Fuzzy Q-Learning Traffic Light Control based on Traffic Flow and Pedestrian Number Estimated from Visual Information

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Jalali Moghaddam marjan; Hosseini matin; SAFABAKHSH REZA

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF COMPUTER AND KNOWLEDGE ENGINEERING Year:2019 | Volume:2 | Issue:1 Page(s): 1-10

Download Full-Text

View:

239

Download:

213

Cites:

Information Journal Paper

Title

Fuzzy Q-Learning Traffic Light Control based on Traffic Flow and Pedestrian Number Estimated from Visual Information

Author(s)

Jalali Moghaddam marjan | Hosseini matin | SAFABAKHSH REZA | Issue Writer Certificate

Keywords

Intelligent traffic control system

Traffic density

Fuzzy logic

traffic light control

Abstract

A vision-based Intelligent traffic control system is a robust framework that controls the traffic flow in real-time by estimating the Traffic density near traffic lights. In this paper, a traffic light control system based on fuzzy Q-learning is proposed according to the vehicle density and the pedestrian number estimated from the visual information. The aim of proposed approach is to minimize the pedestrian and the car waiting time and maximize throughput for an isolated 4-way traffic intersection. Also, the pedestrian traffic light is controlled based on the Fuzzy logic. The states and actions of the Q-learning variables are set by a fuzzy algorithm which can be learned through environmental interactions. The system can detect the number of pedestrians and vehicles using visual information from cameras and machine vision algorithms. The fuzzy control system can adjust the sequence of green phases to decrease the total waiting time and the mean of the queue length. The proposed algorithm was simulated for one hour for each of 14 different traffic conditions and was assessed and compared with the preset cycle time and vehicle actuated approaches. The results showed the proposed algorithm could decrease the total waiting time and the mean of the queue length effectively.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

Jalali Moghaddam, marjan, Hosseini, matin, & SAFABAKHSH, REZA. (2019). Fuzzy Q-Learning Traffic Light Control based on Traffic Flow and Pedestrian Number Estimated from Visual Information. JOURNAL OF COMPUTER AND KNOWLEDGE ENGINEERING, 2(1), 1-10. SID. https://sid.ir/paper/353643/en

Vancouver: Copy

Jalali Moghaddam marjan, Hosseini matin, SAFABAKHSH REZA. Fuzzy Q-Learning Traffic Light Control based on Traffic Flow and Pedestrian Number Estimated from Visual Information. JOURNAL OF COMPUTER AND KNOWLEDGE ENGINEERING[Internet]. 2019;2(1):1-10. Available from: https://sid.ir/paper/353643/en

IEEE: Copy

marjan Jalali Moghaddam, matin Hosseini, and REZA SAFABAKHSH, “Fuzzy Q-Learning Traffic Light Control based on Traffic Flow and Pedestrian Number Estimated from Visual Information,” JOURNAL OF COMPUTER AND KNOWLEDGE ENGINEERING, vol. 2, no. 1, pp. 1–10, 2019, [Online]. Available: https://sid.ir/paper/353643/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops