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Towards data-driven policy development: the case of London's built cycling infrastructure

迈向数据驱动的政策制定：以伦敦建成的自行车基础设施为例

基本信息

批准号：
2106808
负责人：
金额：
--
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2106808
关键词：
Towards data driven policy development

项目摘要

In 2013, £913m of funds was allocated over 10 years for investment in London's cycling infrastructure. Much of this - including guided quietways, protected cycle superhighways and London's crossrail for the bike - opened in summer 2016. The chief objective: to make cycling 'a normal part of everyday life [...] something people hardly think about [...and] something everyone feels comfortable doing' (Greater London Authority 2013).Traditionally, attempts to evaluate such interventions might rely on survey data describing changes in *claimed* behaviour or high-level data from Automatic Traffic Counters describing infrastructure occupancy. The former are often expensive to collect and suffer from numerous (well-documented) biases and the latter are too high-level to capture more subtle changes in behaviour.This project will instead use new, large-scale observational datasets - from London's bikeshare, underground and bus network, from route planning services (CycleStreets.net), user-contributed and social media data -- to describe changes in city-wide cycling behaviours pre- and post- the intervention. Crucially, it will identify rich detail around the impact of current investment on behaviour and contribute quantified estimates, under uncertainty, around the impact of future investment.

2013年，英国政府在10年内拨出9.13亿GB资金，用于投资伦敦的自行车基础设施。其中大部分--包括有导游的安静通道、受保护的自行车高速公路和伦敦的自行车横梁--于2016年夏天开放。主要目标是：让骑自行车成为日常生活的一部分，这是人们几乎想不到的事情，也是每个人都觉得舒服的事情(大伦敦管理局，2013)。传统上，评估这种干预措施的尝试可能依赖于描述*声称的*行为变化的调查数据，或者来自自动交通计数器的描述基础设施占用情况的高级数据。前者的收集成本往往很高，而且存在大量(有充分记录的)偏差，而后者的水平太高，无法捕捉到更微妙的行为变化。该项目将使用新的大规模观察性数据集--来自伦敦的自行车共享、地铁和公交网络、路线规划服务(CycleStreets.net)、用户贡献的数据和社交媒体数据--来描述干预前后全市范围内骑自行车行为的变化。至关重要的是，它将确定当前投资对行为影响的丰富细节，并在不确定的情况下，对未来投资的影响做出量化估计。