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Supplemental material, sj-pdf-1-epb-10.1177_2399808320974693 for Using data mining to explore the spatial and temporal dynamics of perceptions of metro services in China: The case of Shenzhen by Shuli Luo and Sylvia Y He in EPB: Urban Analytics and City Science

The objective is to understand intentions to buy a car and how car-ownership together with other factors influences travel mode choice. A survey (N=1,000) was carried out in Guangzhou in four districts of the city. The survey examined respondents' car ownership, travel mode choices, and personal and situational characteristics. Quota sampling was used to select members of three different groups: (1) car owners; (2) planning to buy a car within the next 5 years; and (3) no car and not planning to buy one. We find that car ownership is the most important predictor of travel mode choice, which also depends on other factors, but only indirectly (mediated through car ownership) by attitudinal factors. However, attitudinal factors have a strong impact on the intention to buy a car, while controlling for other personal and context factors, which play a smaller role. Implications for policy are inferred.

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.

This paper investigates the mobility habits of electric bike owners as well as their preferred routes. Through a GPS tracking campaign conducted in the city of Ghent (Belgium) we analyze the mobility habits (travel distance, time spent, speed) during the week of some e-bike users. Moreover, we propose the results of our map matching, based on the Hausdorff criterion, and preliminary results on the route choice of our sample. We strongly believe that investigating the behavior of electric bikes’ owners can help us in better understanding how to incentivize the use of this mode of transport. First results show that the trips with a higher travel distance are performed during the working days. It could be easily correlated with the daily commuting trips (home-work). Moreover, the results of our map-matching highlight how 61% of the trips are performed using the shortest path.