Last Updated: 4-2023
The rise of car ownership and use has resulted in various environmental, economic, and social issues. Through proposing and implementing policies, governments try to decrease car ownership and use. Public transportation based multimodal trips (PTMTs) can be helpful for this because they combine public transportation with active travel modes and/or the car for the first and last leg of the trip. Due to PTMTs less distance can be traveled by car. However, little previous research includes the first and last leg of PTMTs. Despite the first and last legs can influence the entire trip. Because of this, there is a knowledge gap of variables that influence types of PTMTs. Therefore, this study aims to analyze the variables that influence specific types of PTMTs within The Netherlands. Data concerning personal, household, environmental, and trip characteristics are gained from ODiN and OSM. Subsequently, two Bayesian Belief Networks (BBNs) are estimated to explore the direct and indirect relationships between the included variables and the choice of trip type (unimodal car trip or PTMT) and between the types of PTMTs. The results showed that the variables student public transportation smartcard, number of cars within the household, and travel motive are directly influencing the choice of trip type between a unimodal car trip and a PTMT. Focusing solely on the types of PTMTs, is seems that the variables distance (direct), travel motive (indirect), and travel time (indirect) have an influence. The two BBNs help governments to create policies to reduce car ownership and use and to stimulate PTMTs.