Continuum Traffic Simulation

Sewall, Jason; Wilkie, David A.; Merrell, Paul; Lin, Ming C.

doi:10.1111/j.1467-8659.2009.01613.x

Cited by 81 publications

(76 citation statements)

References 26 publications

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“…Thu s, the au thors of system MATSim ad m it that find ing sim p le p red ictive ru les for the com p u tational p erform ance of their system is d ifficu lt and rep ort only com p u tational tim es for sp ecific scenarios [11]. We cou ld find ou t that the estim ated com p lexity of agent-based sim u lation, as in SUMO [12], is linear w ith regard to the nu m ber of agents [56], w hich is the sam e as in ou r algorithm . H ow ever, for the sake of faster com p u tations, the sim u lators u se sim p lified netw ork m od els.…”

Section: Simulation Algorithm Complexitymentioning

confidence: 99%

“…Works of Sewall et al [56][57] focus on the 'forecast' task in movement (specifically, vehicle traffic) analysis. They developed traffic simulation algorithms allowing generation of realistic 3D animations of simulated vehicle movements.…”

Section: Visual Analytics Methods For Analysis Of Movement Datamentioning

confidence: 99%

“…While the task 'forecast' alone has been addressed by Sewall et al [56][57], our work is the first to propose visual analytics support to all three tasks together in a common framework.…”

Section: How This Work Extends the State Of The Artmentioning

confidence: 99%

“…Macroscopic models (a.k.a. continuous or continuum models [56]) describe the traffic at a high level of aggregation as flow without considering individual vehicles. This is done using sets of differential equations, which are often defined based on analogies to physical phenomena, such as gas or fluid dynamics [44].…”

Section: Transportation Researchmentioning

confidence: 99%

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Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Andrienko

Rinzivillo

2016

Machine Learning and Knowledge Discovery in Databases

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. Permanent repository linkWe propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios.

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Section: Simulation Algorithm Complexitymentioning

confidence: 99%

Section: Visual Analytics Methods For Analysis Of Movement Datamentioning

confidence: 99%

“…While the task 'forecast' alone has been addressed by Sewall et al [56][57], our work is the first to propose visual analytics support to all three tasks together in a common framework.…”

Section: How This Work Extends the State Of The Artmentioning

confidence: 99%

Section: Transportation Researchmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Andrienko

Rinzivillo

2016

Machine Learning and Knowledge Discovery in Databases

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“…Yu et al [2009] used a hydrographic network model to simulate rivers in real time. Sewall et al [2010] used a graph model to simulate traffic flow in a city. Hydraulic models are also widely used in biomedical modeling, especially blood flow simulation [Formaggia and Veneziani 2003;Nobile 2009;Almeder 1999].…”

Section: Related Workmentioning

confidence: 99%

Sketch-based Dynamic Illustration of Fluid Systems

Zhu¹,

Iwata

Haraguchi

et al. 2011

Proceedings of the 2011 SIGGRAPH Asia Conference

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This paper presents a lightweight sketching system that enables interactive illustration of complex fluid systems. Users can sketch on a 2.5-dimensional (2.5D) canvas to design the shapes and connections of a fluid circuit. These input sketches are automatically analyzed and abstracted into a hydraulic graph, and a new hybrid fluid model is used in the background to enhance the illustrations. The system provides rich simple operations for users to edit the fluid system incrementally, and the new internal flow patterns can be simulated in real time. Our system is used to illustrate various fluid systems in medicine, biology, and engineering. We asked professional medical doctors to try our system and obtained positive feedback from them.

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AA‐FVDM: An accident‐avoidance full velocity difference model for animating realistic street‐level traffic in rural scenes

Chen

et al. 2013

Computer Animation & Virtual

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Most of existing traffic simulation efforts focus on urban regions with a coarse two‐dimensional representation; relatively few studies have been conducted to simulate realistic three‐dimensional traffic flows on a large, complex road web in rural scenes. In this paper, we present a novel agent‐based approach called accident‐avoidance full velocity difference model (abbreviated as AA‐FVDM) to simulate realistic street‐level rural traffics, on top of the existing FVDM. The main distinction between FVDM and AA‐FVDM is that FVDM cannot handle a critical real‐world traffic problem while AA‐FVDM settles this problem and retains the essence of FVDM. We also design a novel scheme to animate the lane‐changing maneuvering process (in particular, the execution course). Through numerous simulations, we demonstrate that besides addressing a previously unaddressed real‐world traffic problem, our AA‐FVDM method efficiently (in real time) simulates large‐scale traffic flows (tens of thousands of vehicles) with realistic, smooth effects. Furthermore, we validate our method using real‐world traffic data, and the validation results show that our method measurably outperforms state‐of‐the‐art traffic simulation methods.Copyright © 2013 John Wiley & Sons, Ltd.

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Continuum Traffic Simulation

Cited by 81 publications

References 26 publications

Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Sketch-based Dynamic Illustration of Fluid Systems

AA‐FVDM: An accident‐avoidance full velocity difference model for animating realistic street‐level traffic in rural scenes

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