Designing an intelligent ontological system for traffic light control in isolated intersections

Keyarsalan, Maryam; Montazer, Gholam Ali

doi:10.1016/j.engappai.2011.03.005

Cited by 23 publications

(9 citation statements)

References 14 publications

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“…While [3,4,5] deal with ontological modeling of infrastructure geometry and necessary elements, [6] shows an ontological model of a traffic flow management system for an intersection traffic light with several succeeding rules deciding about traffic light cycle times and phases that make use of the knowledge in the ontology. However, these rules do not expand the ontology by themselves and thus are not part of the ontology.…”

Section: Introductionmentioning

confidence: 99%

Traffic intersection situation description ontology for advanced driver assistance

Hulsen

Zöllner

Weiss

2011

2011 IEEE Intelligent Vehicles Symposium (IV)

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This work provides an approach to create a generic situation description for advanced driver assistance systems using logic reasoning on a traffic situation knowledge base. It contains multiple objects of different type such as vehicles and infrastructure elements like roads, lanes, intersections, traffic signs, traffic lights and relations among them. Logic inference is performed to check and extend the situation description and interpret the situation e. g. by reasoning about traffic rules.The capabilities of our ontological situation description approach are shown at the example of complex intersections with several roads, lanes, vehicles and different combinations of traffic signs and traffic lights. Real-time issues are discussed thereon.

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Section: Introductionmentioning

confidence: 99%

Traffic intersection situation description ontology for advanced driver assistance

Hulsen

Zöllner

Weiss

2011

2011 IEEE Intelligent Vehicles Symposium (IV)

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“…The fuzzy logic traffic light controllers utilize sensors to count the number of vehicles. Therefore, the resulting controllers control the traffic lights according to traffic density [6]. There are different approaches to control traffic lights based on artificial intelligence methods such as fuzzy logic, neural networks, reinforcement learning and evolutionary algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Keyarsalan et al used computer vision techniques and neural networks to extract the traffic data and applied a fuzzy ontology to control traffic light in isolated intersection [6]. Shakeri et al introduced a three layer fuzzy system based on the cellular automata for optimizing traffic light control [14].…”

Section: Introductionmentioning

confidence: 99%

Traffic light control based on fuzzy Q-leaming

Moghaddam

Hosseini

Safabakhsh

2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

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Traffic is an issue that many big cities are confronted with because of ever-increasing population growth. In this paper we propose a two phase traffic light control system based on fuzzy Q-learning for an isolated 4-way intersection. The states and actions of the Q-learning variables is set by a fuzzy algorithm which can be learned through environmental interactions and taking advantage of fuzzy logic. The proposed algorithm was simulated for a period of one hour for each of 14 different traffic conditions. Comparison with other methods was carried out on the 14 traffic conditions. The results showed that the proposed algorithms decrease the total waiting time and the mean of queue length.Index Terms-Fuzzy logic, fuzzy Q-learning, traffic light control.

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“…Due to the complexity of the model, they gave the expression of the partial derivative and changed the rate of the objective function to deal with the problem. In terms of fuzzy sets, Keyarsalan and Montazer [13] applied fuzzy ontology to control traffic light. The most distinguishing characteristic of this model was considering a module for reusing traffic light control knowledge.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Light Signal Timing Using Simulation

Kamran¹,

Ramezani²,

Masoumzadeh³

et al. 2017

Communications on Advanced Computational Science with Applicati

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Increasing trend in the number of vehicles and consequently intensifying the traffic volume, traffic lights optimum timing plays a vital role in congestion mitigation in the critical intersections of urban areas. This paper gives an insight into traffic light timing using simulation modeling methods. In fact, this study aims at minimizing the average time that entities (vehicles) spend in the system (intersection). First, real data including vehicles arrive and leave times, and the time spent waiting at a red light etc. is collected through monitoring an intersection in downtown Urmia, Iran. Then, a simulation model is developed using Arena Simulation Software. The most important feature of the proposed model is the replacement a four-stroke traffic light with the current three-stroke one. After model validation, several alternatives are considered for traffic light timing and the one which resulted the minimum average time spent in the system is selected. According to the simulation results, the four-stroke traffic light yielded a better performance compared to the three-stroke one.

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Designing an intelligent ontological system for traffic light control in isolated intersections

Cited by 23 publications

References 14 publications

Traffic intersection situation description ontology for advanced driver assistance

Traffic intersection situation description ontology for advanced driver assistance

Traffic light control based on fuzzy Q-leaming

Traffic Light Signal Timing Using Simulation

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