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

摘要 空气污染，特别是与交通相关的空气污染(TRAP)，影响包括心血管在内的社区健康 疾病和缺血性中风的发病。在美国，中风是第五大死因，也是第一大死因 成人长期残疾的原因。中风一直与每日平均测量值有关 但对交通本身的影响要小得多。需要各种方法来实时监控流量，以提高我们的 能够理清交通对空气污染和附近地区中风发病的影响 全市范围的规模。考虑到时空差异对于评估急性影响很重要，因为不同的 人们可能会在一天中的不同时间和地点接触到陷阱。预测变化的关键 Trap是对整个城市交通状况的时间变化的测量。对所获得的数据进行评价 在驾驶车辆中出现的支持GPS的手机提供了廉价、实时的机会 对整个街道网络的交通监控，这是传统的交通监控器无法完成的。有很棒的 在空气污染和公共卫生研究中使用这种众包数据的可能性。我们最重要的假设是- SIS是，连接社区规模的交通拥堵与空气污染和中风发病的链接可以是 通过使用例如来自谷歌交通(GT)的众包交通数据进行廉价检查。 在一项初步研究中，我们展示了谷歌用来表示路段拥堵的五种颜色 是一种基于雷达测量的车辆速度测量方法。我们还显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的一个有序的谷歌颜色代码GCC中本地推断出来 颜色。最后，我们证明了车流量和车速(来自雷达设备或GCC)都可以解释 黑碳(BC)的水平，这是陷阱的示踪剂。BC水平可以从GCC预测出来，而且通常很容易就能得到- 能够提供年度平均日交通量(AADT)数据，以及其他与交通量无关的协变量。 我们的总体目标是显示众包交通数据可以用来估计陷阱，并且这些 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)证明可以从时间变化的BC水平推断 GT数据，以及(2)检查缺血性中风发病风险与交通状况之间的关系 每个事件发生前的几小时和几天。我们假设缺血性中风的发病与 以及在本地位置(即，在小缓冲区内)的每小时交通测量。 我们将在中风发病和人群来源的交通数据之间得出的关联可以建立一个直接的 链接到陷阱的来源，并为疾病预防、干预计划和 治疗。我们提出的使用众包交通数据的建议可以应用于美国任何地方和健康 中风以外的其他结果。

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.