Abstract
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.
Highlights
Taking the model for weekday crashes as an example, model 1, model 3, model 5, model 7, and model 9 were developed based on ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), and multi-scale geographically weighted regression (MGWR)
As the local spatial regression model, the local coefficient estimates generated by MGWR can reflect the spatial heterogeneity in the process of Intercept
As the local spatial regression model, the local coefficient estimates generated by MGWR can reflect the spatial heterogeneity in the process of influencing traffic crashes
Summary
According to the World Health Organization, each year, road traffic crashes cause about 1.35 million deaths and 50 million injuries worldwide [1]. Road traffic injuries are estimated to be the leading cause of death across all age groups besides diseases, which is why road environment improvements are urgently needed. The development of geographic information technology provides considerable opportunities to better characterize traffic crashes, develop effective proposals, and provide technical assistance. To better understand the influence mechanism of traffic crashes and improve urban traffic safety, regression models are usually constructed to study the impact of different contributing factors on traffic crashes
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