Fuzzy Rules to Improve Traffic Light Decisions in Urban Roads

Rocha, José Antonio Castán; Martínez, Salvador Ibarra; Menchaca, Julio Laria; Villanueva, Jesús David Terán; Berrones, Mayra Guadalupe Treviño; Cobos, J.; Agundis, D. Uribe

doi:10.4236/jilsa.2018.102003

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Traffic clearance at intersections by giving priority to emergency transit [14][15][16][17][18] extended the green time on detection of emergency vehicle. The recent works in this area include [19] in which a fuzzy inference system uses five input variables to fix the green light interval. Although an 18% improvement in performance was obtained while conducting experiments in simulated environment but due to large scale of inputs the system complexity was high.…”

Section: Introductionmentioning

confidence: 99%

“…This made the system less complex and ease of physical implementation. The cost and complexity of the systems [12][13][14][15][16][17][18][19] due to large scale of inputs required was taken care in the proposed design as the system requires inputs in terms of the traffic volume sensed at each lane and detection of EMV. The proposed system in this paper is designed in a way to achieve simplicity in collection of inputs and easy implementation for both busy and underutilized intersection.…”

Section: Introductionmentioning

confidence: 99%

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Improving traffic and emergency vehicle clearence at congested intersections using fuzzy inference engine

Agrawal¹,

Paulus²

2021

IJECE

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Traffic signals play an important role in controlling and coordinating the traffic movement in cities especially in urban areas. As the traffic is exponentially increasing in cities and the pre-timed traffic light control is insufficient in effective timing of the traffic lights, it leads to poor traffic clearance and ultimately to heavy traffic congestion at intersections. Even the Emergency vehicles like Ambulance and Fire brigade are struck at such intersections and experience a prolonged waiting time. An adaptive and intelligent approach in design of traffic light signals is desirable and this paper contributes in applying fuzzy logic to control traffic signal of single four-way intersection giving priority to the Emergency vehicle clearance. The proposed control system is composed of two parallel controllers to select the appropriate lane for green signal and also to decide the appropriate green light time as per the real time traffic condition. Performance of the proposed system is evaluated by using simulations and comparing with pre-timed control system in changing traffic flow condition. Simulation results show significant improvement over the pre-timed control in terms of traffic clearance and lowering of Emergency vehicle wait time at the intersection especially when traffic intensity is high.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving traffic and emergency vehicle clearence at congested intersections using fuzzy inference engine

Agrawal¹,

Paulus²

2021

IJECE

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“…It is found that fuzzy controllers effectively reduce the delay. Rocha et al [17] customized a fuzzy inference system that could determine when the semaphore should set the green light interval in accordance with particular road needs. Zuraime et al [18] determined the most efficient and ideal timing for traffic signals to accommodate various traffic densities.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Road Traffic Modeling through a Novel Real Time Traffic Simulator

Singh,

Aggarwal

2023

Curr. Appl. Sci. Technol.

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Dynamic traffic control is a challenging task that involves meeting rising traffic demands and cutting down on intersection delays. The existing yellow/red/green light fixed transition periods used by traffic controllers make it impossible for them to adapt to changing real-time traffic conditions at intersections. Furthermore, it would be impractical to hire traffic officers for every intersection throughout the day due to a lack of personnel, and even if sufficient personnel are available, it would be a very expensive set up. A fuzzy based traffic model was designed and simulated in real time conditions using the developed traffic simulator algorithm to control the traffic jamming at road intersections. The developed fuzzy model was based on three fuzzy inputs and its performance was measured for 13 cases of varying road width. The developed model outperformed the traditional fixed-time delay model in all the cases and the level of improvement was further increased when the congestion was high. Narrower roads were more congested and the improvement with fuzzy systems as compared to its fixed time delay counterparts was as high as 26%. This research findings clearly support the use of fuzzy logic for handling the most challenging problem of traffic congestion in densely populated regions.

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“…The second module www.ijacsa.thesai.org calculated the green time extension of the chosen phase with the help of queue length as input. Another work [11] considered the possibility to change the green light duration at an isolated four-lane intersection using fuzzy inference system by taking the road condition, traffic and time of the day as major deciding inputs. Researchers in [12] applied fuzzy logic to improve traffic light by taking queue length, arrival flow and exit flow as inputs and calculated the urgency degree using fuzzy rules.…”

Section: Introductionmentioning

confidence: 99%

Smart Intersection Design for Traffic, Pedestrian and Emergency Transit Clearance using Fuzzy Inference System

Agrawal¹,

Paulus²

2021

IJACSA

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Traffic flow is regulated and controlled with the aid of traffic signals implemented at all major intersections in urban areas. With the increase in vehicles, the traditional control strategies are incapable of clearing heavy traffic which leads to long traffic queues and prolonged waiting time at intersections. Smart cities are increasingly adopting solutions by developing smart traffic lights to improve the flow of vehicles. A major demand arises to increase the efficiency of traffic controllers with the objective to minimize traffic congestion, prioritize emergency transit and give way to pedestrians to cross the lanes at an intersection. This requires leveraging the existing techniques that identify the best solutions at the lowest possible cost. This paper proposes Fuzzy Adaptive Control System (FACS) that uses fuzzy logic to decide the phase sequence and green-time for each lane based on sensed input parameters. It is designed with an aim to improve traffic clearance at isolated intersection especially in peak traffic hours of the day along with giving precedence to emergency vehicle as soon as it is detected and also assist pedestrian passage thus reducing their waiting time at the intersection. Performance of the proposed Fuzzy Adaptive Control System (FACS) is evaluated through simulations and compared with Pre-Timed Control System (PTCS) and Traffic Density-based Control System (TDCS) at a busy intersection with lanes leading to offices, schools and hospitals. Simulation results show significant improvement over PTCS and TDCS in terms of traffic clearance, immediate addressing of the emergency vehicle and giving preference to pedestrian passage at the intersection.

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Fuzzy Rules to Improve Traffic Light Decisions in Urban Roads

Cited by 5 publications

References 6 publications

Improving traffic and emergency vehicle clearence at congested intersections using fuzzy inference engine

Improving traffic and emergency vehicle clearence at congested intersections using fuzzy inference engine

An Efficient Road Traffic Modeling through a Novel Real Time Traffic Simulator

Smart Intersection Design for Traffic, Pedestrian and Emergency Transit Clearance using Fuzzy Inference System

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