Fuzzy sets and systems for a motorway microscopic simulation model

Wu, Jianping; Brackstone, Mark; McDonald, M.

doi:10.1016/s0165-0114(99)00038-x

Cited by 96 publications

(43 citation statements)

References 5 publications

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“…One is developed from the viewpoint of traffic engineering and the other is based on statistical physics. From the perspective of traffic engineers (Brackstone and McDonald, 1999), car-following models can be classified as stimulus-response models (Gazis et al, 1961;Newell, 1961), safety distance models (Gipps, 1981), psychophysical models (Wiedemann, 1974), and artificial intelligence models (Kikuchi and Chakroborty, 1992;Wu et al, 2000).…”

Section: Basic Theoretical Backgroundmentioning

confidence: 99%

Methods for the Assessment of Performance Indication Measures at Amakom Intersection and Their Application in Micro Simulation Modeling

Nyantakyi

Owusu²,

Borkloe³

2014

IJTTE

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Signalized intersections are vital nodal points in a transportation network and their efficiency of operation greatly influences the entire network's performance. Traffic simulation models have been widely used in both transportation operations and traffic analyses because simulation is safer, less expensive, and faster than field implementation and testing. This study assessed performance indication measures at Amakom intersection using micro simulation models in Synchro/SimTraffic. Traffic, geometric and signal control data including key parameters with greatest impact on the calibration process were collected on the field. At 5% significant level, the Chi square test and t-test analyses revealed that headway had a strong correlation with saturation flow compared to speed for both field and simulated conditions. It was concluded that changes in phasing plan with geometric improvement would improve upon the intersection's level of service.

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Section: Basic Theoretical Backgroundmentioning

confidence: 99%

Methods for the Assessment of Performance Indication Measures at Amakom Intersection and Their Application in Micro Simulation Modeling

Nyantakyi

Owusu²,

Borkloe³

2014

IJTTE

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“…Some of them use fuzzy logic. In [24], the whole behaviour model is driven by fuzzy logic rules to overcome the normativity of classic modeling. However, there is no decision process per se, since it is replaced by the set of fuzzy rules.…”

Section: Illustrative Examplementioning

confidence: 99%

Fuzzy Rules for Events Perception and Emotions in an Agent Architecture

Jones¹,

Lourdeaux²

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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In complex simulations, multi-agents systems allow to model virtual humans with an explicit cognitive process representation. However, this cognitive process is hard to model and is therefore generally simplified in an application-dependant way. In order to improve the realism of individual and collective behavior of these agents, we propose to integrate the perception of events and the computation of agents emotions in a fuzzy framework. The modeling of the perception and its effect on emotions through fuzzy rules enables the agents to consider properly the virtual environment.We show how different kinds of fuzzy rules can help in the calculus of emotions. Computation of emotions is based on the evaluation of events' occurrence. Once the events are perceived by the agents, our method uses the desirability of these events to compute emotions relevant to crisis situations. We illustrate this model with a traffic simulation example.

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Section: Literature Reviewmentioning

confidence: 99%

“…Their reason is, as mentioned earlier, that those "mechanistic approaches do not usually incorporate the uncertainties of driver perception and decisions" (6) . Examples of fuzzy logic-based driver models and controllers for car-like vehicles can be found in (6) ∼ (9) . Most of these approaches, propose methods to follow a pre-planned path with the minimum possible error, that is the distance to the path's curve and the angle relative to the tangent of the curve on the nearest point.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller

2004

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MemberThis paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver's approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver's self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.

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Fuzzy sets and systems for a motorway microscopic simulation model

Cited by 96 publications

References 5 publications

Methods for the Assessment of Performance Indication Measures at Amakom Intersection and Their Application in Micro Simulation Modeling

Methods for the Assessment of Performance Indication Measures at Amakom Intersection and Their Application in Micro Simulation Modeling

Fuzzy Rules for Events Perception and Emotions in an Agent Architecture

A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller

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