GeoSparkSim: A Scalable Microscopic Road Network Traffic Simulator Based on Apache Spark

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-...

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Other Authors: Fu, Zishan (Author)
Format: Dissertation
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/2286/R.I.53917
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spelling ndltd-asu.edu-item-539172019-05-16T03:02:03Z GeoSparkSim: A Scalable Microscopic Road Network Traffic Simulator Based on Apache Spark 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 large-scale 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 simulation-aware 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 200 thousand cars over an extensive road network (250 thousand road junctions and 300 thousand road segments). Dissertation/Thesis Fu, Zishan (Author) Sarwat, Mohamed (Advisor) Pedrielli, Giulia (Committee member) Sefair, Jorge (Committee member) Arizona State University (Publisher) Computer science Computer engineering Urban planning Apache Spark Microscopic Simulator Road Network Scalability Traffic Workload Balancing eng 107 pages Masters Thesis Computer Engineering 2019 Masters Thesis http://hdl.handle.net/2286/R.I.53917 http://rightsstatements.org/vocab/InC/1.0/ 2019
collection NDLTD
language English
format Dissertation
sources NDLTD
topic Computer science
Computer engineering
Urban planning
Apache Spark
Microscopic Simulator
Road Network
Scalability
Traffic
Workload Balancing
spellingShingle Computer science
Computer engineering
Urban planning
Apache Spark
Microscopic Simulator
Road Network
Scalability
Traffic
Workload Balancing
GeoSparkSim: A Scalable Microscopic Road Network Traffic Simulator Based on Apache Spark
description 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 large-scale 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 simulation-aware 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 200 thousand cars over an extensive road network (250 thousand road junctions and 300 thousand road segments). === Dissertation/Thesis === Masters Thesis Computer Engineering 2019
author2 Fu, Zishan (Author)
author_facet Fu, Zishan (Author)
title GeoSparkSim: A Scalable Microscopic Road Network Traffic Simulator Based on Apache Spark
title_short GeoSparkSim: A Scalable Microscopic Road Network Traffic Simulator Based on Apache Spark
title_full GeoSparkSim: A Scalable Microscopic Road Network Traffic Simulator Based on Apache Spark
title_fullStr GeoSparkSim: A Scalable Microscopic Road Network Traffic Simulator Based on Apache Spark
title_full_unstemmed GeoSparkSim: A Scalable Microscopic Road Network Traffic Simulator Based on Apache Spark
title_sort geosparksim: a scalable microscopic road network traffic simulator based on apache spark
publishDate 2019
url http://hdl.handle.net/2286/R.I.53917
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