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Record W574142175

Safe and Active Routes to School Program: Experiences and Lessons Learned

2006· article· en· W574142175 on OpenAlexaboutno aff
Sahilali Saiyed, Jacky Kennedy

Bibliographic record

Venue2006 ITE Annual Meeting and Exhibit Compendium of Technical PapersInstitute of Transportation Engineers (ITE) · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)EnforcementSustainable transportBusinessPublic transportScheduleTransport engineeringTransportation planningTraffic congestionEnvironmental planningEngineeringSustainabilityPolitical scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper describes how recent surveys in developed countries have found that very few children walk to school. This is most common in families, where both parents work outside their home, and driving their children to school is the only option to meet their demanding work and home schedule. The increasing use of automobiles has created traffic congestion on roads, spiraling accident fatalities and injuries, environmental pollution, reduced opportunities for daily physical activity for children, thus affecting economic well-being and health of the people in the community. As a result, many communities are implementing Safe and Active Routes to School programs to assess community infrastructure and encourage families to use active travel. Unfortunately, most transportation planners and engineers have concentrated their efforts in providing an effective transportation system for automobile and transit modes. The non-motorized or active transportation modes such as walking, cycling and in-line skating have been either neglected or not recognized as they should have been. It is very essential that transportation and land use planning are integrated to promote sustainable and active transportation modes. This paper emphasizes how Ontario communities are applying the 5 E’s (Engineering, Enforcement, Encouragement, Education and Evaluation) to encourage sustainable and active modes of transportation. Green Communities: Active & Safe Routes to School program in Ontario, Canada, has grown steadily over ten years, building it’s strengths on developing community based programs that include all stakeholders: school districts, municipal transportation staff, police, public health professionals and local groups with an interest in safety, sustainable transportation and health. In Peel Region the program has been in place since 1999 and was renamed by the local committee as the Peel Safe and Active Routes to School (PSARTS). The Region of Peel comprises of three municipalities: Mississauga, Brampton and the Town of Caledon with a population of 1.15 million people. The Peel SARTS program promotes International Walk to School (IWALK) and participation is growing – in 2005 over 107 schools participated. Schools are then encouraged to hold regular walking challenges, like Walking/Wheeling Wednesdays to keep the momentum going. Traffic and other safety issues are addressed through the local Traffic Safety Councils and, where needed, changes are made to schools zones or along routes to school to increase the safety of students walking or biking. The Peel SARTS committee has created resources with a local flavor and step-by-step guidelines on how to implement a program. The authors are thrilled in Peel to recognize the efforts of Morton Way Public School, the winner of the second International Walk to School Award. This school has been involved in the Peel SARTS program for six years, starting with International Walk to School Day and introducing Walking Wednesdays, Walking School Buses, and innovative campaigns to encourage parents to leave their cars at home.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0040.005
Open science0.0030.007
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.289
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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