K
Kranti Kumar
Researcher at Ambedkar University Delhi
Publications - 15
Citations - 402
Kranti Kumar is an academic researcher from Ambedkar University Delhi. The author has contributed to research in topics: Traffic noise & Artificial neural network. The author has an hindex of 6, co-authored 11 publications receiving 302 citations. Previous affiliations of Kranti Kumar include Indian Institutes of Technology & Indian Institute of Technology Roorkee.
Papers
More filters
Journal ArticleDOI
Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network
TL;DR: Results show that Artificial Neural Network has consistent performance even if time interval for traffic flow prediction was increased from 5 minutes to 15 min minutes and produced good results even though speeds of each category of vehicles were considered separately as input variables.
Journal ArticleDOI
Short term traffic flow prediction in heterogeneous condition using artificial neural network
TL;DR: Artificial Neural Network is applied for short term prediction of traffic volume using past traffic data and produced good results in this study even though speeds of each category of vehicles were considered separately as input variables.
Book ChapterDOI
Traffic Accident Prediction Model Using Support Vector Machines with Gaussian Kernel
TL;DR: Urban traffic accident analysis has been done using support vector machines (SVM) with Gaussian kernel to reveal that proposed model has significantly higher predication accuracy as compared with traditional MLP approach.
Journal ArticleDOI
Optimized height of noise barrier for non-urban highway using artificial neural network
TL;DR: Artificial neural network can be useful to determine the height of noise barrier accurately, which can effectively achieve the desired noise level reduction, for a given set of traffic volume, vehicular speed, highway geometry, and site conditions.
Mathematical modeling of road traffic noise prediction
TL;DR: A road traffic noise prediction model for Indian conditions is developed using regression analysis which is based onCalixto model and it was observed that Calixto Model could be satisfactorily applied for Indian Conditions as they give accepted results with a good value.