基于CART改进模型的驾驶员路径选择行为估计方法

针对目前驾驶员路径选择估计精度不高的问题,考虑地区、城镇类型、性别、年龄、是否已婚、学历、职业、是否从事全职工作、收入水平、交通拥挤程度、排队长度、延误时间、道路熟悉程度、路径长度、替代路径节省时间等多方面因素,设计了一个驾驶员路径选择行为调查方案,并对驾驶员群体开展了网上调查.利用Logit模型及Probit模型分析了驾驶员路径选择行为的影响因素,得到性别、年龄、是否从事全职工作、延误时间、道路熟悉程度、路径长度、道路拥挤程度等因素的影响显著.利用改进的分类树(classification and regression tree,CART)模型设计了驾驶员路径选择行为估计模型,重点针对驾驶员路径选择行为的特点对传统CART模型的递归划分与剪枝2个主要算法进行了改进研究.样本测试结果表明:模型的估计精度可达82%,相比现有模型的估计精度至少提高了6%.研究成果可为交通诱导方案的制定提供有效的技术支持.

Drivers Route Choice Behaviors Estimating Method Based on CART Improved Model

In order to solve the problem of the low precision of drivers' route choice estimation, a stated preference survey was conducted on the internet to collect various drivers' route choice behaviors. Based on the findings from the surveys, seventeen potential affecting factors such as city type, region, gender, age, marital status, degree, job, full-time or non-full-time employees, monthly income, crowded level of the current route, vehicle queue length of the current route, delay ratio of the current route, knowledge of an alternate route, length ratio of an alternate route, crowded level of an alternate route, anticipated travel time saving ratio and quality of dynamic travel information were identified and applied to further study. A logit model and a probit model were adopted to evaluate the significance of these factors. Gender, age, full-time or non-full-time employees, delay ratio of the current route, knowledge of an alternate route, length ratio of an alternate route, and crowded level of an alternate route were proved to be significant variables. Then a classification and regression tree ( CART) improved model for estimating drivers' route choice behaviors was built based on the significant variables, where recursive partitioning and pruning algorithms were focus on improved. The verification results showed that the model' estimating precision reaches 82%, which is 6% higher than that of the existing models. The research achievements in this paper can provide technical supports for making traffic induction plans.