Associations between parents׳ perception of traffic danger, the built environment and walking to school

Rothman, Linda; Buliung, Ron; To, Teresa; Macarthur, Colin; Macpherson, Alison; Howard, Andrew

doi:10.1016/j.jth.2015.05.004

Cited by 75 publications

(58 citation statements)

References 47 publications

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“…For family surveys response rates are a concern. In our study response rate was reasonably high (59%) relative to others (eg, 29–44%) but varied by school. In comparison, observational counts confer certain methodological advantages, such as addressing selection and recall bias .…”

Section: Discussionsupporting

confidence: 44%

Getting at Mode Share: Comparing 3 Methods of Travel Mode Measurement for School Travel Research

Sersli

Rothman

Winters

2019

Journal of School Health

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BACKGROUND: Mode share is an important metric for active school travel programs. Common methods for measuring mode share include Hands Up surveys and family surveys, but these require teacher and parental involvement. We used these methods as part of an evaluation of a school-based bicycle training program, and added a novel observational count approach. This paper compares mode share results across the 3 methods. METHODS:We collected data over 2015-2017 at 16 elementary schools. Our outcome of interest was mode share (walk, drive, and bicycle). RESULTS:We found variations in travel mode estimates between methods and across schools. Overall most school journeys were made by walking (55.7% by observational counts, 46.3% by Hands Up surveys, and 51.5% by family surveys) or car (42.5%, 51.4%, and 46.2%, respectively), and a small proportion by bicycle (1.8%, 2.3%, and 2.2%, respectively). At individual schools, Hands Up and family survey results were similar; there was less agreement between these and observational counts. CONCLUSION:School travel practitioners face pragmatic choices in data collection. Observational counts are a nonintrusive method suited for school-wide travel patterns. Hands up and family surveys may be more appropriate for assessing differences between classrooms, ages, or family characteristics.Citation: Sersli S, Rothman L, Winters M. Getting at mode share: comparing 3 methods of travel mode measurement for school travel research.

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Section: Discussionsupporting

confidence: 44%

Getting at Mode Share: Comparing 3 Methods of Travel Mode Measurement for School Travel Research

Sersli

Rothman

Winters

2019

Journal of School Health

Self Cite

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“…Additionally, a growing body of evidence has shown that adult perceptions do not always align with objective measures of the neighbourhood environment [71,74,75]. Rothman and colleagues [76] concluded that there are some differences between parents’ perception and objective measures of traffic safety along school routes. However, some specific measures such as road crossings were related to lower parental perceived traffic danger.…”

Section: Discussionmentioning

confidence: 99%

Children’s Transport Built Environments: A Mixed Methods Study of Associations between Perceived and Objective Measures and Relationships with Parent Licence for Independent Mobility in Auckland, New Zealand

Smith

Amann

Cavadino

et al. 2019

IJERPH

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Children’s independent mobility is declining internationally. Parents are the gatekeepers of children’s independent mobility. This mixed methods study investigates whether parent perceptions of the neighbourhood environment align with objective measures of the neighbourhood built environment, and how perceived and objective measures relate to parental licence for children’s independent mobility. Parents participating in the Neighbourhood for Active Kids study (n = 940) answered an open-ended question about what would make their neighbourhoods better for their child’s independent mobility, and reported household and child demographics. Objective measures of the neighbourhood built environment were generated using geographic information systems. Content analysis was used to classify and group parent-reported changes required to improve their neigbourhood. Parent-reported needs were then compared with objective neighbourhood built environment measures. Linear mixed modelling examined associations between parental licence for independent mobility and (1) parent neighbourhood perceptions; and (2) objectively assessed neighbourhood built environment features. Parents identified the need for safer traffic environments. No significant differences in parent reported needs were found by objectively assessed characteristics. Differences in odds of reporting needs were observed for a range of socio-demographic characteristics. Parental licence for independent mobility was only associated with a need for safer places to cycle (positive) and objectively assessed cycling infrastructure (negative) in adjusted models. Overall, the study findings indicate the importance of safer traffic environments for children’s independent mobility.

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“…Many studies have shown that road density, the number of intersections, and bicycle facilities, etc. can significantly affect students' choices regarding active travel modes [9,15,17].…”

Section: Influence Of the Built Environment On Metro Ridershipmentioning

confidence: 99%

“…Although only a few studies have focused on students' metro ridership [14], many studies have pointed out that the built environment has a considerable influence on students' choice of travel mode for school commuting [15][16][17][18]. For instance, in areas with high residential density and neighborhood density as well as well-connected and accessible by roads, the likelihood of students travelling by active modes increases [17]. Since students usually walk or bicycle from their homes to metro stations, the characteristics of the built environment around metro stations largely affects whether they choose the metro to travel either to or from school.…”

Section: Introductionmentioning

confidence: 99%

The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models

Liu

Shi

et al. 2018

Sustainability

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Long-distance school commuting is a key aspect of students' choice of car travel. For cities lacking school buses, the metro and car are the main travel modes used by students who have a long travel distance between home and school. Therefore, encouraging students to commute using the metro can effectively reduce household car use caused by long-distance commuting to school. This paper explores metro ridership at the station level for trips to school and return trips to home in Nanjing, China by using smart card data. In particular, a global Poisson regression model and geographically weighted Poisson regression (GWPR) models were used to examine the effects of the built environment on students' metro ridership. The results indicate that the GWPR models provide superior performance for both trips to school and return trips to home. Spatial variations exist in the relationship between the built environment and students' metro ridership across metro stations. Built environments around metro stations, including commercial-oriented land use; the density of roads, parking lots, and bus stations; the number of docks at bikeshare stations; and the shortest distance between bike stations and metro stations have different impacts on students' metro ridership. The results have important implications for proposing relevant policies to guide students who are being driven to school to travel by metro instead.

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Associations between parents׳ perception of traffic danger, the built environment and walking to school

Cited by 75 publications

References 47 publications

Getting at Mode Share: Comparing 3 Methods of Travel Mode Measurement for School Travel Research

Getting at Mode Share: Comparing 3 Methods of Travel Mode Measurement for School Travel Research

Children’s Transport Built Environments: A Mixed Methods Study of Associations between Perceived and Objective Measures and Relationships with Parent Licence for Independent Mobility in Auckland, New Zealand

The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models

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