Interactive hybrid simulation of large-scale traffic

Sewall, Jason; Wilkie, David A.; Dubey, Pradeep

doi:10.1145/2024156.2024169

Cited by 48 publications

(52 citation statements)

References 44 publications

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“…In particular, traffic simulation models are classified into macroscopic, mesoscopic, and microscopic [11]. Macroscopic models describe the traffic at a high level of aggregation as flow without considering individual vehicles [12,13].…”

Section: Related Workmentioning

confidence: 99%

“…Different model types are applied to different parts of a network. Thus, Sewall et al [11] perform agent-based simulation of individual vehicles in regions of user's interest while a faster macroscopic model is used in the remainder of the network.…”

Section: Related Workmentioning

confidence: 99%

“…Visualization support to traffic simulation is currently represented only by the works of Sewall et al [11,12], who generate realistic 3D animations of simulated vehicle movements. For the hybrid micro-macro simulation, they designed an interactive tool that automatically and dynamically selects the appropriate simulation method for different parts of the network based on user's needs.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting Spatial Abstraction in Predictive Analytics of Vehicle Traffic

Andrienko

Rinzivillo

2015

IJGI

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By applying visual analytics techniques to vehicle traffic data, we found a way to visualize and study the relationships between the traffic intensity and movement speed on links of a spatially abstracted transportation network. We observed that the traffic intensities and speeds in an abstracted network are interrelated in the same way as they are in a detailed street network at the level of street segments. We developed interactive visual interfaces that support representing these interdependencies by mathematical models. To test the possibility of utilizing them for performing traffic simulations on the basis of abstracted transportation networks, we devised a prototypical simulation algorithm employing these dependency models. The algorithm is embedded in an interactive visual environment for defining traffic scenarios, running simulations, and exploring their results. Our research demonstrates a principal possibility of performing traffic simulations on the basis of spatially abstracted transportation networks using dependency models derived from real traffic data. This

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Spatial Abstraction in Predictive Analytics of Vehicle Traffic

Andrienko

Rinzivillo

2015

IJGI

View full text Add to dashboard Cite

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“…Recently, there has been a number of interesting developments in traffic simulations. For example, Sewall et al proposed a hybrid technique by coupling continuum and agent‐based traffic models , but simulation types can not be alternated quickly. Lu et al presented an accident‐avoidance full velocity difference model to animate traffic flows in rural scenes.…”

Section: Related Workmentioning

confidence: 99%

“…The IDM is regarded as a modern simulation method . However, it sometimes generates unrealistic behavior in cut‐in situations (lane changing manoeuvres) .…”

Section: Preliminariesmentioning

confidence: 99%

A personality model for animating heterogeneous traffic behaviors

Wang

et al. 2014

Computer Animation & Virtual

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How to automatically generate realistic and heterogeneous traffic behaviors has been a much needed yet challenging problem for numerous traffic simulation and urban planning applications. In this paper, we propose a novel approach to model heterogeneous traffic behaviors by adapting a well-established personality trait model (i.e., Eysenck's PEN (psychoticism, extraversion and neuroticism) model) into widely used traffic simulation approaches. First, we collected a large amount of user feedback while users watch a variety of computer-generated traffic simulation video clips. Then, we trained regression models to bridge low-level traffic simulation parameters and high-level perceived traffic behaviors (i.e., adjectives according to the PEN model and the three PEN traits). We also conducted an additional user study to validate the effectiveness and usefulness of our approach, in particular, high correlation coefficients and the Pearson values between users' feedback and our model predictions prove the effectiveness of our approach. Furthermore, our approach can also produce interesting emergent traffic patterns including faster-is-slower effect and sticking-in-a-pin-wherever-there-is-room effect.

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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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Interactive hybrid simulation of large-scale traffic

Cited by 48 publications

References 44 publications

Exploiting Spatial Abstraction in Predictive Analytics of Vehicle Traffic

Exploiting Spatial Abstraction in Predictive Analytics of Vehicle Traffic

A personality model for animating heterogeneous traffic behaviors

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

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