Traffic Management using Logistic Regression with Fuzzy Logic

Tomar, Anurag Singh; Mridula, Singh; Arya, K. V.

doi:10.1016/j.procs.2018.05.159

Cited by 20 publications

(4 citation statements)

References 15 publications

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“…Many researchers were interested in combining LR with fuzzy logic and claimed that this approach led to a more effective predictive system. For example, Tomar et al [78] used logistic regression and fuzzy inference system (FIS) to manage traffic on different routes. Also, logistic regression analysis and fuzzy logic were integrated to identify gully erosion susceptibility [79].…”

Section: Logistic Regressionmentioning

confidence: 99%

Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures

Gkountakou,

Bantilas,

Kavvadias

et al. 2023

Applied Sciences

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The assessment of rocking response is a challenging task due to its high nonlinearity. The present study investigates two methodologies to evaluate finite rocking rotations and overturn of three typical rocking systems. In particular, fuzzy linear regression (FLR) with triangular fuzzy numbers and a hybrid model combining logistic regression and fuzzy logic were adopted. To this end, three typical rocking structures were considered, and nonlinear time history analyses were performed to obtain their maximum response. Eighteen seismic intensity measures (IMs) extracted from recorded seismic accelerograms were considered to predict the responses. In the absence of rocking overturn, the finite rocking rotations and similarity ratios were calculated by adopting the FLR method. Moreover, extensive analysis was performed to evaluate the influence of each IM on the model’s predictions. On the other hand, rocking overturn was evaluated by logistic regression to compute the probability of collapse, followed by the FLR method to estimate the similarity between the different rocking-based structural systems. The root mean square error (RMSE) parameter and the log loss function were determined for every model to assess the predictions that emerged from the two fuzzy methods. As indicated, both methods demonstrated satisfactory results, presenting minimal deviations from the observed values. Finally, in the case of finite rocking rotation predictive models, remarkably high similarity ratios were observed among the various structures, with a median value of 0.96.

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Section: Logistic Regressionmentioning

confidence: 99%

Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures

Gkountakou,

Bantilas,

Kavvadias

et al. 2023

Applied Sciences

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“…In addition, the OD data extracted from the massive taxi‐hailing data sets can fully reflect residents’ travel rules and travel demand distributions [6]. A comprehensive understanding of road travel behaviour can be used for many aspects, such as path planning [7–10], traffic states estimation [11, 12], and location‐based social network [13, 14].…”

Section: Introductionmentioning

confidence: 99%

Analysis of road travel behaviour based on big trajectory data

Wang

Peng

et al. 2020

IET intell. transp. syst

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“…[13][14][15] In fact, fuzzy logic is widely used in combination with neural networks to solve difficult problems in classification, identification and system control. [16][17][18][19] Eric and Finst proposed intelligent transportation system technologies for traffic simulation and management including all methods and components. The intelligent agent system has the ability to respond to any incident urban traffic.…”

Section: Introductionmentioning

confidence: 99%

Prediction of queuing length at metering roundabout using adaptive neuro fuzzy inference system

Abdalla

2019

Measurement and Control

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A metering roundabout where traffic is controlled by signals where phase times are influenced by queue detector occupancy may be the solution to reduce queue lengths under unbalanced traffic flows. In the past decades, a number of studies have attempted to evaluate the effectiveness of metering roundabout, especially on the dominant approach. Little studies, however, have been directed on prediction of the queuing lengths, which is essential to determine the detector locations. This paper introduces a queue length estimation model using adaptive neuro fuzzy inference system for unbalanced roundabout traffic flows. The adaptive neuro fuzzy inference system model consists of an input layer representing four parameters as arrival volumes, conflicting volumes, phase green and red time, and output layer with four neuron representing queuing length. MATLAB software and additional statistical tests were used as the tool to develop the models for the data. In order to conduct credible model validations, model output data were compared against the observed data collected using drones. The results from the analysis demonstrated that adaptive neuro fuzzy inference system model is able to estimate the queuing length at metering roundabouts. Thus, it is expected that the adaptive neuro fuzzy inference system model will help practitioners in determining optimal detector locations and will be a foundation research for roundabouts with signals.

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Traffic Management using Logistic Regression with Fuzzy Logic

Cited by 20 publications

References 15 publications

Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures

Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures

Analysis of road travel behaviour based on big trajectory data

Prediction of queuing length at metering roundabout using adaptive neuro fuzzy inference system

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