Open AccessPassenger Control in Smart Cities Using Deep Learning
Author(s) -
T D Delna,
Dhanya P Pauly,
Dona Johnson,
Jesta Jose
Publication year - 2020
Publication title -
international journal of innovative science and research technology
Language(s) - English
Resource type - Journals
ISSN - 2456-2165
DOI - 10.38124/ijisrt20jul828
Subject(s) - metropolitan area , process (computing) , deep learning , computer science , transport engineering , control (management) , public transport , population , automotive engineering , engineering , computer security , artificial intelligence , geography , archaeology , operating system , demography , sociology
In the current smart city background, people are facing a lot of accidents at the major traffic points of the business towns due to growing population and vehicles growth in smart and metropolitan cities.In this method we consider the auto taxies as well as the public transport. We know that due to the overload in the vehicles the accidents are increasing day by day so using this method the number of accidents be able to be avoided or reduced. This system is introducing the deep learning approach to find the overload in vehicles. We are considering the luggage that is taken along with the passenger and an average weight is given for the load. Then it is combined with the number of passenger and system will predict whether the vehicle is overload or not. Mainly because of using deep learning concepts we can increase the speed of the process and the efficiency. The system will analyse the number of passengers using real time videos using camera and system detect and compare with the overloading conditions to avoidaccidents.