differences of different influencing factors, to explore the influence of reclassified points-of-interest (POI) on traffic crashes occurring on weekdays and weekends. Experiments were conducted on 442 communities in Hankou, Wuhan, and the performance of the proposed method was compared against traditional methods based on ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), and geographically weighted regression (GWR). The experiments show that the proposed method yielded the best fitness of models and more accurate model results of local coefficient estimates. The highlights of the results are as follows: There are differences in the scale of the predictor variables. Residential POI, scenic POI, and transportation POI have a global effect on traffic crashes. Commercial service POI and industrial POI affects traffic crashes at the regional scale, while public service POI affects crashes at the local scale. The local coefficient estimates from residential POI and scenic POI have little impact on traffic crashes. During weekdays, more transportation POI in the entire study area leads to more traffic crashes. While on weekends, transportation POI has a significant positive effect on crashes only in some communities. The local coefficient estimates for industrial POI vary at different periods. Commercial service POI and public service POI may increase the risk of crashes in some communities, which can be observed on weekdays and weekends. Exploring the influence of POI on traffic crashes at different periods is helpful for traffic management strategies and in reducing traffic crashes.Xinyu QuXinyan ZhuXiongwu XiaoHuayi WuBingxuan GuoDeren LiMDPI AGarticleweekday crashesweekend crashespoint of interestmulti-scale geographically weighted regressionroad traffic safetyGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 791, p 791 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	weekday crashes weekend crashes point of interest multi-scale geographically weighted regression road traffic safety Geography (General) G1-922
spellingShingle	weekday crashes weekend crashes point of interest multi-scale geographically weighted regression road traffic safety Geography (General) G1-922 Xinyu Qu Xinyan Zhu Xiongwu Xiao Huayi Wu Bingxuan Guo Deren Li Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression
description	Some studies on the impact of traditional land use factors on traffic crashes do not take into account the limitations of spatial heterogeneity and spatial scale. To overcome these limitations this study presents a systematic method based on multi-scale geographically weighted regression (MGWR), which considers spatial heterogeneity and spatial scale differences of different influencing factors, to explore the influence of reclassified points-of-interest (POI) on traffic crashes occurring on weekdays and weekends. Experiments were conducted on 442 communities in Hankou, Wuhan, and the performance of the proposed method was compared against traditional methods based on ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), and geographically weighted regression (GWR). The experiments show that the proposed method yielded the best fitness of models and more accurate model results of local coefficient estimates. The highlights of the results are as follows: There are differences in the scale of the predictor variables. Residential POI, scenic POI, and transportation POI have a global effect on traffic crashes. Commercial service POI and industrial POI affects traffic crashes at the regional scale, while public service POI affects crashes at the local scale. The local coefficient estimates from residential POI and scenic POI have little impact on traffic crashes. During weekdays, more transportation POI in the entire study area leads to more traffic crashes. While on weekends, transportation POI has a significant positive effect on crashes only in some communities. The local coefficient estimates for industrial POI vary at different periods. Commercial service POI and public service POI may increase the risk of crashes in some communities, which can be observed on weekdays and weekends. Exploring the influence of POI on traffic crashes at different periods is helpful for traffic management strategies and in reducing traffic crashes.
format	article
author	Xinyu Qu Xinyan Zhu Xiongwu Xiao Huayi Wu Bingxuan Guo Deren Li
author_facet	Xinyu Qu Xinyan Zhu Xiongwu Xiao Huayi Wu Bingxuan Guo Deren Li
author_sort	Xinyu Qu
title	Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression
title_short	Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression
title_full	Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression
title_fullStr	Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression
title_full_unstemmed	Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression
title_sort	exploring the influences of point-of-interest on traffic crashes during weekdays and weekends via multi-scale geographically weighted regression
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6b64edba63054c388940f327ce0cab8d
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Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression

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