Crowd-Sourced Traffic Data: Predicting Air Pollution & Ischemic Stroke

众包交通数据：预测空气污染

• 批准号: 10364079
• 负责人: Amelia K Boehme
• 金额: $ 1.33万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-08 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10364079
• 关键词: Accounting; Acute; Adult; Affect; Air; Air Pollution; Area; Attenuated; Automobile Driving; Brain hemorrhage; Buffers; Calibration; Car Phone; Carbon Black; Cardiovascular Diseases; Cardiovascular system; Cause of Death; Cellular Phone; Cities; Classification; Code; Color; Community Health; Congestive; Cross-Over Studies; Data; Data Sources; Detection; Devices; Diesel Exhaust; Evaluation; Event; Exposure to; Goals; Health; Home; Hospitalization; Hour; Intervention; Ischemic Stroke; Link; Location; Maps; Measurement; Measures; Meteorology; Methods; Modeling; Monitor; Neighborhoods; New York; New York City; Nitrogen Dioxide; Noise; Outcome; Partner in relationship; Pathway interactions; Pensions; Pilot Projects; Public Health; Radar; Research; Research Design; Risk; Route; Sampling; Series; Site; Source; Speed; Stroke; System; Telephone; Time; Tracer; Transportation; Travel; Variant; Washington; base; crowdsourcing; design; disability; disorder prevention; fine particles; health assessment; health data; improved; mortality; predictive modeling; spatiotemporal; stroke event; stroke incidence; temporal measurement; traffic-related air pollution

ABSTRACT Air pollution, particularly traffic-related air pollution (TRAP), affects community health including cardiovascular disease and ischemic stroke onset. Stroke is the fifth leading cause of death in the US and the number one cause of long-term adult disability. Stroke has been consistently associated with daily average measurements of TRAP but much less often to traffic itself. Methods are needed to monitor traffic in real time to improve our ability to disentangle the effects of traffic on air pollution and stroke onset at both the neighborhood and citywide scale. Accounting for spatio-temporal variations is important to assessing acute affects since different people can get exposed to TRAP at different times of the day and locations. Key to predicting variations of TRAP is measurement of temporal variations in traffic conditions across the city. Evaluation of data obtained from GPS-enabled mobile phones present in driving vehicles offers the opportunity of inexpensive, real-time traffic monitoring of entire street networks, which cannot be done with traditional traffic monitors. There is great potential of using such crowd-sourced data in air pollution and public health studies. Our overarching hypothe- sis is that the links connecting neighborhood-scale traffic congestion to air pollution and stroke onset can be inexpensively examined by use of crowd-sourced traffic data, e.g., from Google Traffic (GT). In a pilot study, we showed that the five colors that Google uses to indicate congestion of road segments are a measure for vehicle speed based on radar measurements. We also showed that time series of traffic flow (hourly vehicle counts) can be locally inferred from an ordinal Google color code GCC we assigned to the GT colors. Finally we showed that both traffic flow and speed (either derived from a radar-device or GCC) explain levels of black carbon (BC), a tracer for TRAP. BC levels could be predicted from GCC and often readily avail- able annual average daily traffic (AADT) data as well as together with other non-traffic related covariates. Our overall goal is to show that crowd-sourced traffic data can be used to estimate TRAP and that these traffic data estimates can be used directly to investigate associations with health outcomes (without use of air pollution data). Our specific aims are to (1) demonstrate that temporally varying BC levels can be inferred from GT data, and (2) examine the associations between the risk of ischemic stroke onset and traffic conditions in the hours and days preceding each event. We hypothesize that the onset of ischemic stroke will be associated with hourly traffic measurements at the home locations (i.e., within a small buffer zone). The associations we will derive between stroke onset and crowd-sourced traffic data can establish a direct link to the source of TRAP and provide critical information for disease prevention, intervention planning and treatment. Our proposed use of crowd-sourced traffic data can be applied anywhere in the US and to health outcomes other than stroke.

抽象的 空气污染，尤其是交通相关的空气污染（TRAP），会影响社区健康，包括心血管 疾病和缺血性中风发作。中风是美国的第五大死亡原因，排名第一 长期成人残疾的原因。中风一直与每日平均测量 陷阱，但频率少得多。需要方法来实时监控流量以改善我们的 能够消除交通对邻里和中风的影响的影响 全市范围。考虑时空变化的考虑对于评估急性影响很重要，因为不同 人们可以在一天中的不同时间和位置接触陷阱。预测变化的关键 陷阱是对整个城市交通状况的时间变化的测量。评估获得的数据 从驾驶车辆中使用的基于GPS的手机中提供了便宜，实时的机会 对整个街道网络的交通监控，这是传统交通监视器无法完成的。那里很棒 在空气污染和公共卫生研究中使用此类众包数据的潜力。我们的总体假设 SIS是将社区规模交通拥堵连接到空气污染和中风发作的链接可以是 通过使用众包流量数据（例如，从Google流量（GT））进行廉价检查。 在一项试点研究中，我们表明Google用来表明路段充血的五种颜色 是基于雷达测量值的车辆速度的量度。我们还展示了交通流量的时间序列 （每小时车辆计数）可以从我们分配给GT的序列Google颜色代码GCC本地推断出 颜色。最后，我们证明了交通流量和速度（源自雷达设备或GCC）解释 黑碳（BC）的水平，陷阱的示踪剂。 BC水平可以从GCC预测，并且经常很容易获得 - 年度平均每日流量（AADT）数据以及其他非交通相关的协变量。 我们的总体目标是表明众包流量数据可用于估计陷阱，并且这些 交通数据估算可直接用于调查与健康结果的关联（无需使用空气 污染数据）。我们的具体目的是（1）证明可以从时间上变化的BC水平可以从 GT数据和（2）检查缺血性中风的风险与交通状况之间的关联 每个事件之前的小时数。我们假设缺血性中风的发作将是相关的 在家庭位置（即在一个小缓冲区内）进行小时的交通测量值。 我们将在中风发作和众包流量数据之间得出的关联可以建立直接 链接到陷阱的来源，并为疾病预防，干预计划和 治疗。我们建议对众料流量数据的使用，可以在美国任何地方应用于健康 除了中风以外的结果。