This paper describes a new approach for understanding urban mobility called geospatial and temporal (GeoST) mapping, which translates telematics (location) data into travel characteristics. The approach provides the speed-acceleration profile of transport flow at high spatial and temporal resolution. The speed-acceleration profiles can be converted to vehicle-specific power (VSP), which can be used to estimate vehicle emissions. The underlying data used in the model is retrieved from a large telematics dataset, which was collected from GPS-connected vehicles during their journeys over the UK's West Midlands region road network for the years 2016 and 2018. Single journey telematics data were geospatially aggregated and then distributed over GeoST-segments. In total, approximately 354,000 GeoST-segments, covering over 17,700 km of roads over 35 timeslots are parameterized. GeoST mapping of the average vehicle speed (traffic flow), and VSP over different road types are analysed. The role of road slope upon VSP is estimated for every GeoST-segment through knowledge of the elevation of the start and end points of the segments. Previously, road slope and its effect upon VSP have been typically ignored in transport and urban planning studies. A series of case studies are presented that highlight the power of the new approach over differing spatial and temporal scales. For example, results show that the total vehicle fleet moved faster by an average of 3% in 2016 compared to 2018. The studied roads at weekends are shown to be less safe, compared to weekdays, because of lower adherence to speed limits. By including road slope in VSP calculations, the annually averaged VSP results differ by +12.6%, +14.3%, and + 12.7% for motorways, primary roads, and secondary roads, respectively, when compared to calculations that ignore road slope.

中文翻译：

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用于城市交通地理空间和时间映射的远程信息处理数据：对出行特征和车辆特定功率的新见解

本文描述了一种理解城市交通的新方法，称为地理空间和时间 (GeoST) 测绘，它将远程信息处理（位置）数据转化为出行特征。该方法以高空间和时间分辨率提供运输流的速度-加速度曲线。速度-加速度曲线可以转换为车辆特定功率（VSP），可用于估算车辆排放。该模型中使用的基础数据取自大型远程信息处理数据集，该数据集是从 2016 年和 2018 年在英国西米德兰兹地区道路网络上行驶的 GPS 连接车辆中收集的。单程远程信息处理数据经过地理空间聚合，然后分布在 GeoST 段上。总共约 354,000 个 GeoST 路段被参数化，覆盖 35 个时隙、超过 17,700 公里的道路。分析了平均车速（交通流量）的 GeoST 映射以及不同道路类型的 VSP。通过了解路段起点和终点的高程，估算每个 GeoST 路段的道路坡度对 VSP 的作用。此前，在交通和城市规划研究中，道路坡度及其对 VSP 的影响通常被忽略。提出了一系列案例研究，强调了新方法在不同空间和时间尺度上的力量。例如，结果显示，与 2018 年相比，2016 年车辆总数平均增长了 3%。与工作日相比，所研究的周末道路安全性较低，因为对速度限制的遵守程度较低。通过将道路坡度纳入 VSP 计算，与忽略道路坡度的计算相比，高速公路、主要道路和次要道路的年平均 VSP 结果分别相差 +12.6%、+14.3% 和 +12.7%。