Estimating Traffic Disruption Patterns with Volunteered Geographic Information
Estimating Traffic Disruption Patterns with Volunteered Geographic Information
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DOI：
10.1038/s41598-020-57882-2
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发表时间：
2020-01-27
影响因子：
4.6
通讯作者：
Hale, Scott A.
中科院分区：
文献类型：
--
作者：
Camargo, Chico Q.;Bright, Jonathan;Hale, Scott A.
准确理解和预测交通是决策者面临的一个关键问题。道路网络日益拥堵，而交通数据的获取往往成本高昂，使得明智的决策变得更加困难。本文探讨了在何种程度上可以估计交通中断使用的功能，从自愿的地理信息网站OpenStreetMap（OSM）。我们使用OSM功能作为预测的线性回归的交通中断和交通量的计数在6,500点的道路网络内的112个地区的牛津郡，英国。我们发现，超过一半的交通量和中断的变化可以单独用OSM功能来解释，并使用交叉验证和递归特征消除来评估不同土地利用类别的预测能力和重要性。最后，我们表明，使用OSM的粒度点的兴趣数据允许更好的预测比更广泛的类别通常用于交通和土地利用的研究。
Accurate understanding and forecasting of traffic is a key contemporary problem for policymakers. Road networks are increasingly congested, yet traffic data is often expensive to obtain, making informed policy-making harder. This paper explores the extent to which traffic disruption can be estimated using features from the volunteered geographic information site OpenStreetMap (OSM). We use OSM features as predictors for linear regressions of counts of traffic disruptions and traffic volume at 6,500 points in the road network within 112 regions of Oxfordshire, UK. We show that more than half the variation in traffic volume and disruptions can be explained with OSM features alone, and use cross-validation and recursive feature elimination to evaluate the predictive power and importance of different land use categories. Finally, we show that using OSM's granular point of interest data allows for better predictions than the broader categories typically used in studies of transportation and land use.