Open Access Open Access  Restricted Access Subscription Access

Fatal Pedestrian Collision Incidence and Vehicle Speed Control

S. A. Arunmozhi, R. S. Navamani, S. Nivetha, M. Nivethitha, S. Loramary

Abstract


In our everyday life, time is extremely significant factor. To complete the preferred job within the short span of time, the speed of operation must be as high as possible. Considering our day to day living, vehicle density in the roads is continuously increasing to meet out the needs. Moreover, the drivers are not ready to follow the rules and regulation of  traffic control department. Many of the vehicle drivers run the vehicle extremely speedy in the congested regions. Since the vehicle speed is under the control of drivers and the drivers are fond of driving the vehicle at over speed, many accidents happen. To prevail over these crisis, an automatic vehicle speed control with efficient video processing is proposed in this work. The proposed work controls the speed of vehicle steadily when the constrained locations are found. Camera is used for video recording of road scene. Detection and identification of road traffic sign, school zone. hospital zone, etc. is achieved by implementing CNN algorithm with ATMEGA controller.  The proposed system controls the vehicle speed in an effective manner and alerts the driver.


Full Text:

PDF

References


Jayasudha, K., & Chandrasekar, C. (2009). An overview of data mining in road traffic and accident analysis. Journal of Computer Applications, 2(4), 32-37.

Ossiander, E. M., & Cummings, P. (2002). Freeway speed limits and traffic fatalities in Washington State. Accident Analysis & Prevention, 34(1), 13-18.

Evanco, W. M. (1999). The potential impact of rural mayday systems on vehicular crash fatalities. Accident Analysis & Prevention, 31(5), 455-462.

Solaiman, K. M. A., Rahman, M. M., & Shahriar, N. (2013, May). Avra Bangladesh collection, analysis & visualization of road accident data in Bangladesh. In 2013 International Conference on Informatics, Electronics and Vision (ICIEV) (pp. 1-6). IEEE.

Kumar, S., & Toshniwal, D. (2015, December). Analysing road accident data using association rule mining. In 2015 International Conference on Computing, Communication and Security (ICCCS) (pp. 1-6). IEEE..

Krishnaveni, S., & Hemalatha, M. (2011). A perspective analysis of traffic accident using data mining techniques. International Journal of Computer Applications, 23(7), 40-48.

El Tayeb, A. A., Pareek, V., & Araar, A. (2015). Applying association rules mining algorithms for traffic accidents in Dubai. International Journal of Soft Computing and Engineering, 5(4), 1-12.

Li, L., Shrestha, S., & Hu, G. (2017, June). Analysis of road traffic fatal accidents using data mining techniques. In 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA) (pp. 363-370). IEEE.

Äyrämö, S., Pirtala, P., Kauttonen, J., Naveed, K., & Kärkkäinen, T. (2009). Mining road traffic accidents. Reports of the Department of Mathematical Information Technology/University of Jyväskylä. Series C, Software and computational engineering, (2/2009).

Shanthi, S., & Ramani, R. G. (2011). Classification of vehicle collision patterns in road accidents using data mining algorithms. International Journal of Computer Applications, 35(12), 30-37.


Refbacks

  • There are currently no refbacks.