den Braver Nicolette R, Lakerveld Jeroen, Gozdyra Peter, van de Brug Tim, Moin John S, Fazli Ghazal S, Rutters Femke, Brug Johannes, Moineddin Rahim, Beulens Joline W J, Booth Gillian L
Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands; Upstream Team, www.upstreamteam.nl, Amsterdam, the Netherlands.
Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
Environ Int. 2022 May;163:107182. doi: 10.1016/j.envint.2022.107182. Epub 2022 Mar 17.
Car driving is a form of passive transport that is associated with an increase in physical inactivity, obesity, air pollution and noise. Built environment characteristics may influence transport mode choice, but comprehensive indices for built environment characteristics that drive car use are still lacking, while such an index could provide tangible policy entry points.
We developed and validated a neighbourhood drivability index, capturing combined dimensions of the neighbourhood environment in the City of Toronto, and investigated its association with transportation choices (car, public transit or active transport), overall, by trip length, and combined for residential neighbourhood and workplace drivability.
We used exploratory factor analysis to derive distinct factors (clusters of one or more environmental characteristics) that reflect the degree of car dependency in each neighbourhood, drawing from candidate variables that capture density, diversity, design, destination accessibility, distance to transit, and demand management. Area-level factor scores were then combined into a single composite score, reflecting neighbourhood drivability. Negative binomial generalized estimating equations were used to test the association between driveability quintiles (Q) and primary travel mode (>50% of trips by car, public transit, or walking/cycling) in a population-based sample of 63,766 Toronto residents enrolled in the Transportation Tomorrow Survey (TTS) wave 2016, adjusting for individual and household characteristics, and accounting for clustering of respondents within households.
The drivability index consisted of three factors: Urban sprawl, pedestrian facilities and parking availability. Relative to those living in the least drivable neighbourhoods (Q1), those in high drivability areas (Q5) had a significantly higher rate of car travel (adjusted Risk Ratio (RR): 1.80, 95%CI: 1.77-1.88), and lower rate of public transit use (RR: 0.90, 95%CI: 0.85-0.94) and walking/cycling (RR: 0.22, 95%CI: 0.19-0.25). Associations were strongest for short trips (<3 km) (RR: 2.72, 95%CI: 2.48-2.92), and in analyses where both residential and workplace drivability was considered (RR for car use in high/high vs. low/low residential/workplace drivability: 2.18, 95%CI: 2.08-2.29).
This novel neighbourhood drivability index predicted whether local residents drive or use active modes of transportation and can be used to investigate the association between drivability, physical activity, and chronic disease risk.
汽车驾驶是一种被动出行方式，与身体活动减少、肥胖、空气污染和噪音增加有关。建成环境特征可能会影响出行方式的选择，但目前仍缺乏用于驱动汽车使用的建成环境特征综合指标，而这样一个指标可以为政策制定提供切实的切入点。
我们开发并验证了一个邻里可驾车性指数，该指数涵盖了多伦多市邻里环境的综合维度，并研究了其与交通选择（汽车、公共交通或主动出行）之间的关联，总体上、按出行长度以及结合居住邻里和工作场所的可驾车性进行研究。
我们使用探索性因子分析从反映每个邻里对汽车依赖程度的候选变量中得出不同的因子（一个或多个环境特征的聚类），这些候选变量涵盖密度、多样性、设计、目的地可达性、到公共交通的距离以及需求管理。然后将区域层面的因子得分合并为一个单一的综合得分，以反映邻里可驾车性。使用负二项式广义估计方程来检验可驾车性五分位数（Q）与主要出行方式（汽车出行、公共交通出行或步行/骑行出行占比超过50%）之间的关联，该分析基于参与2016年交通明日调查（TTS）第二轮的63766名多伦多居民的样本进行，对个人和家庭特征进行了调整，并考虑了家庭内部受访者的聚类情况。
可驾车性指数由三个因子组成：城市蔓延、行人设施和停车位可用性。相对于居住在可驾车性最低的邻里（Q1）的居民，居住在高可驾车性区域（Q5）的居民汽车出行率显著更高（调整后风险比（RR）：1.80，95%置信区间（CI）：1.77 - 1.88），公共交通使用率更低（RR：0.90，95%CI：0.85 - 0.94），步行/骑行率更低（RR：0.22，95%CI：0.19 - 0.25）。短途出行（<3公里）的关联最强（RR：2.72，95%CI：2.48 - 2.92），并且在同时考虑居住和工作场所可驾车性的分析中（高/高与低/低居住/工作场所可驾车性情况下的汽车使用率RR：2.18，95%CI：2.08 - 2.29）。
这个新的邻里可驾车性指数能够预测当地居民是开车还是使用主动出行方式，并可用于研究可驾车性、身体活动和慢性病风险之间的关联。
