A Parallel-based Map Matching Approach over Urban Place Records

Abstract

In the Smart Cities scenario, to avoid the conflicting geospatial records between official and non-official sources, it is necessary to detect the inconsistencies regarding the geospatial data provided by them. To this end, the map matching task, i.e., the task of identifying correspondent features between two geospatial data sources, should be applied. For spatial Big Data, the map matching task is confronted with challenges related to volume and veracity of the data. In this sense, we propose a Spark-based map matching approach, called MATCH-UPS. To evaluate, real-world data sources of New York (USA) and Curitiba (Brazil) were applied. The results showed that MATCH-UPS improved the precision by 26% and reduced the execution time by one third.

A new approach for matching road lines using efficiency rates of similarity measures

“…Lei and Lei (2019) also developed a flow-based optimization model that seeks to minimize the total discrepancy between two datasets. Moreover, Araújo et al (2019) proposed a Spark-based approach using the names of the places (semantic) and context information (e.g., neighbouring streets) to compare the corresponding objects in realworld data sources of New York and Curitiba.…”