A Fuzzy Logic-Based Smart Traffic Management Systems
- 1 Department of Information Systems, Faculty of Computers and Information, Fayoum University, Fayoum, Egypt
- 2 Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
- 3 New Heliopolis Engineering Institute, Cairo , Egypt
Traffic congestion is a serious problem in many developing countrieslike Egypt. In the presence of thetraditional traffic light system, it creates health risks, wastes time andfuel, and pollutes the air. Therefore, there is a big need for a smart trafficmanagement system. This study contributes in solving this problem byintroducing an artificial intelligence-based smart traffic light system usingfuzzy logic to ensure the smooth flow of traffic in cities. Research resultsindicate that smart traffic light management is very crucial in smart cities toreduce their carbon footprint to save the world and save energy. Other possibleapplication could be implemented with minor system upgrades such as Detectionand Management of traffic Congestion, Automatic Billing of Toll Charges,Automatic detection of speed limit Violation, Route planning, IntelligentInternet of Vehicles, and Prevention of Road Accidents. Four traffic lightapproaches (fixed-time, smart-time, fixed-time-fuzzy logic, andsmart-time-fuzzy-logic) are developed with different eight traffic scenarios.The four approaches are applied with two tools, the first tool is a realMarquette which consists of 4 roads with only one or two vehicles. The secondis a simulation software which consists of 4 roads with more than 100 vehiclesthrough 10 min. Many sensors such as (IR sensors, rain sensors, and LEDs tocontrol traffic light) are used to collect data and then the fuzzy algorithm isused to ensure the smooth flow of traffic in cities. The traffic data acquiredfrom the vehicles is fed into the proposed model to maximize the duration ofthe green light based on the road state. According to experimental results, theproposed smart fuzzy technique simulation software reduced the average waitingtime of the vehicles from 769 to 289 sec in heavy rain condition. It also reducedthe total time for all vehicles from 2490 to 1154 sec.
Copyright: © 2022 Adel Abdallah Abdou, Mohamed Hassan Farrag and A. S. Tolba. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Traffic Congestion
- Fuzzy Logic
- Smart Traffic System
- Arduino Mega