Dissecting GeoSparkSim: a scalable microscopic road network traffic simulator in Apache Spark
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Dissecting GeoSparkSim: a scalable microscopic road network traffic simulator in Apache Spark Jia Yu1 · Zishan Fu1 · Mohamed Sarwat1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Researchers and practitioners have widely studied road network traffic data in different areas such as urban planning, traffic prediction and spatial-temporal databases. For instance, researchers use such data to evaluate the impact of road network changes. Unfortunately, collecting large-scale high-quality urban traffic data requires tremendous efforts because participating vehicles must install global positioning system(GPS) receivers and administrators must continuously monitor these devices. There have been some urban traffic simulators trying to generate such data with different features. However, they suffer from two critical issues (1) Scalability: most of them only offer single-machine solution which is not adequate to produce largescale data. Some simulators can generate traffic in parallel but do not well balance the load among machines in a cluster. (2) Granularity: many simulators do not consider microscopic traffic situations including traffic lights, lane changing, car following. This paper proposed GeoSparkSim, a scalable traffic simulator which extends Apache Spark to generate large-scale road network traffic datasets with microscopic traffic simulation. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark, to deliver a holistic approach that allows data scientists to simulate, analyze and visualize large-scale urban traffic data. To implement microscopic traffic models, GeoSparkSim employs a simulationaware vehicle partitioning method to partition vehicles among different machines such that each machine has a balanced workload. The experimental analysis shows that GeoSparkSim can simulate the movements of 300 thousand vehicles over a very large road network (250 thousand road junctions and 300 thousand road segments) and outperform the existing competitors. Keywords Spatio-temporal data · Apache Spark · Traffic model · Microscopic traffic simulation
* Jia Yu [email protected] Extended author information available on the last page of the article
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Distributed and Parallel Databases
1 Introduction Road network traffic data contains the trajectories of a set of vehicles moving over a road network. Each trajectory consists of a number of GPS points which capture the vehicle locations at every audited time step. Such traffic data has been widely studied by researchers and practitioners in different disciplines that include urban planning, traffic prediction and spatial-temporal databases. For instance, researchers use traffic data to evaluate the impact of road network changes. Unfortunately, although there are millions of vehicles driving in big cities, collecting large-scale high-quality traffic data requires tremendous efforts since participating vehicles must install GPS receivers and administrators must continuousl
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