An adaptive approach to enhanced traffic signal optimization by using soft-computing techniques

Angulo, Eusebio; Romero, Francisco P.; Garcı́a, Ricardo; Serrano-Guerrero, Jesús; Olivas, José Á.

doi:10.1016/j.eswa.2010.08.011

Cited by 33 publications

(7 citation statements)

References 24 publications

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“…This scheme is based on CTDP and employs the classical structure proposed in the literature based on two phases, off-line and on-line (see Antoniou et al, 2011). The essential ideas of the proposed approach were suggested in Angulo et al (2011). This prototype of urban traffic control system is based on clustering/classification techniques.…”

Section: A Prototype Of An Urban Traffic Control System For Small Andmentioning

confidence: 99%

An Approach to Dynamical Classification of Daily Traffic Patterns

García-Ródenas

López-García

Sánchez-Rico

2016

Computer aided Civil Eng

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This article proposes a prototype of an urban traffic control system based on a prediction‐after‐classification approach. In an off‐line phase, a repository of traffic control strategies for a set of (dynamic) traffic patterns is constructed. The core of this stage is the k‐means algorithm for daily traffic pattern identification. The clustering method uses the input attributes flow, speed, and occupancy and it transforms the dynamic traffic data at network level in a pseudo‐covariance matrix, which collects the dynamic correlations between the road links. A desirable number of traffic patterns is provided by Bayesian Information Criterion and the ratio of change in dispersion measurements. In an on‐line phase, the current daily traffic pattern is predicted within the repository and its associated control strategy is implemented in the traffic network. The dynamic prediction scheme is constructed on the basis of an existing static prediction method by accumulating the trials on set of patterns in the repository. This proposal has been assessed in synthetic and real networks testing its effectiveness as a data mining tool for the analysis of traffic patterns. The approach promises to effectively detect the current daily traffic pattern and is open to being used in intelligent traffic management systems.

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Section: A Prototype Of An Urban Traffic Control System For Small Andmentioning

confidence: 99%

An Approach to Dynamical Classification of Daily Traffic Patterns

García-Ródenas

López-García

Sánchez-Rico

2016

Computer aided Civil Eng

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“…In contrast, signal timing plans at https://doi.org/10.36909/jer.v8i4.8349 isolated intersections operate independently, and these intersections do not have any connection. Isolated signal timing systems are summarized in two main categories: nonadaptive and adaptive signal control systems (Angulo et al, 2011). Nonadaptive traffic signal control corresponds to fixed-time signal control systems, and adaptive traffic signal control systems apply signal timing plans according to changes in the traffic flow.…”

Section: Introductionmentioning

confidence: 99%

“…Nonadaptive traffic signal control corresponds to fixed-time signal control systems, and adaptive traffic signal control systems apply signal timing plans according to changes in the traffic flow. Since real-time monitoring of traffic conditions with developing technology allows reaching the instant real-world data, it would be easier to manage the traffic with adaptive signal control approaches (Angulo et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Radial Basis Function Neural Networks for Urban Traffic Signal Control

Gençosman

2020

Journal of Engineering Research is an international, peer-revie

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In this study, a real-world isolated signalized intersection with a fixed-time signal control system is considered. The signal timing plans are arranged regardless of the traffic density, and these plans cause delays in vehicle queues. To increase the efficiency of the intersection, an adaptive traffic signal control system is proposed to manage the intersection. To find the appropriate adaptive green times for each lane, simulations are performed by traffic simulation software using vehicle arrivals and other information about vehicle movements gathered from the real-world intersection. Then, a hybrid radial basis function neural network is developed to forecast the adaptive green times, which is trained and tested with historical arrivals and simulation results. The performance of the proposed network is compared with well-known data mining classification methods, such as support vector regression, k-nearest neighbors, decision tree, random forest, and multilayer perceptron methods, by different evaluation parameters. The comparison results provide that the developed radial basis function neural network outperforms other classification methods and can be successfully used for forecasting adaptive green times as an alternative to complex unsupervised classification methods.

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“…Obtained results showed that realtime adaptive management model is quite successful for improving the performance of intersection. Angulo et al [17] presented an application of different computing techniques for adaptive traffic management. e model developed was compared with nonadaptive static and dynamic management.…”

Section: Introductionmentioning

confidence: 99%

A Differential Evolution Algorithm‐Based Traffic Control Model for Signalized Intersections

Çakıcı

Murat

2019

Advances in Civil Engineering

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Sustainable management of traffic flows at signalized intersections is an important issue in terms of traffic engineering. The minimization of lost time, emission, fuel consumption, etc., can be achieved by optimization-based intersection management. In this study, a new traffic signal control model is developed for the management of three-leg signalized intersections. In the proposed model, signal timing and signal phasing are optimized simultaneously using Differential Evolution (DE) algorithm which is one of the population-based metaheuristic algorithms. The effectiveness of the model is tested on sample traffic scenarios with VISSIM simulation software considering average vehicle delay performance criteria. Results show that the proposed approach may reduce the average vehicle delay between the rates of 28%–42% and 3%–38% comparing to the optimum fixed-time signal control and vehicle-actuated signal control for tested scenarios, respectively.

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An adaptive approach to enhanced traffic signal optimization by using soft-computing techniques

Cited by 33 publications

References 24 publications

An Approach to Dynamical Classification of Daily Traffic Patterns

An Approach to Dynamical Classification of Daily Traffic Patterns

Hybrid Radial Basis Function Neural Networks for Urban Traffic Signal Control

A Differential Evolution Algorithm‐Based Traffic Control Model for Signalized Intersections

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