What drives active transportation choices among the aging population? Comparing a Bayesian belief network and mixed logit modeling approach

As people age, they typically face declining levels of physical ability and mobility. However, walking and bicycling can remain relatively easy ways to be physically active for older adults provided that the built environment facilitates these activities. The aim of this study is to investigate which variables are most effective for urban planners and management to promote the participation in active travel behavior by the older population. To do so we investigate the participation of the aging population in walking and bicycling activities as a function of socio-demographics and physical and social environmental characteristics. Revealed individual travel choice data including all walking and bicycling trips for one day from a random sample of 4396 respondents in the age category of 65 years and over in the Netherlands were analyzed. For each trip, a large number of explanatory variables was available including information on mode choice, purpose of the trip, travel distance, travel duration, weekday, and number of trips per day. In addition, socio-demographics such as gender, age, income, whether the person has a partner, owns a bike, has a drivers license, and car possession were included. The data was fused with social and physical environmental characteristics at the neighborhood level including, urban density level, accessibility of shops, green/recreation areas, and restaurant/cafes, and indicators describing the safety and social cohesion. Mixed logit models (ML) have proven to be a useful tool for predicting active transportation choices and assessing policy measures and planning interventions. However, including such a large number of attributes and covariates in the model and finding meaningful interactions with the mode alternatives is a challenging task. Because variables are often highly correlated and the structure of their relationships is typically not clear (e.g., mediating effects, interaction effects, etc.) model variable selection and defining an appropriate structure for explanatory variables typically is difficult. A Bayesian belief network (BBN) approach can overcome such difficulties by deriving and representing all direct and indirect relations between variables by using a network learning algorithm. The network learning involves two main tasks: first learning the structure of the network and then finding the parameters (Conditional Probability tables) for that structure. However, although BBN is useful in discovering the appropriate data structure among a set of variables it is less well suited to predict outcomes of specific dependent variables. In this paper we therefore analyze the rich available data set using both alternative approaches. By comparing and integrating the outcomes of these analyses we can support more informed decisions about variable and model selection as well as provide guidance for specific urban planning and management interventions to promote active transportation choices by the elderly

Toward personalised and dynamic cultural routing: a three-level approach

This paper introduces the concept of “smart routing” as a recommender system for tourists that takes into account the dynamics of their personal user profiles. The concept relies on three levels of support: 1) programming the tour, i.e. selecting a set of relevant points of interests (POIs) to be included into the tour, 2) scheduling the tour, i.e. arranging the selected POIs into a sequence based on the cultural, recreational and situational value of each, and 3) determining the tour’s travel route, i.e. generating a set of trips between the POIs that the tourist needs to perform in order to complete the tour. The “smart routing” approach intends to enhance the experience of tourists in a number of ways. The first advantage is the system’s ability to reflect on the tourists’ dynamic preferences, for which an understanding of the influence of a tourist’s affective state and dynamic needs on the preferred activities is required. Next, it arranges the POIs together in a way that creates a storyline that the tourist will be interested to follow, which adds to the tour’s cultural value. Finally, the POIs are connected by a chain of multimodal trips that the tourist will have to make, also in accordance with the tourist’s preferences and dynamic needs. As a result, each tour can be personalised in a “smart” way, from the perspective of both the cultural and the overall experience of taking it. We present the building blocks of the “smart routing” concept in detail and describe the data categories involved. We also report on the current status of our activities with respect to the inclusion of a tourist’s affective state and dynamic needs into the preference measurement phase, as well as discuss relevant practical concerns in this regard

Een conjunct keuze model voor variatiezoekend gedrag van themapark bezoekers

No abstract

Environmental correlates of active travel behavior of children

This study explored the participation of children in walking and bicycling for transportation, school, and various leisure purposes, and the relation with social and physical environmental characteristics and sociodemographics. Detailed individual travel data, including all walking and bicycling trips from a random sample of 4,293 children in the primary-school-age category in the Netherlands were investigated. Specifically, a Bayesian belief network was proposed that derives and represents all direct and indirect relations between the variables. The participation in active travel behavior has a direct relationship with all trip characteristics such as travel time and distance, and trip purpose, and is related to the car possession of the household. The degree of urbanization also is an important explanatory variable for participation in walking and bicycling by children. All the other social and physical environmental characteristics have an indirect influence on travel mode choice

Green spaces in the direct living environment and social contacts of the aging population

Green spaces in the living environment may provide a meeting place and support social contacts. When people get older they, in general, are less mobile and have more limited activity spaces. At the same time they are faced with smaller social networks due to social and health related changes. Green spaces in their direct living environment are therefore important to support their needs. The aim of this study was to better understand the nature of the relationship between various types of green spaces in the direct living environment and the extent and nature of social contacts of the aging generation, taking into account socio-demographics and other physical and social environmental characteristics. Data for this study were obtained from a survey about living surroundings from a national representative sample of 1501 persons in the age category of 60 years and over in the Netherlands conducted in 2009. The survey included both subjective and objective measurements of the direct living environment of the respondents. Specifically, a Bayesian belief network was used to formulate and estimate the direct and indirect relationships between the selected variables. Results show that social contacts among neighbors are mainly influenced by the availability of trees and grass and the perceived level of green. Green spaces support social contacts in the neighborhood. However, the safety and maintenance of the green spaces are also important; high quality green spaces support social contacts between neighbors and strengthen communities for the aging population