A Data-Integration Analysis on Road Emissions and Traffic Patterns
A Data-Integration Analysis on Road Emissions and Traffic Patterns
复制标题
道路排放和交通模式的数据集成分析
DOI：
10.1007/978-3-030-63393-6_34
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
and Baroud, H.
中科院分区：
文献类型：
--
作者：
Qu, A.;Wang, Y.;Hu, Y.;Wang, Y.;and Baroud, H.
了解人类活动和城市交通模式是解决许多城市问题的关键，如拥堵和排放。由于有大量不同保真度的数据集，主要挑战之一是数据源的稀疏性和异构性。整合这些数据源对于更好地为系统设计和社区一级战略提供信息至关重要。在本文中，我们整合了各种数据源，包括土地利用、车辆排放和建筑足迹，以全面可视化和分析芝加哥环路地区的交通模式。我们首先实现并比较了三种不同的最近邻搜索算法来确定建筑物占用分配，然后针对土地利用、公共交通和人口等因素对车辆排放进行时空相关性分析。最后，从数据分析的角度讨论了交通特征，如交通拥堵的形成和高峰时间等。
Understanding human activities and urban mobility patterns is key to solving many urban issues such as congestion and emissions. With the abundant data sets available at different levels of fidelity, one of the main challenges is the sparsity and heterogeneity of data sources. The integration of such data sources is essential to better inform system design and community-level strategies. In this paper, we incorporate a variety of data sources including land use, vehicle emissions and building footprint to comprehensively visualize and analyze traffic patterns in the Chicago Loop area. We first implement and compare three different nearest-neighbor-search algorithms to determine building occupancy assignment, and then perform a spatial-temporal correlation analysis of vehicle emissions focusing on factors such as land use, public transit and demographic. Lastly, we discuss the traffic characteristics from data analysis, such as traffic congestion formation and rush hours etc.
影响因子：
6.7
作者：
Huang, Wei;Xu, Shishuo;Zipf, Alexander
通讯作者：
Zipf, Alexander