Mode shifting in school travel mode: examining the prevalence and correlates of active school transport in Ontario, Canada

<p>Abstract</p> <p>Background</p> <p>Studies examining the correlates of school transport commonly fail to make the distinction between morning and afternoon school trips. The purpose of this study was to examine the prevalence and correlates of mode shift from passive in the morning to active in the afternoon among elementary and secondary school students in Ontario, Canada.</p> <p>Methods</p> <p>Data were derived from the 2009 cycle of the Ontario Student Drug Use and Health Survey (OSDUHS). 3,633 students in grades 7 through 12 completed self-administered questionnaires. Socio-demographic, behavioural, psychological, and environmental predictors of active school transport (AST) were assessed using logistic regression.</p> <p>Results</p> <p>Overall, 47% and 38% of elementary school students reported AST to and from school, respectively. The corresponding figures were 23% and 32% for secondary school students. The prevalence of AST varied temporarily and spatially. There was a higher prevalence of walking/biking found for elementary school students than for secondary school students, and there was an approximate 10% increase in AST in the afternoon. Different correlates of active school transport were also found across elementary and secondary school students. For all ages, students living in urban areas, with a shorter travel time between home and school, and having some input to the decision making process, were more likely to walk to and from school.</p> <p>Conclusions</p> <p>Future research examining AST should continue to make the analytic distinction between the morning and afternoon trip, and control for the moderating effect of age and geography in predicting mode choice. In terms of practice, these variations highlight the need for school-specific travel plans rather than 'one size fits all' interventions in promoting active school transport.</p

The Role of Human Movement in the Transmission of Vector-Borne Pathogens

Vector-borne diseases constitute a largely neglected and enormous burden on public health in many resource-challenged environments, demanding efficient control strategies that could be developed through improved understanding of pathogen transmission. Human movement—which determines exposure to vectors—is a key behavioral component of vector-borne disease epidemiology that is poorly understood. We develop a conceptual framework to organize past studies by the scale of movement and then examine movements at fine-scale—i.e., people going through their regular, daily routine—that determine exposure to insect vectors for their role in the dynamics of pathogen transmission. We develop a model to quantify risk of vector contact across locations people visit, with emphasis on mosquito-borne dengue virus in the Amazonian city of Iquitos, Peru. An example scenario illustrates how movement generates variation in exposure risk across individuals, how transmission rates within sites can be increased, and that risk within sites is not solely determined by vector density, as is commonly assumed. Our analysis illustrates the importance of human movement for pathogen transmission, yet little is known—especially for populations most at risk to vector-borne diseases (e.g., dengue, leishmaniasis, etc.). We outline several important considerations for designing epidemiological studies to encourage investigation of individual human movement, based on experience studying dengue

Routine pattern discovery and anomaly detection in individual travel behavior

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling

The Strathclyde Evaluation of Children's Active Travel (SE-CAT): study rationale and methods

Peer reviewedPublisher PD

Relationships between active school transport and adiposity indicators in school age children from low-, middle- and high-income countries

OBJECTIVES: Within the global context of the nutrition and physical activity transition it is important to determine the relationship between adiposity and active school transport (AST) across different environmental and socio-cultural settings. The present study assessed the association between adiposity (that is, body mass index z-score (BMIz), obesity, percentage body fat (PBF), waist circumference) and AST in 12 country sites, in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). METHODS: The analytical sample included 6797 children aged 9–11 years. Adiposity indicators included, BMIz calculated using reference data from the World Health Organization, obesity (BMIz ⩾+2 s.d.), PBF measured using bioelectrical impedance and waist circumference. School travel mode was assessed by questionnaire and categorized as active travel versus motorized travel. Multilevel linear and non-linear models were used to estimate the magnitude of the associations between adiposity indicators and AST by country site and sex. RESULTS: After adjusting for age, sex, parental education and motorized vehicle availability, children who reported AST were less likely to be obese (odds ratio=0.72, 95% confidence interval (0.60–0.87), P<0.001) and had a lower BMIz (−0.09, s.e.m.=0.04, P=0.013), PBF (least square means (LSM) 20.57 versus 21.23% difference −0.66, s.e.m.=0.22, P=0.002) and waist circumference (LSM 63.73 cm versus 64.63 cm difference −0.90, s.e.m.=0.26, P=0.001) compared with those who reported motorized travel. Overall, associations between obesity and AST did not differ by country (P=0.279) or by sex (P=0.571). CONCLUSIONS: AST was associated with lower measures of adiposity in this multinational sample of children. Such findings could inform global efforts to prevent obesity among school-age children

The use of census migration data to approximate human movement patterns across temporal scales

Human movement plays a key role in economies and development, the delivery of services, and the spread of infectious diseases. However, it remains poorly quantified partly because reliable data are often lacking, particularly for low-income countries. The most widely available are migration data from human population censuses, which provide valuable information on relatively long timescale relocations across countries, but do not capture the shorter-scale patterns, trips less than a year, that make up the bulk of human movement. Census-derived migration data may provide valuable proxies for shorter-term movements however, as substantial migration between regions can be indicative of well connected places exhibiting high levels of movement at finer time scales, but this has never been examined in detail. Here, an extensive mobile phone usage data set for Kenya was processed to extract movements between counties in 2009 on weekly, monthly, and annual time scales and compared to data on change in residence from the national census conducted during the same time period. We find that the relative ordering across Kenyan counties for incoming, outgoing and between-county movements shows strong correlations. Moreover, the distributions of trip durations from both sources of data are similar, and a spatial interaction model fit to the data reveals the relationships of different parameters over a range of movement time scales. Significant relationships between census migration data and fine temporal scale movement patterns exist, and results suggest that census data can be used to approximate certain features of movement patterns across multiple temporal scales, extending the utility of census-derived migration data

Open source, spatial analysis, and activity-travel behaviour research: capabilities of the aspace package

Spatial analysis, Spatial statistics, Activity-travel behaviour, Centrographics, Home range ecology, Open source software, Urban form, C10, C80, C88, R20, R41,

Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS)

Spatial behaviour, Intra-personal variability, Intra-household activities, Weekend activity-travel behaviour, Activity spaces,