Application of Fuzzy Logic in Prevention of Road Accidents Using Multi Criteria Decision Alert

Main Article Content

Rajshri Gupta
Onkar K. Chaudhari


With development and growth of technology, there has been an upsurge in number of vehicles on the street globally, which has resulted in increase of traffic jams and road accidents. This growing problem is being studied by researchers to find the solution. Fuzzy logic method is widely used in Intelligent Transportation Systems (ITS). In our study, considering various parameters required to control the vehicle safety, a fuzzy logic model was developed for automatic speed alert, brake alert with sensors and cameras in vehicles. For intelligent transport, Fuzzy Interface System (FIS) is developed to support drivers’ decision making and alert them to control speed and brake so as to avoid accidents. The Rule Based System was established using various linguistic rules. Accordingly, 50 effective IF—Then rules were generated in the present study. In conclusion, this model can be inbuilt in the vehicles, with different number of inputs and outputs and for a fully automated system, to reduce the road accidents and traffic jams. The outcome of the research would lead to reduction road accidents.

Fuzzy logic, Intelligent Transportation Systems, Fuzzy Interface System, speed alert, brake alert, sensors, vehicle safety.

Article Details

How to Cite
Gupta, R., & Chaudhari, O. K. (2020). Application of Fuzzy Logic in Prevention of Road Accidents Using Multi Criteria Decision Alert. Current Journal of Applied Science and Technology, 39(36), 51-61.
Original Research Article


Zadeh LA. Fuzzy algorithm. Information and Control. 1968;12:94-102.

Zadeh LA. Outline of the new approach to the analysis of complex system and decision processes, IEEE Transactions on Systems, Man and Cybernetics, SMC-3. 1973;28–44.

Zadeh LA. Making computers think like people. IEEE Spectrum. 1984;8:26-32.

Bellman RE, Zadeh LA. Decision making in a fuzzy environment. Management Science. 1970;17(4):141-164.

Chaudhari OK, Khot PG, Deshmukh KC, Bawne NG. Application of fuzzy logic in decision making to optimize the profit in the field of rice cultivation. International Journal of Applied Mathematics and Applications. 2011;3(2):153-165.

Hao Wang, Lai Zheng, Xianghai Meng. Traffic accidents prediction model based on fuzzy logic. Tan H, Zhou M. (Eds.): CSE, Part I, CCIS. 2011;201:101–108.

Terzi Serdal, Topkara Yaşar, Albayrak Mehmet. A fuzzy logic model for prevention of vehicle pursuit distance as automatically, International XII. Turkish Symposium on Artificial Intelligence and Neural Networks – TAINN; 2003.

Rehman Abbad Ur, Mushtaq Zohaib, Qamar Muhammad Attique. Fuzzy logic based automatic vehicle collision prevention system. IEEE Conference on Systems, Process and Control, Bandar Sunway, Malaysia. 2015;18-20.

Massami Erick P, Myamba Benitha M. Evaluation of road traffic accident risk based on fuzzy set theory. International Journal of Emerging Technology and Advanced Engineering. 2014;4(8).

Haoran Wu Yan Li, Chaozhong Wu, Zheng Ma, Haiying Zhou. A longitudinal minimum safety distance model based on driving intention and fuzzy reasoning. 4th International Conference on Transportation Information and Safety (ICTIS). 2017;158-162.

Ali Karimi, Samira Eslamizad, Maryam Mostafaee, Mahin Haghshenas, and Mahdi Malakoutikhah. Road accident modeling by fuzzy logic based on physical and mental health of drivers. International Journal of Occupational Hygiene, by Iranian Occupational Health Association (Ioha) Ijoh. 2016;8:208-216.

Elda Maraj, Shkelqim Kuka. Prediction of road accidents using fuzzy logic. Journal of Multidisciplinary Engineering Science and Technology (JMEST). 2019;6(12). ISSN: 2458-9403.

Kikuchi Shinya, Mijkovic Dragna. A Method to adjust observed transportation data: Application to passenger counts on a transit line. 7803-7078-3/01(c). IEEE; 2001.