Open-sourced real-time visualization platform for traffic simulation

Jaeeun Jung, Taeho Oh, Inhi Kim, Shinhyoung Park

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


To solve the city's traffic congestion problem, micro-traffic simulation is widely used to analyze the current situation and implement a plan to solve the problem. With the nature of the simulation, it is possible to design multiple scenarios and predict future traffic conditions. The state of the network is usually represented visually through 3D graphics and numbers such as speed, delay time, etc. However, analysis results are generally not intuitive, making it difficult for non-experts to judge traffic indicators. This study presents the possibility of utilizing an open-sourced real-time web-based platform linking simulation results to dashboards. Here, the micro traffic simulation, VISSIM is used. Traffic flow at intersections and location information of individual vehicles are stored in the cloud from VISSIM at analysis intervals. In real-time, the saved results are loaded into Elasticsearch, an open-source visualization tool. Data are visualized according to temporal and spatial characteristics and constitute the dashboard layout. Users intuitively analyze and predict traffic conditions through this visualization platform. In addition, traffic conditions can be easily communicated to general users who use web-based information.

Original languageEnglish
Pages (from-to)243-250
Number of pages8
JournalProcedia Computer Science
StatePublished - 2023
Event14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023 - Leuven, Belgium
Duration: 15 Mar 202317 Mar 2023


  • Real-time Data
  • Smart City
  • Traffic Simulation
  • Web Dashboard


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