Optimizing urban traffic control using a rational agent

Martínez, Salvador Ibarra; Rocha, José Antonio Castán; Laria-Menchaca, Julio

doi:10.1631/jzus.c1400037

Cited by 4 publications

(2 citation statements)

References 41 publications

(42 reference statements)

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“…Preliminary results of the model are compared against real data as well as versus results presented in Ref. [17]. Experiment 1.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Improving Traffic Conditions by Using Neural Networks

C.¹,

R.²,

M.³

et al. 2016

CTA

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Abstract:The paper promotes the implementation of a neural network approach to improve one of the most transcendent traffic conditions: the mobility of the cars in any particular junction. Neural networks have proven to be an effective paradigm of modern computing, providing extensive benefits in a wide range of applications. In this sense, the paper uses a BPNN (backpropagation neural network) model. The three input nodes are related to: n1: the amount of cars in the road; n2: the green light interval; and n3: the distance (taking into account the quantity of cars) between the first car in the intersection and the last car in the longest line in front of it. In particular, the paper promotes that each traffic light signal will be capable of offering a new green light interval according to the requirement and constrains of the vitality, ensuring a vehicular mobility level greater than 65%. To assess this idea, the paper presents two experiments confronting the real world data versus experimental results. For example, in the first experiment, the BPNN improves the performance of the real data about vehicular mobility in almost 30%. Finally, some conclusions and future work are presented.

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“…Preliminary results of the model are compared against real data as well as versus results presented in Ref. [17]. Experiment 1.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Indeed there has been a growing interest among the transportation community in the uses of traffic simulation modeling for evaluating the systems performance of transportation networks [17]. In this sense, the paper proposes the implementation of a traffic light signal control based on BPNN (backpropagation neural network).…”

Section: Neural Network Modelmentioning

confidence: 99%

Improving Traffic Conditions by Using Neural Networks

C.¹,

R.²,

M.³

et al. 2016

CTA

View full text Add to dashboard Cite

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Improving Vehicular Mobility in Urban Traffic Using Ubiquitous Computing

Castán¹,

Martínez²,

Menchaca³

et al. 2016

JCC

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The paper introduces a novel paradigm to use ubiquitous computing in urban traffic control as a methodology to include the benefits of considering physical elements implicated in the environment. This promising idea arises from previous results in the literature, where ubiquitous computing achieves significant and promising results in diverse scenarios. Some works in the state-ofthe-art clearly state that traditional traffic light signals are not capable of offering higher service levels when they should control the vehicular mobility because of different constraints. In this sense, the paper proposes an adaptation of the main ideas of ubiquitous computing as a metaphor to facilitate the interaction between users and traffic infrastructures in order to improve the users' experience on the road.

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Increasing Efficiency of an Intelligent Semaphore by Implementing an ID3 Approach in Decision-Making

Rocha¹,

Martínez²,

Menchaca³

et al. 2018

JCC

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The main objective of a semaphore is to provide a correct and fluent vehicular mobility. Many countries around the world are using such devices in urban areas. However, the traditional semaphore operative ways are outdated. We report in a previous contribution the development of a methodology capable of improving the vehicular mobility by proposing a new green light interval based on road conditions with a CBR approach. However, this proposal should include whether it is needed to modify such light duration. To do this, the paper proposes the adaptation of an inductive decision tree helping to decide when the semaphore should try to fix the green light interval according to specific road requirements. Some experiments are conducted in a simulated environment to evaluate the pertinence of implementing a decision-making before the CBR methodology. For example, using the ID3 approach the decisions of the system improve almost 8% in a set of 10,000 experiments. Finally, some conclusions are drawn to emphasize the benefits of including this technique in a methodology to implement intelligent semaphores.

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Optimizing urban traffic control using a rational agent

Cited by 4 publications

References 41 publications

Improving Traffic Conditions by Using Neural Networks

Improving Traffic Conditions by Using Neural Networks

Improving Vehicular Mobility in Urban Traffic Using Ubiquitous Computing

Increasing Efficiency of an Intelligent Semaphore by Implementing an ID3 Approach in Decision-Making

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