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Binary Probit Model on Drivers Route Choice Behaviors Based on Multiple Factors Analysis

机译:基于多因素分析的司机路由选择行为的二进制探测模型

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This study explores many details of the drivers response to dynamic travel information with variable message signs (VMS) which is the one of the most common advanced traveler information systems (ATIS) deployed in many areas all over the world. A stated preference (SP) survey was conducted to collect various drivers route choice behavior with VMS. Based on the surveys, seventeen potential affecting factors such as city kind, region, gender, age, marital status, degree, job, whether full-time worker, monthly income, crowded level on 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 on an alternate route, anticipated travel time saving ratio and quality of dynamic travel information were identified and applied to further study.Abinary probitmodel was adopted to evaluate the significance of these seventeen factors. Gender, age, whether full-time worker, delay ratio of the current route, knowledge of an alternate route, length ratio of an alternate route, and crowded level on an alternate route were proved to be significant variables. Then a model for estimating drivers route choice results was build based on the significant variables. The verification results showed that themodel estimating precision could reached 76%.
机译:本研究探讨了具有可变消息标志(VMS)的动态旅行信息的驾驶员响应的许多细节,这是在世界各地的许多领域部署的最常见的高级旅行者信息系统(ATIS)之一。进行了一个已说明的偏好(SP)调查,以利用VM收集各种驾驶员路由选择行为。基于调查,十七次潜在影响因素,如城市,地区,性别,年龄,婚姻状况,学位,工作,无论是全职工人,每月收入,拥挤水平在目前的路线,车队队列的当前路线,当前路线的延迟比,替代路线的知识,替代路线的长度比,替代路线上拥挤的水平,识别出的预期行进时间和动态旅行信息的质量,并应用进一步研究。同意概率是采用评估十七个因素的意义。性别,年龄,无论是全职工作人员,当前路线的延迟比,替代路线的知识,替代路线的长度比以及替代路线的拥挤水平都被证明是显着的变量。然后,用于估算驱动程序路由选择结果的模型是基于显着变量构建的。验证结果表明,Themodel估算精度可达76%。

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