Neighborhood Effects of Safe Routes to School Programs on the Likelihood of Active Travel to School

Author(s):  
Carole T. Voulgaris ◽  
Reyhane Hosseinzade ◽  
Anurag Pande ◽  
Serena E. Alexander

Safe routes to school (SRTS) programs aim to increase the share of students commuting to school by active modes (e.g., walking and cycling). This study measures the relationship between the presence of SRTS programs in a neighborhood and children’s journey-to-school mode choice. Children were identified from households in the 2012 California Household Travel Survey and they were classified based on whether they commuted to school by active modes. Next, census tracts with SRTS programs were identified based on the presence of data in the National Center for Safe Routes to School (NCSRTS) data collection system. Based on these two datasets, a logistic regression model estimated the likelihood that a child commuted to school by active modes, based on the presence of a SRTS program and controlling for individual, household, and tract characteristics. This analysis was supplemented with stakeholder interviews about the nature of SRTS programs within the study area and how they are perceived. Findings indicate that longer trip distance and race (relative to white students) are associated with reduced rates of active travel to school, but that these differences are mitigated by the presence of SRTS programs. Interviews suggest SRTS programs in the study area primarily emphasize education and encouragement rather than engineering interventions. It was concluded that the effect of such SRTS programming might best be described as reducing barriers to active school travel rather than simply increasing the likelihood of using active modes.

2012 ◽  
Vol 18 (1) ◽  
pp. 8-15 ◽  
Author(s):  
Jamie F. Chriqui ◽  
Daniel R. Taber ◽  
Sandy J. Slater ◽  
Lindsey Turner ◽  
Kerri McGowan Lowrey ◽  
...  

2018 ◽  
Author(s):  
Rhiannon Lee White ◽  
Philip D. Parker ◽  
David R. Lubans ◽  
Freya MacMillan ◽  
Rebecca Olson ◽  
...  

2011 ◽  
Vol 81 (12) ◽  
pp. 741-748 ◽  
Author(s):  
Anna E. Price ◽  
Delores M. Pluto ◽  
Olga Ogoussan ◽  
Jorge A. Banda

1987 ◽  
Vol 19 (6) ◽  
pp. 735-748 ◽  
Author(s):  
S Hanson ◽  
M Schwab

This paper contains an examination of the fundamental assumption underlying the use of accessibility indicators: that an individual's travel behavior is related to his or her location vis-à-vis the distribution of potential activity sites. First, the conceptual and measurement issues surrounding accessibility and its relationship to travel are reviewed; then, an access measure for individuals is formulated. Using data from the Uppsala (Sweden) Household Travel Survey and controlling for sex, automobile availability, and employment status, the authors explore the relationship between both home- and work-based accessibility and five aspects of an individual's travel: mode use, trip frequencies and travel distances for discretionary purposes, trip complexity, travel in conjunction with the journey to work, and size of the activity space. From the results it can be seen that although all of these travel characteristics are related to accessibility to some degree, the travel–accessibility relationship is not as strong as deductive formulations have implied. High accessibility levels are associated with higher proportions of travel by nonmotorized means, lower levels of automobile use, reduced travel distances for certain discretionary trip purposes, and smaller individual activity spaces. Furthermore, the density of activity sites around the workplace affects the distances travelled by employed people for discretionary purposes. Overall, accessibility level has a greater impact on mode use and travel distance than it does on discretionary trip frequency. This result was unexpected in light of the strong trip frequency–accessibility relationship posited frequently in the literature.


Author(s):  
Ryland Lu

This paper addresses academic discourse that critiques urban rail transit projects for their regressive impacts on the poor and proposes bus funding as a more equitable investment for urban transit agencies. The author analyzed data from the 2012 California Household Travel Survey on transit trips in Los Angeles County. The author cross-tabulated data on the modal breakdown of transit trips by household income category and on the breakdown of household income associated with trips by bus and rail transit modes. The author also comparatively evaluated the speed of trips (as a ratio of miles per hour) taken by rail and by bus by low-income households in the county. The author found convincing evidence that, on average, trips low-income households made by rail transit covered a greater distance per hour than trips taken by bus transit, but that trips made on the county’s bus rapid transit services with dedicated rights-of-way had a higher mean speed than those taken by rail. Moreover, the mode and income cross-tabulations indicate that rail transit projects only partially serve low-income households’ travel needs. To the extent that equitable transit planning entails minimizing the disparities in access, both rail and bus rapid transit projects can advance social justice if they are targeted at corridors where they can serve travel demand by low-income, transit dependent households.


2021 ◽  
Author(s):  
◽  
Edward Johnsen

<p>Economic agents frequently make joint decisions, which often require a compromise by some or all of the participants. We propose an econometric model in which groups of agents make a joint decision; each agent has preferences modelled using a combination of multi-nominal logit and conditional logit parts. We combine these marginal preferences to create a joint set of probabilities of the group making a particular choice, which enables parameter estimation by maximum likelihood. We can also make the weight applied to an individual agents preferences depend on characteristics of the agent or group. To demonstrate the use of the model, data is obtained from the New Zealand Household Travel Survey. We estimate our model to show how households might make the joint decision of where to live, given that different household members have different work locations.</p>


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