Examining the Impact of the Walking School Bus With an Agent-Based Model

We used an agent-based model to examine the impact of the walking school bus (WSB) on children’s active travel to school. We identified a synergistic effect of the WSB with other intervention components such as an educational campaign designed to improve attitudes toward active travel to school. Results suggest that to maximize active travel to school, children should arrive on time at “bus stops” to allow faster WSB walking speeds. We also illustrate how an agent-based model can be used to identify the location of routes maximizing the effects of the WSB on active travel. Agent-based models can be used to examine plausible effects of the WSB on active travel to school under various conditions and to identify ways of implementing the WSB that maximize its effectiveness.Active travel to school (ATS) is of special significance to children’s physical and mental health and has important implications for the environment and sustainable development.1,2 Today, fewer than 15% of US children and adolescents walk or bicycle to school, compared with approximately 50% half a century ago.3 Multiple factors are associated with ATS, including characteristics of children and families as well as features of schools and neighborhoods.1,4Existing interventions designed to promote ATS are heterogeneous in terms of size, scope, and focus,5–7 and according to a recent systematic review,8 most have shown limited effectiveness in promoting ATS. Interventions targeting schools, parents, and communities and those geared toward a specific goal tend to be more effective than those that are broader in focus. The ability to evaluate an intervention’s impact has often been limited by methodological issues such as lack of an experimental study design and limitations in the validity and reliability of the measures used. The complexity of the factors influencing ATS and the fact that the effects of these factors may vary from context to context further limit the utility of both observational and experimental studies in evaluating the long-term impact of various interventions.One notable example of an intervention to increase ATS is the walking school bus (WSB).9 The WSB is a program in which children walk to school in groups led by adults along a planned route with designated meeting places (i.e., “bus stops”) where other children join in. The primary goal is to allow children to actively and safely commute to school. During the 2009–2010 school year, about 6.2% of the elementary schools in the United States organized a WSB.10 Although most WSBs have shown promising short-term benefits,7,11–15 evidence of their effectiveness over long periods is limited.A major challenge in evaluating the impact of interventions such as the WSB is that it is difficult to evaluate how the intervention may interact with features of the context in which it is implemented or how features of the intervention may influence its effectiveness. For example, the effects of ATS may be modified by educational campaigns targeted at increasing favorable attitudes toward walking among children.Various features related to how the WSB is implemented may also influence its effectiveness. When using the WSB, children may take longer to arrive at school because of the waiting time at the “bus stop,” the group’s decreased walking speed, and detours that need to be taken for the child to reach the WSB route. If children arrive at the stop earlier (increasing their waiting times), group walking speed can increase (because the group is less likely to wait at each stop for other children to join in), reducing the total travel time. The challenge is to find the strategy that minimizes waiting time while maximizing walking speed.Another challenge for the WSB is the selection of bus routes and stops. More bus routes may attract more children to join the WSB but may also necessitate more adult involvement. It is therefore important to identify the most beneficial placement of a limited number of bus routes. To our knowledge, there is scant information on how these contextual or implementation factors could affect the impact of the WSB on children’s active travel to school.A large body of work has recommended multilevel interventions (i.e., interventions that combine environmental and individual-level elements) for behavioral change because of possible synergistic effects.16–20 It has been noted that evidence on interventions derived from randomized trials is often limited because the impact of context as a modifier cannot be investigated. For example, it is well established that physical activity is influenced by the interplay between environmental and psychological factors.21,22 Yet, existing observational or experimental studies are ill suited to investigate these interactions. Modeling or simulation studies can be important sources of complementary evidence on how the impact of interventions such as the WSB may be affected by various contextual factors.In public health, there has been growing interest in using complex systems modeling as a complement to observational studies and randomized trials to better understand the plausible impact of interventions or policies in different contexts.23–25 The tools of complex systems models allow researchers to evaluate the effects of interventions while accounting for dependencies and feedbacks, which are not easily captured in standard statistical analyses. One such tool is agent-based modeling. Agent-based models (ABMs) are computational models that can be used to simulate the actions and interactions of agents as well as the dynamic interactions between agents and their environments to gain an understanding of the functioning of a system.26,27 ABMs have increasingly been used to investigate how the social and built environments shape people’s travel behavior.28–32We previously developed an ABM31 to simulate children’s ATS within a hypothetical city. The model was used to explore the plausible implications of policies targeting 2 established barriers to ATS: long distance to school and traffic safety. In this study, we extended the model to examine the potential effects of the WSB on ATS. Specifically, we examined whether the effects of the WSB are enhanced by an educational campaign aimed at improving attitudes toward ATS among children, how changing the walking speed of the WSB and the waiting time for children at the WSB stops affects ATS, and the impact of different bus route placements. All 3 issues are important in the design and implementation of the WSB but are difficult to assess in experimental or observational studies. Because previous work suggested that ATS is influenced by population density,31 we investigated the 3 issues across various levels of population density.