Parking choice behaviour analysis of rural residents based on the latent variable random forest model

The imbalance of rural parking supply and demand has a great impact on traffic congestion and environmental pollution,which has attracted the attention of many scholars as well as policymakers.However,most of the current research on parking choice focuses on urban business and residential areas rather than on rural parking choice behaviour,and focuses on the analysis of observable factors,ignoring the internal relationship with potential variables.This study considers the heterogeneity of individuals and uses the random forest(RF)algorithm to construct a model of rural residents’willingness to choose parking with both latent and explicit variables,to explore how much and in what ways individual characteristics and parking characteristics affect rural residents’parking choices,and to explore parking planning programmes and strategies that are truly applicable to rural areas.The results of the study suggest that the safety and convenience of the parking environment are key factors influencing the parking choice behaviour of rural residents,and can greatly improve the predictive accuracy of the parking willingness model.Upon comparison,it is found that the application of the RF algorithm is also significantly better than the logit model in terms of prediction effect,indicating that there is a nonlinear effect among the factors influencing the parking choice behaviour of rural residents and that the RF model with the addition of latent variables provides a better explanatory ability for the study of the parking choice behaviour of rural residents.

Situation and hotspot analysis on rural transport infrastructure from the perspective of bibliometric analysis

The construction of rural transportation infrastructure is of great significance to promote the organic integration of transportation construction with resource exploitation and industrial development in rural areas,and realize poverty alleviation and poverty reduction driven by convenient transportation.In order to understand the research status of rural transport infrastructure at home and abroad,this paper adopts CiteSpace,VOSviewer,Pajek,SPSS and other software and makes quantitative analysis and knowledge map drawing of the literature on rural transportation infrastructure collected in Web of Science database.The results show that the emphasis of this field is different in different periods,Chinese scholars'attention to this field increased significantly after the rural revitalization strategy was put forward.Based on the classification results of keyword clustering,the following conclusions are drawn:The planning mode of rural transportation infrastructure under rural revitalization is relatively backward and the lack of theoretical analysis of the root causes has achieved the purpose of rational allocation of transportation resources.Moreover,there are few studies on the mechanism and empirical analysis of transport infrastructure's effect on rural poverty reduction.From the analysis of research hotspots,the integration of urban and rural areas and rural road safety will be the main research direction in the future,and rural road accidents and other topics have research potential.

Identifying accident black spots based on the accident spacing distribution

The identification of accident black spots is of great significance for the prevention of traffic accidents.Commonly used accident black spot identification methods divide road sections for the analysis of accident data,the direct result of which is the splitting of accident black spots,which affects the results.This paper is based on three years of traffic accident data from the Beijing-Harbin Expressway,including the time and location of traffic accidents,form of the accident fatalities,severe injuries,slight injuries,and property damage only(PDO).To avoid road division effects,an identification method based on the accident spacing distribution is established by using quality control theory.The results show that the average number of accidents per kilometer by the method proposed in this paper is 42,which is much higher than 10,identified by other identification methods.The method proposed in this paper improves the accuracy of the identification results.This method avoids the problem of road segmentation found in other common methods and can more accurately reflect the spatial distribution of traffic accidents.Thus making the identification of accidents more scientific and accurate.