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Active modes (i.e. walking and cycling) have received significant attention by governments worldwide, due to the benefits related to the use of these modes. Consequently, governments are aiming for a modal shift from motorised to active modes. Attitudes are generally considered to play an important role in travel behaviour. Understanding the relationship between the attitude towards modes and the daily mobility pattern, can support policies that aim at increasing the active mode share. This paper investigates the daily mobility patterns of individuals using a latent class cluster analysis. The relationship between these classes and attitudes towards modes is investigated. Data of the Netherlands Mobility Panel (MPN) of the year 2016 is used, in combination with a companion survey focussing on active modes. This study identifies five classes of mobility patterns: (1) car and bicycle users, (2) exclusive car users, (3) car, walk, and bicycle users, (4) public transport + users, and (5) exclusive bicycle users. Eight factors of attitudes towards modes are identified: five mode related attitudes, two public transport related attitudes, and one related to the prestige of using modes. The results show that the majority of the users exhibits a multimodal daily mobility pattern. Generally, individuals are more positive toward used modes, compared to unused modes. Furthermore, a high level of travel mode consonance is found. When this is not the case (dissonance), often active modes or sustainable modes are preferred. Consequently, when the goal is achieving a higher active mode share, some individuals need to be targeted to change their mobility portfolio (exclusive car users and car and bicycle users), whereas others should be encouraged to increase the use of active modes at the cost of car use (public transport + users and car, walk, and bicycle users).
Original languageEnglish
Number of pages24
JournalTransportation
DOIs
Publication statusPublished - 1 Jan 2019

    Research areas

  • Attitudes towards modes, Daily mobility pattern, Latent class cluster analysis, Travel behaviour

ID: 51830737