Development of Intelligent Traffic Light System Based On Congestion Estimation Using Fuzzy Logic

Alam, Javed; Pandey, Prof. M. K.

doi:10.9790/0661-16363644

Cited by 15 publications

(12 citation statements)

References 10 publications

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“…In this modeling, the traffic light time intervals are changed adaptively to provide priority service to emergency vehicles and services as follows: (1) change the status of traffic lights, (2) change of speeds, (3) release the lines for emergency vehicles, (4) allow using pre-booked lines, and (5) using redirection protocols. In [5], Alam et al presented a method of calculating traffic flow using a fuzzy logic approach. In this method, two electromagnetic sensors are placed on each side of the road to gather information on the traffic congestion.…”

Section: Smart Traffic Lights Mmethodsmentioning

confidence: 99%

“…According to Figure 1, a grid cell with m = 10 h (100 cells) is considered which u 1 is supposed to pass cells through (c 32 , c 25 , c 28 , c 46 , c 68 , c 86 , c 75 , c 83 , c 71 , c 53 } in 10 time units. Therefore, the vehicle's movement pattern is: 2) , (72, 3) , (54, 4) , (76, 5) , (58, 6) , (47, 7) , (28, 8) , (7, 9) , (25, 10)} Table 2 contains the notations that used in the system description.…”

Section: System Descriptionmentioning

confidence: 99%

“…Although this method has significant impacts on reducing air pollution and traffic congestion, the lack of public infrastructure and low quality of services may propel people to opt private vehicles in a developing country where the public infrastructure may be poor [4], (II) smart traffic lights that can control the signal timing based on the traffic condition in real time i.e., number of vehicles on the road and the length of the queues. One of the benefits of smart traffic lights is the reduced delay time in intersections and the improved support for emergency vehicles [5]. Despite its benefits, the smart traffic method will need overly expensive equipment in its implementation [6], (III) modern technology methods can take advantage of hybrid electric vehicles on reduction of air pollution index and fossil fuel [7], but this method has high maintenance cost, (IV) limited traffic zone methods can restrict mobility of private vehicles based on some criteria i.e., road pricing, restrict traffic zone, and odd-even traffic restriction.…”

Section: Introductionmentioning

confidence: 99%

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Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City

Azami¹,

Jan

Iranmanesh³

et al. 2020

Sensors

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Traffic control is one of the most challenging issues in metropolitan cities with growing populations and increased travel demands. Poor traffic control can result in traffic congestion and air pollution that can lead to health issues such as respiratory problems, asthma, allergies, anxiety, and stress. The traffic congestion can also result in travel delays and potential obstruction of emergency services. One of the most well-known traffic control methods is to restrict and control the access of private vehicles in predetermined regions of the city. The aim is to control the traffic load in order to maximize the citizen satisfaction given limited resources. The selection of restricted traffic regions remains a challenge because a large restricted area can reduce traffic load but with reduced citizen satisfaction as their mobility will be limited. On the other hand, a small restricted area may improve citizen satisfaction but with a reduced impact on traffic congestion or air pollution. The optimization of the restricted zone is a dynamic multi-regression problem that may require an intelligent trade-off. This paper proposes Optimal Restricted Driving Zone (ORDZ) using the Genetic Algorithm to select appropriate restricted traffic zones that can optimally control the traffic congestion and air pollution that will result in improved citizen satisfaction. ORDZ uses an augmented genetic algorithm and determinant theory to randomly generate different foursquare zones. This fitness function considers a trade-off between traffic load and citizen satisfaction. Our simulation studies show that ORDZ outperforms the current well-known methods in terms of a combined metric that considers the least traffic load and the most enhanced citizen satisfaction with over 30.6% improvements to some of the comparable methods.

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Section: Smart Traffic Lights Mmethodsmentioning

confidence: 99%

Section: System Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City

Azami¹,

Jan

Iranmanesh³

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…The total number of rules is the product of the total number of each membership function involved in a traffic situation; in this case, VQueuel (3), peddelay(3), Totalped(3) and weather (2). Figure 8 is a display rule editor containing some of the rules.…”

Section: Fuzzy Rules Formationmentioning

confidence: 99%

Fuzzy Logic Concept for Safety Driven Vehicle-Pedestrian Traffic Interaction

Olanrewaju¹,

Obiniyi²,

Junaidu³

2017

IJCA

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In recent times, it has been discovered that fuzzy logic is an important tool for effective traffic control system. Research on intelligent traffic systems has revealed that the implementation of linguistic variables in fuzzy systems help in taking care of diverse possible decisions that can be taken by humans in traffic control. However, the majority of these works focus on vehicular traffic without adequate consideration for pedestrian crossings. This research therefore focuses on incorporation of pedestrian crossing variables into vehicular traffic control using fuzzy logic. The implementation of the fuzzy logic inference system was carried out using MATLAB 2014a. The impact of pedestrian delay, total pedestrian were part of consideration for signal time allocation. The result proved that pedestrian delay has significant contribution to traffic control systems to enhance safety of pedestrians.

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“…Evaluation in this research expert system was adopted using fuzzy logic [3] principals for teachers' evaluation process. In this research they have developed the knowledge acquisition tool for the teachers' assessment problem in the development of intelligent expert system.…”

Section: Related Workmentioning

confidence: 99%

A Soft Computing Model for Evaluating Teachers’Overall Performance using Fuzzy Logic

Alam¹,

P²,

ey³

2017

J Inform Tech Softw Eng

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In the entire world, there is a widespread recognition that evaluation framework is a key to building stronger and fairer academic institution system. All countries emphasize the evaluation not as end in itself but instead an important tool for achieving improved student outcomes. Teacher evaluation typically has two major purposes. First, it seeks to improve the teacher's own practice by identifying strengths and weaknesses for further professional development and involves helping teachers learn about, reflect on and adjust their practice. Second it is aimed at holding teacher's accountability for their performance in enhancing student learning. It typically entails performance based career advancement and/or salaries, bonus pay, or the possibility of sanctions for underperformance and usually involves evaluating performance at nodal points in a teacher's career. The term soft computing represents the combination of emerging problem solving technologies, such as fuzzy logic, probabilistic reasoning, neural networks and genetic algorithms. Each of these technologies provides us with complementary reasoning and searching methods to solve complex real world problems. We have proposed a soft computing model for evaluating teachers' overall performance using fuzzy logic. There are two different modules namely teachers' overall performance module-1 (TOP-M1) and teachers' overall performance module-2 (TOP-M2). First module TOP-M1, calculates teaching performance. Second module TOP-M2, calculates academic and administrative performance. On the bases of teaching performance and academic and administrative performance we calculate overall performance. Software has been developed in MATLAB. This soft computing model for evaluating teachers' overall performance using fuzzy logic will not only be useful for decision makers to evaluate teachers' abilities and improve student outcomes but may also be adopted in writing Annual Confidential Reports(ACR) for appraisal of all the teachers of an academic institution. Simulation results verify the performance of our proposed soft computing model for evaluating teachers' overall performance using fuzzy logic.

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Development of Intelligent Traffic Light System Based On Congestion Estimation Using Fuzzy Logic

Cited by 15 publications

References 10 publications

Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City

Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City

Fuzzy Logic Concept for Safety Driven Vehicle-Pedestrian Traffic Interaction

A Soft Computing Model for Evaluating Teachers’Overall Performance using Fuzzy Logic

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