TY - GEN
T1 - Demonstrating geosparksim
T2 - 16th International Symposium on Spatial and Temporal Databases, SSTD 2019
AU - Fu, Zishan
AU - Yu, Jia
AU - Sarwat, Mohamed
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/8/19
Y1 - 2019/8/19
N2 - Road network traffic data has been widely studied by researchers and practitioners in different areas such as urban planning, traffic prediction and spatial-temporal databases. The existing urban traffic simulators suffer from two critical issues (1) scalability: most of them only offer single-machine solutions which are 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, and car following. In the paper, we propose 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. A full-fledged prototype of GeoSparkSim is implemented in Apache Spark. In this demonstration, we will show the attendees how to issue GeoSparkSim simulation tasks via the user interface, visualize simulated vehicle movements, and monitor the backend Spark cluster status.
AB - Road network traffic data has been widely studied by researchers and practitioners in different areas such as urban planning, traffic prediction and spatial-temporal databases. The existing urban traffic simulators suffer from two critical issues (1) scalability: most of them only offer single-machine solutions which are 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, and car following. In the paper, we propose 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. A full-fledged prototype of GeoSparkSim is implemented in Apache Spark. In this demonstration, we will show the attendees how to issue GeoSparkSim simulation tasks via the user interface, visualize simulated vehicle movements, and monitor the backend Spark cluster status.
KW - Distributed computation
KW - Road network
KW - Traffic simulation
UR - http://www.scopus.com/inward/record.url?scp=85071642397&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071642397&partnerID=8YFLogxK
U2 - 10.1145/3340964.3340984
DO - 10.1145/3340964.3340984
M3 - Conference contribution
AN - SCOPUS:85071642397
T3 - ACM International Conference Proceeding Series
SP - 186
EP - 189
BT - Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019
PB - Association for Computing Machinery
Y2 - 19 August 2019 through 21 August 2019
ER -