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The aim of this study is to address the uncertainty problem caused by measurement error in random utility and random regret choice models. Based on formal analysis and empirical comparison, we provide new insights about the uncertainty problem in discrete choice modeling. Using standard assumptions, random measurement error is introduced into level-of-service variables. The effect of measurement error is analysed by comparing the estimated parameters of the concerned choice models, before and after introducing measurement error. We argue that although measurement error leads to biased estimation results in both types of models, bias appears differently in these choice models because random regret models involve a comparison of alternatives, and therefore uncertainty tends to accumulate. Therefore, bias tends to be larger. Moreover, since random regret models are constructed assuming semi-compensatory decision processes, uncertainty is not changed in the noncompensatory area. Several approaches are discussed to overcome this uncertainty problem.

Job application decisions are the first step in the process of arranging work schedules. In turn, work schedules will affect spouses' work time synchronization and household task allocation, and associated travel. In two-adult households with children, children-related obligations and activities complicate work schedule arrangements. As part of a wider project, the aim of this paper is to estimate the effects of job attributes, socio-demographics and social influence on households' job application decisions, which will be input to a model of work schedule arrangements and synchronized household activities. A mixed binary logit model is used to estimate the effects of job attributes, socio-demographic characteristics, and social influence on the probability of applying for a specific job profile. The model estimation is based on a stated choice experiment, which systematically varies job attribute levels and degree and nature of social influences. Results show that social influence plays a less significant role compared with job attributes and socio-demographic characteristics.

Travel satisfaction refers to people's evaluation of transport services and their experience during travel. This topic has recently started to attract attention in transportation research. Previous studies on travel satisfaction have typically tried to understand the effects of trip-related objective attributes such as travel mode, travel time and travel cost on people's travel satisfaction. The interpretation of these results may be problematic if studies ignore people's psychological conditions when they provided the satisfaction ratings. The effects of travel attributes may then be confounded with effects of personality and mood. The aim of this study is to analyse the direct or indirect effects of personality and mood on differential degrees of travel satisfaction of each trip stage using the path analysis approach. Results show that mood directly influences travel satisfaction, while the effects of personality are indirect.