Two-Lane Traffic Flow Simulation Model via Cellular Automaton

Rawat, K.; Katiyar, V. K.; Gupta, Pratibha

doi:10.1155/2012/130398

Cited by 16 publications

(8 citation statements)

References 16 publications

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“…Although CAs are very simple in their nature, they are able to simulate all kinds of behaviors, such as traffic flows (Rawat et al [18] , Guzman et al [19] ) and segmentation of images in regions (Diosan et al [20] ). Similarly, IoAs can be used to simulate programs working on arrays of objects.…”

Section: Background and Related Researchmentioning

confidence: 99%

Neural networks learn to detect and emulate sorting algorithms from images of their execution traces

Perticas

Indurkhya

2020

Information and Software Technology

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Context: Recent advancements in the applicability of neural networks across a variety of fields, such as computer vision, natural language processing and others, have re-sparked an interest in program induction methods. (Kitzelman [1] , Gulwani et al. [2] or Kant [3].) Problem: When performing a program induction task, it is not feasible to search across all possible programs that map an input to an output because the number of possible combinations or sequences of instructions is too high: at least an exponential growth based on the generated program length. Moreover, there does not exist a general framework to formulate such program induction tasks and current computational limitations do not allow a very wide range of machine learning applications in the field of computer programs generation.Objective: In this study, we analyze the effectiveness of execution traces as learning representations for neural network models in a program induction set-up. Our goal is to generate visualizations of program execution dynamics, specifically of sorting algorithms, and to apply machine learning techniques on them to capture their semantics and emulate their behavior using neural networks. Method:We begin by classifying images of execution traces for algorithms working on a finite array of numbers, such as various sorting and data structures algorithms. Next we experiment with detecting sub-program patterns inside the trace sequence of a larger program. The last step is to predict future steps in the execution of various sorting algorithms. More specifically, we try to emulate their behavior by observing their execution traces. We also discuss generalizations to other classes of programs, such as 1-D cellular automata. Results:Our experiments show that neural networks are capable of modeling the mechanisms underlying simple algorithms if enough execution traces are provided as data. We compare the performance of our program induction model with other similar experimental results from Graves et al. [4] and Vinyals et al. [5]. We were also able to demonstrate that sorting algorithms can be treated both as images displaying spatial patterns, as well as sequential instructions in a domain specific language, such as swapping two elements. We tested our approach on three types of increasingly harder tasks: detection, recognition and emulation. Conclusions:We demonstrate that simple algorithms can be modelled using neural networks and provide a method for representing specific classes of programs as either images or sequences of instructions in a domain-specific language, such that a neural network can learn their behavior. We consider the complexity of various set-ups to arrive at some improvements based on the data representation type. The insights from our experiments can be applied for designing better models of program induction.

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Section: Background and Related Researchmentioning

confidence: 99%

Neural networks learn to detect and emulate sorting algorithms from images of their execution traces

Perticas

Indurkhya

2020

Information and Software Technology

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“…Más tarde, en el trabajo Rawat, Katiyar & Gupta (2012), se hace una corta pero bien presentada exposición de los modelos con dos carriles inducidos por los diferentes modelos de un carril. En otros estudios, se han ido cambiando las reglas de los modelos a medida que se van obteniendo datos reales del comportamiento del tráfico.…”

Section: Modelos Con Dos Y Múltiples Carrilesunclassified

Una revisión de modelos de tráfico automotor usando autómatas celulares Luis Bladismir Ruiz

Tineo

Ruiz

2019

NOVASINERGIA

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Los Autómatas Celulares son un modelo matemático utilizado para estudiar y representar sistemas dinámicos, adecuados para modelar sistemas naturales como la evolución de virus y bacterias, así como el flujo de gases, líquidos y del tráfico vehicular y peatonal. El primer modelo probabilístico no trivial fue presentado por Nagel y Schreckenberg (1992) y cada vez son más los investigadores que se suman a su aplicación en la vida diaria. En este artículo se plantea una revisión del comportamiento del flujo vehicular (FV) para vías de uno y varios carriles, considerando las modificaciones de los modelos ya existentes y los cambios en la forma de conducir con la finalidad de conocer el enfoque de los diversos investigadores sobre el estudio de tráfico vehicular. El estudio revela que el FV se adapta a las condiciones del tráfico del momento y que el modelo, por su propiedad, permite modificar reglas de interacción y considerar la infraestructura de la vía para el modelo de un carril mientras que los modelos con carriles múltiples son más complejos.

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“…In the current researches, traffic speed prediction methods are mainly divided into two categories: model-driven methods [5]- [8] that rely on theoretical mathematical models and data-driven methods that use statistical methods [9]- [11] and artificial intelligence algorithms [12]- [14]. Model-driven approaches use prior knowledge and physical properties to model traffic flow and predict road traffic speeds through traffic simulation.…”

Section: Introductionmentioning

confidence: 99%

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction

Fu¹,

Liang²,

Hou³

et al. 2022

Preprint

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Traffic speed prediction is the key to many valuable applications, and it is also a challenging task because of its various influencing factors. Recent work attempts to obtain more information through various hybrid models, thereby improving the prediction accuracy. However, the spatial information acquisition schemes of these methods have two-level differentiation problems. Either the modeling is simple but contains little spatial information, or the modeling is complete but lacks flexibility. In order to introduce more spatial information on the basis of ensuring flexibility, this paper proposes IRNet (Transferable Intersection Reconstruction Network). First, this paper reconstructs the intersection into a virtual intersection with the same structure, which simplifies the topology of the road network. Then, the spatial information is subdivided into intersection information and sequence information of traffic flow direction, and spatiotemporal features are obtained through various models. Third, a self-attention mechanism is used to fuse spatiotemporal features for prediction. In the comparison experiment with the baseline, not only the prediction effect, but also the transfer performance has obvious advantages.

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Two-Lane Traffic Flow Simulation Model via Cellular Automaton

Cited by 16 publications

References 16 publications

Neural networks learn to detect and emulate sorting algorithms from images of their execution traces

Neural networks learn to detect and emulate sorting algorithms from images of their execution traces

Una revisión de modelos de tráfico automotor usando autómatas celulares Luis Bladismir Ruiz

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction

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