Comparative Evaluation of Microscopic Car-Following Behavior

Panwai, Sakda; Dia, Hussein

doi:10.1109/tits.2005.853705

Cited by 255 publications

(110 citation statements)

References 17 publications

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“…10 A description of the differences between those models is out of scope of this paper. We refer the interested reader to [36]. We only list here the widely used models in traffi c simulations.…”

Section: Car Following Modelsmentioning

confidence: 99%

Mobility models for vehicular ad hoc networks: a survey and taxonomy

Härri

Filali

Bonnet

2009

IEEE Commun. Surv. Tutorials

620

304

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Vehicular Ad-hoc Networks (VANETs) have been recently attracting an increasing attention from both research and industry communities. One of the challenges posed by the study of VANETs is the definition of a generic mobility model providing an accurate, realistic vehicular mobility description at both macroscopic and microscopic levels. Today, most mobility models for vehicular studies only consider a limited macro-mobility, involving restricted vehicles movements, while little or no attention is paid to micro-mobility and its interaction with the macro-mobility counterpart. On the other hand, the research community cannot have access to realistic traffic generator which have not been designed to collaborate with network simulators. In this paper, we first introduce a classification of existing methods for the generation of vehicular mobility models, then we describe the various approaches used by the community for realistic VANET simulations. Finally, we provide an overview and comparison of a large range of mobility models proposed for vehicular ad hoc networks.

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Section: Car Following Modelsmentioning

confidence: 99%

Mobility models for vehicular ad hoc networks: a survey and taxonomy

Härri

Filali

Bonnet

2009

IEEE Commun. Surv. Tutorials

620

304

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“…Car following models: Describe the behavior of each driver in relation to its neighboring vehicles (Panwai and Dia, 2005).…”

Section: Basic Equation Of Traffic Stream Modelmentioning

confidence: 99%

Intelligent Model for Traffic Safety Applications

R.¹

2012

Journal of Computer Science

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Problem statement:This study presents an analysis on road traffic system focused on the use of communications to detect dangerous vehicles on roads and highways and how it could be used to enhance driver safety. Approach: The intelligent traffic safety application model is based on all traffic flow theories developed in the last years, leading to reliable representations of road traffic, which is of major importance in achieving the attenuation of traffic problems. The model includes also the decision making process from the driver in accelerating, decelerating and changing lanes. Results:The individuality of each of these processes appears from the model parameters that are randomly generated from statistical distributions introduced as input parameters. Conclusion: This allows the integration of the individuality factor of the population elements yielding knowledge on various driving modes at wide variety of situations.

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“…The EM showed that the two reactive agent-based models (Fuzzy ARTMAP and Back Propagation networks), performed better than the Gippsbased models. [12].…”

Section: A Simulation Resultsmentioning

confidence: 99%

“…It should be pointed out that Panwai and Dia [12] also used the same time series data set to test the performance of car following models in three well-known traffic simulators: AIMSUN, VISSIM, and PARAMICS. The results are shown in Table 5 below.…”

Section: A Simulation Resultsmentioning

confidence: 99%

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A reactive agent-based neural network car following model

Panwai

Dia

Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005.

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Abstract-This paper presents a car following model which was developed using reactive agent techniques based on a neural network approach for mapping perceptions to actions. The model has a similar formulation to the desired spacing models which do not consider reaction time or attempt to explain the behavioural aspects of car following. A number of error tests were used to compare the performance of the model against a number of established car following models. The results showed that simple back-propagation neural network models outperformed the Gipps and Psychophysical family of car following models. A qualitative drift behaviour analysis also confirmed the findings. For microscopic validation, speed and position of individual vehicles computed from the model were compared to field data. Macroscopic validation involved comparison of the field data and model results for trajectories, average speed, density and volume. Model validation at the microscopic and macroscopic levels showed very close agreement between field data and model results.

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Comparative Evaluation of Microscopic Car-Following Behavior

Cited by 255 publications

References 17 publications

Mobility models for vehicular ad hoc networks: a survey and taxonomy

Mobility models for vehicular ad hoc networks: a survey and taxonomy

Intelligent Model for Traffic Safety Applications

A reactive agent-based neural network car following model

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