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

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
批准号:
2106808
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --

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中文摘要
翻译
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。
英文摘要
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.
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
复杂数据下半参数转换模型及其在老年慢性病发展中的应用研究
  • 批准号:
    72101261
  • 项目类别:
    青年科学基金项目(C类)
  • 资助金额:
    30.0万元
  • 批准年份:
    2021
  • 负责人:
    孙韬
  • 依托单位:
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    40万元
  • 批准年份:
    2020
  • 负责人:
    Vikrant Gupta
  • 依托单位: