In this study, we explored to combine traffic maps and smartphone trajectories to model traffic air pollution, exposure and health impact. The approach was step-by-step modeling through the causal chain: engine emissi...In this study, we explored to combine traffic maps and smartphone trajectories to model traffic air pollution, exposure and health impact. The approach was step-by-step modeling through the causal chain: engine emission, traffic density versus traffic velocity, traffic pollution concentration, exposure along individual trajectories, and health risk. A generic street with 100 km/h speed limit was used as an example to test the model. A single fixed-time trajectory had maximum exposure at velocity of 45 km/h at maximum pollution concentration. The street population had maximum exposure shifted to a velocity of 15 km/h due to the congestion density of vehicles. The shift is a universal effect of exposure. In this approach, nearly every modeling step of traffic pollution depended on traffic velocity. A traffic map is a super-efficient pre-processor for calculating real-time traffic pollution exposure at global scale using big data analytics.展开更多
文摘In this study, we explored to combine traffic maps and smartphone trajectories to model traffic air pollution, exposure and health impact. The approach was step-by-step modeling through the causal chain: engine emission, traffic density versus traffic velocity, traffic pollution concentration, exposure along individual trajectories, and health risk. A generic street with 100 km/h speed limit was used as an example to test the model. A single fixed-time trajectory had maximum exposure at velocity of 45 km/h at maximum pollution concentration. The street population had maximum exposure shifted to a velocity of 15 km/h due to the congestion density of vehicles. The shift is a universal effect of exposure. In this approach, nearly every modeling step of traffic pollution depended on traffic velocity. A traffic map is a super-efficient pre-processor for calculating real-time traffic pollution exposure at global scale using big data analytics.
文摘大数据时代背景下,对车辆的GPS(global positioning system,全球定位系统)轨迹数据进行研究分析,能够帮助交通管理者充分了解交通态势及发展趋势,为精细化管理提供数据支撑。为通过货运车辆运行情况探索甘肃省货运规律,以甘肃省货运车辆GPS数据为例,充分关联区域内的相关产业分布,分析货运走行规律,探索区域货运态势,通过等时差抽取估算法得到产业分布情况、货运OD(Origin and destination,起讫点)情况、货运通道偏好情况、货运车辆停留点分布情况等4项交通分析结果。采取等时差抽取估算法省去了对所有车辆逐一进行轨迹重构的工作量,可直接估算出道路的单公里货运车辆流量值,并且最终结果显示误差率在5%以内,可为同类研究提供借鉴。
