Understanding social processes of shopping destination choice - An approach to model stability and variability

Transport systems are affected by fundamental technical and social dynamics. For planners and researchers, travel demand models are an important tool to gain insights into possible effects of these dynamics and to find appropriateways of dealing with them. However, most state-of-the-art travel demand models underestimate social aspects of travel choices, which we consider essential for understanding stability and variability of travel behavior. Based on a qualitative interview study, the paper presents an interdisciplinary approach to consider social aspects for modeling shopping destination choice. Starting point for our considerations is that people are social beings, moving around to build and maintain relationships, and that these relationships only unfold in relation to overall sociotechnical structures. The interview study provides evidence for relationships between stores and customers. Relationships can be distinguished in two dimensions. First, in terms of the nature of a relationship: having a relationship either with the owner of a specific store or towards specific brands. Second, in terms of the meaning: if the relationships is perceived meaningfull or obligatory. Findings are represented in a first modeling approach. The results show that the more seldom relationships to owners of a store can be modeled easily, while the more typical relationships to brands require further research

How Late Reporters Effect Data Quality in Longitudinal Surveys – Experiences From the German Mobility Panel

Survey design always has a significant influence on the outcomes. Therefore, this paper investigates how follow-up campaigns affect survey outcomes and response rates in longitudinal surveys. Furthermore, it is assessed how late reports in the fall affect survey outcomes. The analyses are based on the unique data of the German Mobility Panel. Overall, this paper paints a broad picture of the methodological aspects and overlapping effects that should be considered before starting the fieldwork of longitudinal surveys. The results indicate that people who are reminded to participate positively influence the survey outcomes - even when the report is belated

Exploring the role of individuals’ attitudes in the use of on-demand mobility services for commuting – A case study in eight Chinese cities

The use of on-demand mobility (ODM) services has increased in Chinese cities and is used by people for various purposes, such as leisure activities or commuting. The aim of this study is to identify and analyze factors that play a role in the use of ODM services for commuting of high-income earners in China. In previous studies, this group of people was identified as extremely relevant for ODM use as they can afford the services in principle. A specific focus of this study is on the influence of travel mode attitudes as well as sociodemographic characteristics. The data set used in this study was collected with the innovative travel skeleton approach based on information given by high-income individuals. The survey took place in eight different Chinese cities with 5,192 respondents. They have provided insights on their everyday travel (e.g., commuting) and attitudes towards car and public transit. To investigate the role of psychological factors behind the use frequency of ODM services, we applied a factor analysis to identify latent factors from psychological item sets used. Next, we integrated them into an ordered hybrid choice model (OHCM). The results show that people’s perceived public transit experience increase the probability to use ODM more often for commuting. We suggest a strong interrelation between public transit and such services, even among people with high incomes

Comparison of Discrete Choice and Machine Learning Models for Simultaneous Modeling of Mobility Tool Ownership in Agent-Based Travel Demand Models

Individual travel behavior, such as mode choice, is determined to a distinct degree by the respective portfolio of available mobility tools, such as the number of cars, public transit pass ownership, or a carsharing membership. However, the choice of different mobility tools is interdependent, and individuals weigh alternatives against each other. This process of parallel trade-offs is currently not reflected in typically used sequential logit models of agent-based travel demand models. This study fills this research gap by applying discrete choice and neural network models on a synthetic population to model multiple mobility tool ownership simultaneously. Using data from a national household travel survey, both model types approximated the given target distributions of mobility tools more accurately than the sequence of three corresponding logit models. Owing to its greater flexibility, the tested shallow and deep neural network exhibited higher predictive accuracy than simultaneous discrete choice models. The results indicated that neural networks with only one hidden layer were more robust and easier to formulate and interpret than deep networks with three hidden layers. Finally, the flat neural network was applied to a different synthetic population resulting in equally accurate results

Balancing Innovation and Continuity – Experiences with Survey Design Adaptations of the German Mobility Panel

The German Mobility Panel is one of the longest-lasting studies with a basically unchanged design in mobility behaviour research world-wide. As a result one central asset of this study is the provision of time series data. Nevertheless in repeated surveys, design changes are sometimes inevitable due to new research questions or external developments. Since 1994 the German Mobility Panel has seen only minor design adaptations. After nearly 20 years with a more or less unchanged design, declining participation rates by certain person groups and new survey methods have required fundamental changes in the survey design. This paper describes design changes to the German Mobility Panel in 2013 and analyses the first outcomes generated by the methodological changes

How the COVID-19 pandemic changes daily commuting routines – Insights from the German Mobility Panel

The COVID-19 pandemic has had a major impact on everyday travel and, by extension, everyday commuting. During the pandemic, some people were able to work from home while others continued commuting. This study examines how commuting behavior changed between 2019 and 2020. In this study, we analyze panel data of the German Mobility Panel, a national household travel survey. We paint a broad picture of the characteristics and behavior of those who commuted during the pandemic. The analyses focus on the intra- and interpersonal differences and are presented in a mostly descriptive way. The results show that people with low income and a low level of education are primarily those who cannot work from home and do not have flexible working hours. The results further show that especially public transport has lost importance in daily commuting. However, those who commuted in 2019 and 2020 did not significantly change their commuting behavior regarding commuting time and commuting mode