PERFORMANCE MATTERS ON IDENTIFICATION OF ORIGIN-DESTINATION MATRIX ON BIG GEOSPATIAL DATA
One of the common problems at the intersection of geographical information science and transportation science is the estimation of origin-destination (OD) matrices. The emergence of sensor technologies offers unprecedented opportunities in this regard since massive amounts of traffic data can be collected in an easy way. Researchers and practitioners need to choose a suitable DataBase Management System (DBMS) among alternatives, such that storing and analysing traffic data to estimate the OD matrix is feasible. The aim of this paper is to compare the performance of two such notable DBMSs, PostgreSQL and MongoDB, in the context of OD matrix estimation. The experiments are carried out on New York City’s openly available taxi data on two different polygon sets: taxi zones and census blocks. These polygon layers consist of 263 and 38794 features respectively. The results suggest that Postgres outperforms MongoDB by generating the OD matrix instantly. The run time of MongoDB varies depending on the analysed time interval and follows a trip demand curve. As there are more trips involved in the generation of the OD matrix, so does the execution time increases in MongoDB. On the other hand, the query results are the same. Finally, the origin points of the taxi trips are visualised in QGIS using the ‘TimeManager’ plugin, and results are presented through a web-interface.