Determinants and development of multimodal travel patterns

Abstract

Passenger traffic by car is regarded as one of the main contributors to energy consumption and emission in the transport sector. Car dependency and limited shifts to more carbon-friendly alternative travel modes in industrialised countries play a major role in maintaining unsustainable mobility systems, despite governments' increased attention and effort in enhancing multimodal travel behaviour. In other words, having a diverse mode usage as a travel user. Compared to travellers who only use the car, multimodal travellers are likely easier to shift to more sustainable and health-enhancing modes, such as the bicycle and public transport, when applying policies. However, how (multi)modal travel patterns developed over time and the determinants of being multimodal are not often researched in combination with measuring multimodality and showing corresponding travel patterns. A Latent Class Cluster Analysis is performed once for 2010-2017 using cross-sectional data from the Dutch National Travel Survey (OViN) to measure multimodality whilst capturing distinct travel user groups per year, based on the frequency of travel mode use. Socio-demographic, mobility resource and built-environment variables are included as potential determinants of belonging to a specific group. The main results are that overall mobility patterns of the captured travel user classes were hardly subject to change. Moreover, only the smallest two out of five identified classes have travel behaviour with a higher degree of multimodality. Besides, the likely strong effect of owning mobility resources or not (e.g., a licensure, household car, company car, or household bicycle) on being likely in a car-dependent or a multimodal travel user group is shown. Most remarkably, our findings add to the existing knowledge by revealing that company car ownership plays a significant role in being a car-dependent travel user. Based on our results, identified policy directions include, but are not limited to, affecting mode choices of employees (owning a company car) via employer-based programs to incentivise them to use the bicycle or public transportation. Nevertheless, the knowledge about several travel user classes comprising multimodal travel patterns can be extended in several areas, most notably, by including attitudinal factors which could be tracked longitudinally on the individual level, such as perceptions about willingness to use travel modes, to acquire a more profound view about what strives people to behave in a certain way over a more extended period.