The factors influencing trip departure time are taking more importance in urban planning practice since congestion is increasingly being addressed by travel demand management (TDM) strategies. In this paper we formulate and estimate a joint travel mode-departure time model for commuting trips using combined revealed preference (RP) and stated choice (SC) data. The RP data considered nine alternative modes and up to 11 time periods, and the level-of-service data were obtained at an unusual level of precision using GPS measurements. The travel time, cost and cost divided by the wage rate coefficients were fairly similar in both the RP and SC environments, suggesting equal error variances for both datasets. The only parameters that differed between each type of data were those associated with the schedule delay early (SDE) and late (SDL) variables required by Small’s Scheduling Model. This may be due to the potentially different temporal perspectives between RP choices (longer term decisions) and SC decisions, arguably shorter term given the nature of the experiment and the context presented in it (implementation of a congestion charging policy and a flexible working-hours scheme). The models were used to forecast the impacts of a hypothetic congestion charging scheme in Santiago, showing that the schedule delay coefficients derived from the SC context produced a smoother and less-peaked temporal distribution of travel demand than the RP parameters.

由于出行需求管理 (TDM) 策略越来越多地解决交通拥堵问题，因此影响出行出发时间的因素在城市规划实践中变得越来越重要。在本文中，我们使用联合显示偏好 (RP) 和陈述选择 (SC) 数据为通勤旅行制定和估计联合出行方式-出发时间模型。RP 数据考虑了九种替代模式和多达 11 个时间段，服务水平数据是使用 GPS 测量以异常精确的水平获得的。在 RP 和 SC 环境中，旅行时间、成本和成本除以工资率系数非常相似，这表明两个数据集的误差方差相等。调度模型。这可能是由于 RP 选择（长期决策）和 SC 决策之间可能存在不同的时间观点，考虑到实验的性质和其中提出的背景（拥堵收费政策的实施和灵活的工作时间），可以说是短期的方案）。这些模型用于预测圣地亚哥假设的拥堵收费计划的影响，表明从 SC 上下文导出的时间表延迟系数产生了比 RP 参数更平滑且峰值更低的旅行需求时间分布。