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Creation of a Multimodal Urban Transportation Network through Spatial Data Integration from Authoritative and Crowdsourced Data
ISPRS International Journal of Geo-Information ( IF 3.4 ) Pub Date : 2021-07-09 , DOI: 10.3390/ijgi10070470
Rodrigo Smarzaro , Clodoveu A. Davis , José Alberto Quintanilha

One of the most significant challenges in cities concerns urban mobility. Urban mobility involves the use of different modes of transport, which can be individual or collective, and different organizations can produce their respective datasets that, usually, are used isolated from each other. The lack of an integrated view of the entire multimodal urban transportation network (MUTN) brings difficulties to citizens and urban planning. However, obtaining reliable and up-to-date spatial data is not an easy task. To address this problem, we propose a framework for creating a multimodal urban transportation network by integrating spatial data from heterogeneous sources. The framework standardizes the representation of different datasets through a common conceptual model for spatial data (schema matching), uses topological, geometric, and semantic information to find matches among objects from different datasets (data matching), and consolidated them into a single representation using data fusion techniques in a complementary, redundant and cooperative way. Spatial data integration makes it possible to use reliable data from official sources (possibly outdated and expensive to produce) and crowdsourced data (continuously updated and low cost to use). To evaluate the framework, a MUTN for the Brazilian city of Belo Horizonte was built integrating authoritative and crowdsourced data (OpenStreetMap, Foursquare, Facebook Places, Google Places, and Yelp), and then it was used to compute routes among eighty locations using four transportation possibilities: walk, drive, transit, and drive–walk. The time and distance of each route were compared against their equivalent from Google Maps, and the results point to a great potential for using the framework in urban computing applications that require an integrated view of the entire multimodal urban transportation network.

中文翻译:

通过来自权威和众包数据的空间数据集成创建多模式城市交通网络

城市中最重大的挑战之一是城市交通。城市交通涉及使用不同的交通方式,可以是个人的,也可以是集体的,不同的组织可以生成各自的数据集,这些数据集通常彼此隔离使用。缺乏对整个多式联运城市交通网络 (MUTN) 的综合视图,给市民和城市规划带来了困难。然而,获得可靠且最新的空间数据并非易事。为了解决这个问题,我们提出了一个框架,通过整合来自异构来源的空间数据来创建多模式城市交通网络。该框架通过空间数据的通用概念模型(模式匹配)标准化不同数据集的表示,使用拓扑、几何、和语义信息以在来自不同数据集的对象之间找到匹配(数据匹配),并使用数据融合技术以互补、冗余和协作的方式将它们合并为单个表示。空间数据集成使得使用来自官方来源(可能已经过时且制作成本高)和众包数据(持续更新且使用成本低)的可靠数据成为可能。为了评估该框架,为巴西贝洛奥里藏特市构建了一个 MUTN,整合了权威和众包数据(OpenStreetMap、Foursquare、Facebook Places、Google Places 和 Yelp),然后使用它来计算使用四种交通工具的八十个地点之间的路线可能性:步行、驾车、交通和驾车步行。
更新日期:2021-07-09
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