Classification of Service Functions in the Life Circle of Beijing Subway Line 7

As a key node for the surrounding area,metro stations are closely connected with the surrounding urban space and develop cooperatively.Different types of metro stations have differences in land use and functional positioning.This paper mainly used the methods of Tyson polygon,kernel density analysis and correlation analysis,based on POI data,to classify the stations of Beijing Metro Line 7.This paper made a detailed analysis of commercial subway stations,and analyzed the distribution characteristics of commercial metro stations on Line 7.

Static Frame Model Validation with Small Samples Solution Using Improved Kernel Density Estimation and Confidence Level Method

An improved method using kernel density estimation （KDE） and confidence level is presented for model validation with small samples. Decision making is a challenging problem because of input uncertainty and only small samples can be used due to the high costs of experimental measurements. However, model validation provides more confidence for decision makers when improving prediction accuracy at the same time. The confidence level method is introduced and the optimum sample variance is determined using a new method in kernel density estimation to increase the credibility of model validation. As a numerical example, the static frame model validation challenge problem presented by Sandia National Laboratories has been chosen. The optimum bandwidth is selected in kernel density estimation in order to build the probability model based on the calibration data. The model assessment is achieved using validation and accreditation experimental data respectively based on the probability model. Finally, the target structure prediction is performed using validated model, which are consistent with the results obtained by other researchers. The results demonstrate that the method using the improved confidence level and kernel density estimation is an effective approach to solve the model validation problem with small samples.

GIS-based spatial analysis of urban traffic accidents： Case study in Mashhad, Iran

There is a growing concern in traffic accident rate in recent years. Using Mashhad city （Iran second populous city） traffic accident records as case study, this paper applied the combi- nation of geo-information technology and spatial-statistical analysis to bring out the influence of spatial factors in their formation. The aim of the study is to examine 4 clustering analyses to have a better understanding of traffic accidents patterns in complex urban network. In order to deploy the clustering technique in urban roads, 9331 point features for inner city traffic accidents during 12 months have been registered according to their x and y location in geographic information system （GIS）. The mentioned areas were analyzed by kernel density estimation （KDE） using ARCMAP and two other analyses using SANET 4th edition software so that the results of network analysis can be compared with traditional KDE method. In addi- tion, this research introduces five classifications for determining the eventfulness of the under study area based on standard deviation and to make priority in creating security in the area. The nearest neighbor and K-function output analysis consist of four curves and regarding the fact that for all fatal, injury and property damage only crashes, the observed value curve is above the 5% confidence interval. Accidents in the study region are more clustered than ex- pected by random chance. The importance of this study is to use GIS as a management system for accident analysis by combination of spatial-statistical methods.