Nationally scalable and adaptive methods for traffic estimation with open data

对于许多从业者来说，交通规划的证据基础中缺失的一个缺口是关于道路交通的公开数据：交通量、速度、成分、配时、随时间变化。需要道路交通状况的知识，以及反映不确定性的可信区间(因为没有一个数据集或模型是完美的)，需要改进交通规划，特别是在活跃出行的背景下。机动车交通量和速度与骑自行车和行人受伤增加有关(Aldred 2019)，因此阻碍了积极旅行的开展(Aldred和Goodman 2018)及其带来的好处，如改善身心健康和脱碳(交通部2020)。因此，对积极出行方式的关注导致对当地证据的需求增加，以提供从新自行车道到改善路面质量的干预措施，这一点也就不足为奇了。该项目将探索可用的数据集和工具，以了解、优先和设计活跃的出行基础设施，包括自行车道、人行道、过境点和缓解交通的功能。产出将包括从一系列数据来源中提取有关步行和骑自行车的本地供应(和差距)的新方法，捕捉基础设施数据集的政策相关元素的“来源不可知”表示法，以及关于英国现有交通基础设施可用数据集的优势和劣势的政策相关发现。鉴于目前是对基于证据的可持续交通政策的高需求时期，预计该项目将在国际上得到应用。在研究背景方面，已经发现来自OSM的公开数据可以提供车辆数量的代理(Chan和Cooper 2019)。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分解OD数据集之后(Lovelace、Félix和Carlino 2022)。PHD将通过开发新的混合模型来解决这个问题，该模型将机器学习、空间网络分析和起点-目的地分析相结合，以生成对交通量的估计(随后还会产生其他变量，如速度和成分)。将探索贝叶斯方法，以便在该地区最近工作的基础上，提供预测交通量和对道路安全影响的中央估计之外的置信度区间(例如，Gilardi等人)。2022年)。最后，后面的步骤将涉及将结果与开放访问的道路碰撞数据进行比较(Lovelace等人。2019年)。预期结果：链路水平的道路交通开放估计以PCU/天为单位测量的道路交通水平的中央估计，在连接到OSM道路几何的置信度区间表示观察值预计在90%的时间内下降的范围(这可以通过真实世界的实验来验证模型)开放访问代码和模型，使得能够随着更多的数据上线(例如，随着道路计数器数据集的更新)更新全国道路交通估计。例如，审查后COVID封锁对作为工作基础的方法的影响；对结果进行经验数据分析，例如将道路交通量与现行的旅行基础设施和/或交通部指导的行为产出和活动进行比较，例如地方当局的教程；从政策角度使用产出的报告；以及举办讲习班，与利益攸关方就利用交通数据进行运输规划进行能力建设。研究导致的政策变化

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