Nationally scalable and adaptive methods for traffic estimation with open data

使用开放数据进行全国可扩展和自适应的流量估计方法

• 批准号: 2747587
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2747587
• 关键词: Nationally; scalable; adaptive; methods; traffic

A missing gap in the evidence base for transport planning, for many practitioners, is open data on road traffic: volumes, speeds, composition, timings, change over time. Knowledge of road traffic conditions, with confidence intervals to reflect uncertainty (for no dataset or model is perfect), is needed improve transport planning, especially in the context of active travel. Motor traffic volume and speed are associated with increased cycling and pedestrian injuries (Aldred 2019), hence impeding the uptake of active travel (Aldred and Goodman 2018) and the benefits it brings, such as improved mental and physical health and decarbonization (Department for Transport 2020). Therefore, it is not surprising that focus on active modes of travel has led to an increased demand for local evidence to inform interventions ranging from new cycleways to improved pavement quality. This project will explore available datasets and tools to understand, prioritise and design active travel infrastructure, including cycleways, pavements, crossing points and traffic-calming features. Outputs will include new methods for extracting insight from a range of data sources on local provision (and gaps) for walking and cycling, 'source-agnostic' representations to capture policy relevant elements of infrastructure datasets and policy relevant findings on strengths and weaknesses in available datasets on active transport infrastructure in the UK. Given that it is a time of high demand for evidence-based sustainable transport policies, the project is expected to have international applications. In terms of research background, it has been found that open data from OSM can provide a proxy for vehicle volumes (Chan and Cooper 2019). Another study on estimating traffic volumes have found that OD data analysis could produced desired results, especially after new 'jittering' methods to dis-aggregated OD datasets are used (Lovelace, Félix, and Carlino 2022). The PhD will tackle the problem by developing new hybrid models, that combine machine learning, spatial network analysis, and origin-destination analysis to generate estimates of traffic volumes (and subsequently other variables such as speed and composition). Bayesian approaches will be explored, to provide confidence intervals in addition to central estimates of predicted traffic and impacts on road safety, building on recent work in the area (e.g. Gilardi et al. 2022). Finally, later steps will involve comparing the results with open access road crash data (Lovelace et al. 2019). Expected outcomes: Open estimates of road traffic at the link level Central estimates of road traffic levels, measured in terms of PCU/day, at the link level joined to OSM road geometries Confidence intervals representing the range within which observed values would be expected to fall 90% of the time (this could be followed-up by real world experiments to validate the model) Open access code and models enabling updating of road traffic estimates nationwide as more data comes online (e.g. as road counter datasets are updated) Academic papers Exporatory analysis of road traffic data, e.g. looking at post COVID lockdown related impacts On the methods underlying the work Empirical data analysis of the results, e.g. comparing road traffic volumes with active travel infrastructure and/or behaviour Outputs and activities directed by the DfT, e.g. tutorials for local authorities, reports on using the outputs from a policy perspective, and a workshop to build capacity with stakeholders on making use of traffic data for transport planning. Policy changes resulting from the research

对于许多从业者来说，交通规划证据库中缺少的一个空白是道路交通的公开数据：数量，速度，组成，时间，随时间的变化。道路交通状况的知识，用置信区间来反映不确定性（因为没有数据集或模型是完美的），需要改进交通规划，特别是在主动出行的情况下。机动车交通量和速度与骑自行车和行人受伤的增加有关（Aldred 2019），因此阻碍了积极旅行的吸收（Aldred和Goodman 2018）及其带来的好处，例如改善身心健康和脱碳（运输部2020）。因此，毫不奇怪，对积极的旅行方式的关注导致对当地证据的需求增加，以告知从新的自行车道到改善路面质量的干预措施。该项目将探索可用的数据集和工具，以了解，优先考虑和设计主动旅行基础设施，包括自行车道，人行道，交叉点和交通平静功能。产出将包括从一系列数据源中提取洞察力的新方法，这些数据源涉及步行和骑自行车的本地供应（和差距），“源不可知”表示以捕获基础设施数据集的政策相关元素，以及英国主动交通基础设施可用数据集的优势和劣势的政策相关发现。鉴于目前对循证可持续交通政策的需求很高，该项目有望在国际上得到应用。 在研究背景方面，已经发现来自OSM的开放数据可以提供车辆数量的代理（Chan和库珀，2019年）。另一项关于估计交通量的研究发现，OD数据分析可以产生预期的结果，特别是在使用新的“抖动”方法来分解OD数据集之后（Lovelace，Félix和卡利诺，2022年）。该博士将通过开发新的混合模型来解决这个问题，该模型将联合收割机学习、空间网络分析和起点-目的地分析相结合，以生成交通量的估计（以及随后的速度和组成等其他变量）。将探索贝叶斯方法，以提供除了预测交通量和对道路安全影响的中心估计之外的置信区间，以该领域最近的工作为基础（例如Gilardi等人，2022年）。最后，后续步骤将涉及将结果与开放式道路碰撞数据进行比较（Lovelace et al. 2019）。预期成果：路段级别道路交通的开放估计道路交通水平的中央估计，以PCU/天衡量，在连接到OSM道路几何形状的链路级别上置信区间表示观测值在90%的时间内预计会落在的范围（这可以通过真实的世界实验来验证模型）开放获取代码和模型，随着更多数据的上线，可以更新全国范围内的道路交通估计（例如，随着道路计数器数据集的更新）学术论文道路交通数据的探索性分析，例如，查看COVID封锁后的相关影响关于工作方法对结果进行经验数据分析，例如，将道路交通量与活跃的旅行基础设施和/或行为进行比较输出和DfT指导的活动，例如，为地方当局提供教程，关于从政策角度使用产出的报告，以及一个讲习班，与利益攸关方一起建设利用交通数据进行交通规划的能力。研究导致的政策变化