class='kwd-title'>Method name: Traffic flow gene'/> Generating traffic flow and speed regional model data using internet GPS vehicle records
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Generating traffic flow and speed regional model data using internet GPS vehicle records

机译：使用互联网GPS车辆记录生成交通流量和速度区域模型数据

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class="kwd-title">Method name: Traffic flow generation based on GPS recordings class="kwd-title">Keywords: GPS, Vehicles, Spatial bias, Internet, Data, Brazil class="head no_bottom_margin" id="abs0010title">AbstractNowadays, many smart-phones and vehicles are equipped with Global Position System (GPS) for tracking and navigation purposes, providing an opportunity to derive highly representative local vehicular flow and estimate vehicular emissions information. Here, we report and discuss methods used to handle large volumes of such activity data, namely 124 million GPS recordings from the web page Maplink.com.br, extract high spatial resolution vehicular flow information for a vast area in South-east Brazil, and correct for bias using traffic counts observations for the same area. The method consists in >filter speed and accelerations, >assign buffers to the road network, >aggregate speed by street, >fill missing number of lanes, generate traffic >flow. Methods presented here were used to inform traffic-related air quality modelling and used as part of local air pollution management activities but are also amenable to any work that would be enhanced by more locally representative or time-resolved inputs for traffic flow, e.g. traffic network management, and demand modelling. class="first-line-outdent" id="lis0005">
  • • 124 million GPS observations from electronic devices were used to generate traffic flow.
  • • Spatial bias was investigated and accounted for using independent local traffic count data.
  • • Traffic count rescaled GPS traffic flow provide a robust description of spatial and quantitative traffic patterns.
    • 机译：<！-fig ft0-> <！-fig @ position =“ anchor” mode =文章f4-> <！-fig mode =“ anchred” f5-> <！-fig / graphic | fig / alternatives / graphic mode =“ anchored” m1-> class =“ kwd-title”>方法名称：基于GPS记录的交通流量生成 class =“ kwd-title”>关键字：< / strong> GPS，车辆，空间偏差，互联网，数据，巴西 class =“ head no_bottom_margin” id =“ abs0010title”>摘要如今，许多智能手机和车辆都配备了全球定位系统（GPS） ），以进行跟踪和导航，从而提供了一个机会，可以得出具有高度代表性的本地车辆流量并估算车辆排放信息。在这里，我们报告并讨论用于处理大量此类活动数据的方法，即从Maplink.com.br网页上进行的1.24亿次GPS记录，为巴西东南部广大地区提取高分辨率的车辆流量信息，以及使用相同区域的流量计数观察值校正偏差。该方法包括>过滤器速度和加速度，>分配到道路网络的缓冲区，>按街道汇总速度，>填充缺少车道数，产生流量>流量。此处介绍的方法用于告知与交通有关的空气质量模型，并用作本地空气污染管理活动的一部分，但也适用于将由更具本地代表性或时间解析的交通流量输入来增强的工作，例如交通流量。交通网络管理和需求建模。 class =“ first-line-outdent” id =“ lis0005”> <！-list-behavior =简单的前缀-word = mark-type = none最大标签大小= 9 ->
  • •来自电子设备的1.24亿个GPS观测值用于生成交通流。
  • •对空间偏差进行了调查，并使用独立的本地交通进行了说明计数数据。
  • •流量计数重定比例的GPS流量提供了对空间和定量流量模式的可靠描述。

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