SALT-Viz: Real-Time Visualization for Large-Scale Traffic Simulation

This paper introduces the framework of an innovative incident management platform with the main objective of providing decision-support and situation awareness for transport management purposes on a real-time basis. The logic of the platform is to detect and then classify incidents into two types: recurrent and non-recurrent, based on their frequency and characteristics. Under this logic, recurrent incidents trigger the data-driven machine learning module which can predict and analyze the incident impact, in order to facilitate informed decisions for transport management operators. Non-recurrent incidents activate the simulation module, which then evaluates quantitatively the performance of candidate response plans in parallel. The simulation output is used for choosing the most appropriate response plan for incident management. The current platform uses a data processing module to integrate complementary data sets, for the purpose of improving modeling outputs. Two real-world case studies are presented: 1) for recurrent incident management using a data-driven model, and 2) for non-recurrent incident management using traffic simulation with parallel scenario evaluation. The case studies demonstrate the viability of the proposed incident management framework, which provides an integrated approach for real-time incident decision-support on large-scale networks.

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Interactive Real-Time Visualization for Large-Scale Spatial Datasets Based on OptIputer

2010 International Conference on Computational Intelligence and Software Engineering ◽

10.1109/cise.2010.5677197 ◽

2010 ◽

Author(s):

Rui-peng Tong ◽

Di Zhao

Keyword(s):

Real Time ◽

Large Scale ◽

Spatial Datasets ◽

Real Time Visualization

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Research on Real-Time Visualization of Large-scale 3D Terrain

Procedia Engineering ◽

10.1016/j.proeng.2012.01.198 ◽

2012 ◽

Vol 29 ◽

pp. 1702-1706 ◽

Cited By ~ 4

Author(s):

Hou Han-dan ◽

Zhang Jian-fei

Keyword(s):

Real Time ◽

Large Scale ◽

3D Terrain ◽

Real Time Visualization

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Web‐based real‐time visualization of large‐scale weather radar data using 3D tiles

Transactions in GIS ◽

10.1111/tgis.12638 ◽

2020 ◽

Author(s):

Mingyue Lu ◽

Xinhao Wang ◽

Xintao Liu ◽

Min Chen ◽

Shuoben Bi ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Weather Radar ◽

Radar Data ◽

Web Based ◽

Real Time Visualization

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PARALLEL REAL-TIME IMPLEMENTATION OF LARGE-SCALE, ROUTE-PLAN-DRIVEN TRAFFIC SIMULATION

International Journal of Modern Physics C ◽

10.1142/s0129183196000156 ◽

1996 ◽

Vol 07 (02) ◽

pp. 133-153 ◽

Cited By ~ 32

Author(s):

M. RICKERT ◽

P. WAGNER

Keyword(s):

Simulation Model ◽

Real Time ◽

Large Scale ◽

Traffic Simulation ◽

Vehicular Traffic ◽

Workstation Cluster ◽

Ongoing Effort ◽

Realistic Problem

This work is part of our ongoing effort to design and implement a traffic simulation application capable of handling realistic problem sizes in multiple real-time. Our traffic simulation model includes multi-lane vehicular traffic and individual route-plans. On a 16-CPU SGI Power Challenger and a 12-CPU SUN workstation-cluster we have reached real-time for the whole German Autobahn network.

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HiIndex: An Efficient Spatial Index for Rapid Visualization of Large-Scale Geographic Vector Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10100647 ◽

2021 ◽

Vol 10 (10) ◽

pp. 647

Author(s):

Zebang Liu ◽

Luo Chen ◽

Anran Yang ◽

Mengyu Ma ◽

Jingzhi Cao

Keyword(s):

Real Time ◽

Large Scale ◽

Information Science ◽

Treatment Time ◽

Spatial Index ◽

Vector Data ◽

Recursion Method ◽

Tree Generation ◽

Real Time Visualization ◽

Pre Treatment

In the big data era, rapid visualization of large-scale vector data has become a serious challenge in Geographic Information Science (GIS). To fill the gap, we propose HiIndex, a spatial index that enables real-time and interactive visualization of large-scale vector data. HiIndex improves the state of the art with its low memory requirements, fast construction speed, and high visualization efficiency. In HiIndex, we present a tile-quadtree structure (TQ-tree) which divides the global geographic range based on the quadtree recursion method, and each node in the TQ-tree represents a specific and regular spatial range. In this paper, we propose a quick TQ-tree generation algorithm and an efficient visualization algorithm. Experiments show that the HiIndex is simple in structure, fast in construction, and less in memory occupation, and our approach can support interactive and real-time visualization of billion scale vector data with negligible pre-treatment time.

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