基于条件自回归模型的城市宏观安全分析

Macro level Safety Analysis in Urban Using Conditional Autoregressive Model

摘要:

基于上海市外环以内263个交通分析小区的事故、道路、交通、土地利用数据，在交通分析小区层面建立贝叶斯负二项条件自回归模型，分析事故在交通分析小区层面的显著影响因素.结果表明，主、次干道长度的增加会显著增加交通分析小区内的事故数量；道路网密度、交叉口数量与小区事故数具有显著正相关性；随着客车产生量的增加，事故数量增加；土地利用强度高，相应的事故数量增多.

Abstract:

This study collected crash data, roadway features, traffic characteristics and land use data in 263 traffic analysis zones (TAZ) within the Outer Ring in Shanghai. TAZ level Bayesian Negative Binomial Conditional Autoregression model was established. Modeling results show that the frequency of total crashes in each TAZ can increase with longer arterials and minor arterials. The numbers of intersections and road density are positively correlated with crashes. More crashes occur with the increasing of car production and land development intensity.