CAUSAL RELATIONSHIP AMONG TRAVEL MODE, ACTIVITY, AND TRAVEL PATTERNS

CAUSAL RELATIONSHIP AMONG TRAVEL MODE, ACTIVITY, AND TRAVEL PATTERNS
复制标题

DOI:
10.1061/(asce)0733-947x(2003)129:1(16
复制
发表时间:
2003
期刊:
Journal of Transportation Engineering-asce
影响因子:
--
通讯作者:
T. Jang
T. Jang
中科院分区:
其他
文献类型:
--
作者:
T. Jang

文献摘要

被引文献

相似文献

作为活动执行的简单或复杂的旅行模式是相互关联的,并且使用各种旅行模式。人们试图将多次出行连接成链条,以节省出行距离或时间等交通资源,从而产生复杂的出行模式。城市交通环境的这些变化以及一些基于活动特征的研究使得由于出行联动行为而难以预测出行。建立两个模型来实现研究目标。第一个是在限制条件下采用三阶段最小二乘(3SLS)估计方法的活动出行方式的分配模型。第二个是协方差结构模型,用于分析潜在变量和估计变量之间的直接和间接影响。由于3SLS的结果,出行方式的分配主要受性别、教育程度等个人属性以及婚姻家庭属性(例如是否存在13岁以下儿童)的影响。在协方差结构模型中,表明个人特征的外生估计变量可以很好地解释个人潜在变量,而只有家庭收入可以解释家庭潜在变量。虽然出行行为潜变量受出行模式潜变量的影响较大,但受活动潜变量的影响较小。
Simple or complex travel patterns, performed as activities, are linked, and various travel modes are used. Individuals try to link several trips as a type of chain to save transportation resources, such as travel distance or time, which consequently produces complex travel patterns. These changes in the urban transportation environment and some studies on activity-based characteristics make it difficult to forecast trips as a result of trip linkage behavior. Two models are established to achieve research aims. The 1st is an allocation model for travel modes for activities by the 3 stage least square (3SLS) estimation method under the restricted conditions. The 2nd is a covariance structure model to analyze direct and indirect effects among latent variables and estimated variables. As a result of 3SLS, the allocation of travel modes is mainly influenced by personal attributes such as gender, education level, and marriage and household attributes, such as the existence of children under 13 years old. In the covariance structure model, it is shown that exogenous estimated variables for personal characteristics explain well the personal latent variable and only household income explains the household latent variable. While the travel behavior latent variable is well effected by travel mode latent variable, it is less effected by the activity latent variable.