Cycle accessibility and level of traffic stress: A case study of Toronto

Faghih-Imani, Ahmadreza; Miller, Eric J.; Saxe, Shoshanna

doi:10.1016/j.jtrangeo.2019.102496

Cited by 20 publications

(12 citation statements)

References 44 publications

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“…The level of traffic stress (LTS) for each link in the Toronto road network was labelled as LTS1-4 following the approach of Furth, Mekuria, and Nixon (2016) and Imani, Miller, and Saxe (2019). LTS1 indicates low-stress roads for all cyclists including children.…”

Section: Methodsmentioning

confidence: 99%

“…Cycling accessibility was calculated following Imani, Miller, and Saxe (2019) using a 30-minute isochrone at each LTS for each census dissemination area (DA), and a 15 km/h travel speed. Cumulative opportunities for each DA were calculated at each LTS for five indicators: area, populations, jobs, food stores, and park area.…”

Section: Methodsmentioning

confidence: 99%

“…A park was considered accessible if it intersected the isochrone. We focused on accessibility at LTS2 because it corresponds to the LTS tolerance of the mainstream population (Dill and McNeil 2016) and is predictive of mode choice (Imani, Miller, and Saxe 2019).…”

Section: Methodsmentioning

confidence: 99%

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The Impact of COVID-19 Cycling Infrastructure on Low-Stress Cycling Accessibility: A Case Study in the City of Toronto

Chan

Saxe

2021

Findings

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This paper investigates the impact of COVID-19 cycling infrastructure on Toronto's low-stress cycling accessibility. We calculated the level of traffic stress (LTS) for the road network with and without the cycling infrastructure built in response to the COVID-19 pandemic. We then computed the accessibility to populations, jobs, food stores, and parks for each dissemination area at each LTS. We find that the COVID-19 cycling infrastructure increased low-stress population and job accessibility by 10.4% and 22.3% respectively, and granted food and park access to 54,023 and 3,178 people. Accessibility gains were largest in areas where new infrastructure linked with preexisting networks.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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The Impact of COVID-19 Cycling Infrastructure on Low-Stress Cycling Accessibility: A Case Study in the City of Toronto

Chan

Saxe

2021

Findings

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“…The application of this planning assessment tool in Moscow, ID, was done to evaluate a set of capital investment scenarios for improving bikeability within the city. Imani et al ( 12 ) applied a four-tiered level of traffic stress (LTS) measure ( 13 ) to a street network in Toronto, Canada, while adopting a cumulative opportunities accessibility metric to estimate bicycling access within a 30-min commute based on LTS thresholds. Neighborhood cycling access to jobs was bifurcated at 5,000 jobs to distinguish between low and high access levels, which informed the specification of a binary logit model to identify associated predictors.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Neighborhood Differences in Bicycling Accessibility to Physical and Virtual Workplaces

Martínez

Phair

Russo

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Health concerns brought about by the Covid-19 pandemic, coupled with technological advancements over the past two decades have altered the traditional workplace setting. Some professionals in the tertiary and quaternary economic sectors have grown accustomed to virtual working environments, whereas others, including essential workers, have experienced less flexibility in this regard and continue to commute to physical work settings. During the pandemic, residents of underresourced communities, who were more likely to adopt lower-cost shared-use travel modes and would have benefited from good access to high-quality bicycling infrastructure, embodied a greater share of the nonhealthcare workforce who was required to commute to a physical work setting. This circumstance highlighted a need for research that examines bicycling accessibility to workplaces distinguished by telework potential, with a hypothesis that underresourced communities with more limited mobility options have poorer bicycling access to traditional workplace settings. In response, this study, which describes the application of a novel bicyclist routing algorithm, investigated how these differences in job accessibility relate to variations in the social context of neighborhoods where bicyclist commute trips originate, and the level of traffic stress incurred by a current or prospective bicyclist along modeled routes in Flagstaff, AZ. Study findings, which in part confirmed its hypothesis while also noting a latent demand for bicycling to physical workplaces along low-stress facilities, offer unique insights into how the changing nature of work should be accounted for as active transportation planners and policy makers seek to provide safer and more robust bicycle networks to their diverse communities.

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“…None of the tools we evaluated yet balances all of these criteria, though researchers and software developers are continuing to develop new measures and enhance existing ones that are moving closer to doing so. These enhancements include a) incorporating trip chaining and scheduling into utility-based measures (Dong et al, 2006) and accounting in various ways in place-based measures for the effects of b) travel time reliability (Chen et al, 2017;Conway et al, 2017), c) traffic congestion (Vandenbulcke et al, 2009), d) transportation system reliability and robustness (Liao & van Wee, 2017), e) competition among workers for jobs (Cheng & Bertolini, 2013;Ong & Blumenberg, 1998;van Wee et al, 2001), f) employment diversity (Cheng & Bertolini, 2013), g) job matching based on skills and qualifications (Pan et al, 2020), and h) level of traffic stress affecting cycling (Gehrke et al, 2020;Imani et al, 2019;McCahill et al, 2017). Dynamic accessibility tools have also been developed to account for temporal variations in accessibility (Lee & Miller, 2018;Wang et al, 2018), such as between peak congested and off-peak free-flowing traffic, when waits for public transit are short or long, and between weekdays and weekends.…”

Section: Building a Better Measurementioning

confidence: 99%