基于多项罗吉特模型的路网形态判别

Street Patterns Distinction Based on Logit Model

摘要:

以718个交通分析小区作为研究对象，根据路网形态特征将其分成5类，并分别计算每个小区路网的6种定量指标.研究结果表明，网状指数、断头路比例和四肢交叉口比例三个指标对不同路网形态下均具有较好的筛选性.最后，基于上述指标建立了量化判断路网形态的多项罗吉特模型，实例验证结果表明，该模型的准确性达到88.4%，相较于人工判断提高了3.0%.提供了一种量化判别路网形态的方法，为研究路网形态对交通的影响提供了帮助.

Abstract:

In this study, 718 traffic analysis zones were classified into 5 groups according to the features of the network. Then, 6 quantitative indexes of different road networks were calculated. It was concluded that Meshedness, proportion of cul de sacs and proportion of four leg intersection were the best measure to distinguish and describe various street patterns. At last, the multinomial logit model was developed based on the above mentioned indices to quantitatively distinguish street patterns, and the accuracy of the model was proved to be 88.4%, which was 3.2% higher than that of the visual inspection. This paper offers an approach to quantitatively distinguish street patterns, which can be used to study the relationship between street patterns and traffic performance.