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.

This paper considers which work-related trip patterns are included in household travel surveys and which in commercial travel surveys and if there are certain patterns that are distinctly underrepresented in either one. The study is structured as a comparison between data from a household travel survey and data from a commercial travel survey. Both surveys were conducted in Germany and within close temporal proximity. We applied cluster analysis to identify differences in the data and identify work-related travel patterns. The results show that work-related travel patterns are quite complex. Although some patterns are covered in both surveys, mobile workers’ travel patterns in particular are not represented well in the household travel survey. Furthermore, our analysis shows that not all commercial trips are generated by motorized vehicles and a considerable share of work-related trips are undertaken using public transport or active modes of transport that are not covered by the commercial travel survey. The results indicate that researchers and transport planners creating travel demand models need to pay more attention to work-related travel behavior and acknowledge that depending on the area of study, traditional household travel surveys may not provide a complete sample of the population; however, simply adding data on commercial trips from commercial travel demand models to data from household travel surveys does not provide a complete picture of work-related travel either.

To achieve climate goals in the transport sector, many countries are trying to promote the use of public transport. However, to implement effective policies, one must understand the motivations of people who use or do not use public transport today. In this study, we examine the psychographic profiles using latent class analysis to identify the reasons why people use or do not use public transport and link these profiles to reported travel behavior. For the latent class analysis, we use selected psychological items of the German Mobility Panel (MOP), a national household travel survey, that capture attitudes toward public transport. The results highlight four classes that differ based on their psychological profiles: PT-Averse, Privacy Aware Environmentalists, Pragmatists, and PT-Lovers. The results further show that Privacy Aware Environmentalists and PT-Lovers, who have a strong personal norm, frequently use public transport and environmentally friendly transport modes. Thus, the personal norm is a driver of public transport use. The lack of privacy, which the Privacy Aware Environmentalists complain about in public transport, is not a barrier to public transport use.