Estimation of travel demand models from panel data

随机效用模型（RUM）现在被认为是分析选择行为的最先进方法，例如诺贝尔奖授予丹尼尔麦克法登和丹尼尔卡内曼。RUM模型的校准需要使用包含个体应答者所作选择信息的数据。最初的建模技术主要是在一个显示偏好的情况下，每个受访者做出一个单一的选择，也被称为横截面的情况。然而，多年来，越来越多地使用面板数据集，其中每个受访者都有多个选择，这种情况在声明选择（SC）数据的情况下最为突出。这种SC数据集现在越来越多地用于模型的估计，这些模型被用来指导重要的政策决策，例如在交通规划实践中，标准建模方法是为横截面背景开发的，这一事实在许多应用依赖于面板数据的时候提出了一个重要问题。事实上，不清楚在一个跨部门框架中所作的假设，例如在敏感性异质性方面的假设，是否适用于小组背景。此外，增加的并发症，表现出自己在一个小组的框架，如疲劳和学习，是通过设计不纳入横截面的方法。一些努力已经进入解决这种情况在现有的文献。然而，这在很大程度上包括简单化的纠正方法或准面板方法的使用，这些方法是横截面方法的简单扩展，对面板背景下的行为做出相当严格的假设。在越来越依赖面板数据的时代，最新技术的这些潜在缺点是令人担忧的主要原因。本项目旨在解决这一问题，研究将通过审查现行做法和确定用于分析面板数据的方法，包括已开发的软件来进行。将查明现有方法中的差距，并制定填补这些差距的方法。所有的方法将被放入一个通用的数学框架。将使用现有的数据集编写和测试软件，执行总体框架。然后将通过网络调查收集新的数据，以测试总体框架中现有数据无法解决的问题。这项工作的及时性源于在交通规划实践中广泛使用面板数据以及基于面板数据的政策和投资决策的重要性。特别是创新政策的制定，解决交通拥堵和排放等紧迫问题所需的技术，往往依赖于SC面板数据的使用。交通以外的其他领域也将受益于这项研究。这项工作将有利于研究和实践，特别是通过提供明确描述和全面的模型估计方法。

Random utility models (RUM) are now recognised, e.g. by the Nobel prize awards to Daniel McFadden and Daniel Kahnemann, as a state-of-the-art method for the analysis of choice behaviour. The calibration of RUM models requires the use of data containing information on choices made by individual respondents. The initial modelling techniques were largely developed in a revealed preference context where each respondent makes a single choice, also referred to as a cross-sectional situation. However, over the years, more and more use is being made of panel datasets in which multiple choices are observed for each respondent, a situation that arises most prominently in the case of stated choice (SC) data. Such SC datasets are now increasingly used in the estimation of models that are relied on to guide important policy decisions, for example in transport planning practice.The fact that the standard modelling methodology was developed for a cross-sectional context poses an important issue at a time when many applications rely on panel data. Indeed, it is not clear whether the assumptions made in a cross-sectional framework, for example in terms of heterogeneity in sensitivities, apply in a panel context. Furthermore, added complications that exhibit themselves in a panel framework, such as fatigue and learning, are by design not incorporated in the cross-sectional methodology.Some effort has gone into addressing this situation in the existing literature. However, this has largely consisted of simplistic correction approaches or the use of quasi-panel approaches that are simple extensions of cross-sectional approaches making rather stringent assumptions about behaviour in a panel context. These potential shortcomings of the state of the art are a major cause for concern in an age of increasing reliance on panel data. The present project aims to address this.The research will proceed through a review of the current practice and an identification of the methods being used to analyse panel data, including the software that has been developed. Gaps in the existing methods will be identified and methods to fill those gaps will be developed. All the methods will then be put into a general mathematical framework. Software will be written and tested, implementing the general framework, using existing data sets. New data will then be collected, using a web-based survey, to test aspects of the general framework that cannot be addressed with existing data.The timeliness of the work arises from the extensive use that is made in transportation planning practice of panel data and the importance of the policy and investment decisions that are based on it. Particularly the development of innovative policy, required to address pressing issues of traffic congestion and emissions, often relies on the use of SC panel data. Areas other than transport will also benefit from this research. The work will benefit both research and practice, in particular by providing model estimation methods that are clearly described and comprehensive.