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

摘要 空气污染，特别是交通相关的空气污染（TRAP），影响社区健康，包括心血管疾病 疾病和缺血性中风发作。中风是美国第五大死亡原因， 成年人长期残疾。中风一直与每日平均测量值相关 但很少有交通本身。需要真实的实时监控流量的方法，以改善我们的 有能力理清交通对空气污染和中风发作的影响， 全市规模。考虑到时空变化对评估急性影响很重要，因为不同的 人们可以在一天中的不同时间和地点接触TRAP。关键在于预测 TRAP是衡量整个城市交通状况随时间变化的指标。对所获数据的评价 从驾驶车辆中存在的支持GPS的移动的电话提供了廉价、实时 整个街道网络的交通监控，这是传统的交通监控器无法做到的。有很大 在空气污染和公共卫生研究中使用这种众包数据的潜力。我们首要的假设- SIS是将社区规模的交通拥堵与空气污染和中风发作联系起来的联系， 通过使用众包交通数据进行廉价检查，例如，Google Traffic（GT） 在一项试点研究中，我们发现，谷歌用来表示路段拥堵的五种颜色 是基于雷达测量的车辆速度的测量。我们还发现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的顺序Google颜色代码GCC中进行本地推断 颜色.最后，我们表明，交通流量和速度（无论是来自雷达设备或GCC）解释， 黑碳（BC），TRAP的示踪剂。BC水平可以从GCC中预测，并且通常很容易获得- 年平均日交通量（AADT）数据以及其他非交通相关协变量。 我们的总体目标是表明，众包的交通数据可以用来估计陷阱，这些 交通数据估计可直接用于调查与健康结果的关联（不使用空气 污染数据）。我们的具体目标是：（1）证明时间上变化的BC水平可以从 GT数据，以及（2）检查缺血性卒中发作风险与交通条件之间的关系， 每一个事件发生前的几个小时和几天。我们假设缺血性卒中的发生与 利用归属位置处的每小时业务量测量（即，在一个小的缓冲区内）。 我们将得出的中风发作和人群来源的交通数据之间的关联可以建立一个直接的 链接到TRAP的来源，为疾病预防、干预计划和 治疗我们提出的使用众包交通数据可以应用于美国任何地方和健康 除了中风之外的其他结果。

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.