基于有限混合零截尾事故预测模型的事故多发段判别方法

为了准确判别事故多发段,有针对性地提出安全应对措施以提升道路交通的安全水平,针对零值缺失交通事故数据并考虑其异质性特点,在单零截尾负二项(ZTNB)模型的基础上建立有限混合零截尾事故预测模型(FMZTNB)。应用R软件对单零截尾负二项模型中的参数进行估计,采用马尔科夫链蒙特卡洛算法(MCMC)对FMZTNB预测模型参数进行求解,并采用Gelman-Rubin收敛统计量对抽样结果进行检查。选择事故风险水平分别为低、中和高的9个路段,分别用2种模型对交通事故次数进行预测。综合观测到的事故次数和相应的事故预测模型结果,采用经验贝叶斯方法对事故相对多发段进行判别。最后采用事故次数一致性检验、判别点段一致性检验和排序一致性检验3种检验方式对判别结果对比分析。结果表明:基于事故率的事故相对多发段判别方法存在较大的不一致性,基于零截尾负二项预测模型的路段事故相对多发判别结果明显优于基于传统负二项预测模型的结果。整体上,基于有限混合零截尾事故预测模型的事故相对多发路段的判别结果高于基于单零截尾负二项分布模型的判别结果。     

共享单车出行空间异质性特征及驱动因素研究

为实现基于轨迹数据挖掘的共享单车出行空间异质性特征及其驱动因素评估,本文应用核密度分析和热点探测,获取采样分析区域并以热力值表征共享单车出行发生量,减少尺度效应的干扰;引入空间统计学的半变异函数模拟共享单车出行发生量的结构性和随机性变化规律,挖掘空间异质性特征,确定邻域效应的尺度范围;利用空间序列的斜率表征变化趋势,同时,结合改进的空间滞后和残差模型,区分土地利用、邻域效应和其他建成环境各自对共享单车出行空间异质性特征的驱动力。以北京市为案例进行分析,结果表明：北京市的共享单车出行存在中等的、正的空间自相关性,空间异质性特征的最佳拟合模型为指数模型;空间自相关性的衰减半径为1860 m,大于此距离时邻域效应消失;建成环境对空间异质性特征的相对驱动力最大,邻域效应对其的相对驱动力则处于中间水平,而土地利用对其的相对驱动力最小。     

铁路出发旅客衔接方式选择的复合异质性模型

鉴于多属性决策中存在的属性偏好和处理两类异质性现象,以铁路出发旅客衔接方式选择问题为对象,对两类异质性的效用表达进行了讨论,构建了能同时描述旅客属性偏好和处理异质性的复合异质性(Hybrid)非集计模型.采用成都东客站SP调查数据进行模型标定,结果表明,Hybrid模型的拟合度优于单一异质性模型.旅客属性处理规则自述结果与Hybrid模型分类结果的对比分析表明,模型对旅客属性处理特征的表达准确.进一步对不同类型旅客属性标定结果的分析显示,Hybrid模型可实现对衔接方式选择行为的精细化描述.     

Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles

Autonomous vehicles use sensing and communication technologies to navigate safely and efficiently with little or no input from the driver. These driverless technologies will create an unprecedented revolution in how people move, and policymakers will need appropriate tools to plan for and analyze the large impacts of novel navigation systems. In this paper we derive semiparametric estimates of the willingness to pay for automation. We use data from a nationwide online panel of 1260 individuals who answered a vehicle-purchase discrete choice experiment focused on energy efficiency and autonomous features. Several models were estimated with the choice microdata, including a conditional logit with deterministic consumer heterogeneity, a parametric random parameter logit, and a semiparametric random parameter logit. We draw three key results from our analysis. First, we find that the average household is willing to pay a significant amount for automation: about $3500 for partial automation and $4900 for full automation. Second, we estimate substantial heterogeneity in preferences for automation, where a significant share of the sample is willing to pay above $10,000 for full automation technology while many are not willing to pay any positive amount for the technology. Third, our semiparametric random parameter logit estimates suggest that the demand for automation is split approximately evenly between high, modest and no demand, highlighting the importance of modeling flexible preferences for emerging vehicle technology.     

A probabilistic stationary speed–density relation based on Newell’s simplified car-following model

Probabilistic models describing macroscopic traffic flow have proven useful both in practice and in theory. In theoretical investigations of wide-scatter in flow–density data, the statistical features of flow density relations have played a central role. In real-time estimation and traffic forecasting applications, probabilistic extensions of macroscopic relations are widely used. However, how to obtain such relations, in a manner that results in physically reasonable behavior has not been addressed. This paper presents the derivation of probabilistic macroscopic traffic flow relations from Newell’s simplified car-following model. The probabilistic nature of the model allows for investigating the impact of driver heterogeneity on macroscopic relations of traffic flow. The physical features of the model are verified analytically and shown to produce behavior which is consistent with well-established traffic flow principles. An empirical investigation is carried out using trajectory data from the New Generation SIMulation (NGSIM) program and the model’s ability to reproduce real-world traffic data is validated.     

考虑均值及方差异质性的外卖骑手闯红灯行为影响因素分析

针对外卖骑手闯红灯事件频发、事故风险隐患较高的问题,调查了西安市多个信号交叉口处外卖骑手的闯红灯行为。将闯红灯行为作为因变量,将骑手个人特征、穿越行为特征、交通及环境特征等作为自变量,构建了考虑均值及方差异质性的随机参数Logit模型。采用Halton序列抽样进行参数估计,并结合参数估计结果和平均边际效应值量化分析各自变量对因变量的影响。结果表明：“饿了么”和“UU跑腿”的骑手闯红灯概率较低,到达时段为红灯第2段或第3段、停车线之后等待通行、冲突方向机动车流量较大等变量也会显著降低闯红灯概率,而同向违章人数增加、午高峰、晚高峰等变量会显著增加闯红灯概率。其中,使闯红灯概率提升最大的变量为晚高峰,其平均边际效应为0.278;使闯红灯概率降低最大的变量为停车线后等待,其平均边际效应为-0.222。此外,停车线后等待和晚高峰为随机参数变量,参数均服从正态分布,其均值和标准差分别为-1.379,1.359和2.502,5.360,且都具有显著的均值及方差异质性。对于停车线后等待这个变量,红灯第2段会同时提高其参数均值和方差,即增加闯红灯的概率,并增加该变量对闯红灯行为影响的随机性。对于晚高峰变量,机动车流量较大会同时降低其参数均值和方差,即降低闯红灯的概率,并降低该变量对闯红灯行为影响的随机性。此外,违章人数为1也会降低晚高峰参数的方差。