摘要
差异化收费作为调控高速公路流量时空分布的手段,研究该条件下货车出行行为可为制定差异化收费方案提供量化分析依据。利用甘肃省张掖至酒泉段货车出行意向调查数据,建立MNL模型、路径选择分别位于上下层的2种NL模型以及CNL模型,标定结果表明CNL模型拟合精度最高。对CNL模型进行选择枝效用弹性分析,利用标定后的选择模型结合现状数据进行模拟,对优惠组合条件-流量曲面进行梯度分析。经弹性系数分析,夜间-非收费公路选择枝的直接弹性系数为-0.044与-0.13,说明夜间行驶于非收费公路的货车驾驶员更易因效用函数变化改选其他方案;经曲面梯度分析,可得当折扣组合坐标为(0.318,0.354)时梯度的模取得最大值,即优惠额在白天31.8%、夜间35.4%时对货车吸引效率最高,优惠比例超过此优惠组合时吸引效率将逐渐递减。
Differentiated charge is a method to regulate the spatio-temporal distribution of expressway traffic. Behaviors of trucks under this condition are studied to provide quantitative analysis basis for formulation of differentiated charge schemes. A MNL model and two NL models are developed with path selection in upper and lower layers;and a CNL model is developed by using SP survey data of trucks from Zhangye to Jiuquan in Gansu Province. The calibration results show that the CNL model has the highest fitting precision. Then the CNL model is selected for elastic analysis of the alternatives. Finally, a calibrated selection model combined with the actual data is used for simulation. Gradient analysis is used for preferential combination condition-flow surface. According to the analysis of elastic coefficient, the direct elastic coefficients of night-non-toll road alternatives are -0.044 and -0.13, which indicates that the truck drivers who drive on non-toll roads at night are more likely to re-elect other options due to changes in utility functions. Through the surface gradient analysis, the maximum modulus of the gradient are obtained when the discount combination coordinate is (0.318, 0.354), that is, the discount amount has the highest attraction to trucks when it is 31.8% during the day and 35.4% at night. When the discount ratio exceeds this preferential combination, the attraction efficiency will gradually decrease.
作者
肖清榆
袁振洲
吴玥琳
吴先宇
买媛媛
XIAO Qingyu;YUAN Zhenzhou;WU Yuelin;Wu Xianyu;MAI Yuanyuan(Key Laboratory of Industry of Big Data Application Technologies for ComprehensiveTransport, Beijing Jiaotong University, Beijing 100044, China;School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;China Academy of Transportation Science, Beijing 100013, China)
出处
《交通信息与安全》
CSCD
北大核心
2019年第5期133-140,共8页
Journal of Transport Information and Safety
基金
国家重点基础研究发展计划项目(2012CB725403)
北京市自然科学基金项目(9132015)资助