Adaboost Triggered Extreme Learning Machine Algorithm for Traffic Flows Prediction in Vehicular Networks

Authors

  • Jothy. N , Jayanthi. K

Abstract

With the advent of vehicular IoT, smart transportation systems play a vital role in today's life
demanding the prediction of the vehicular traffic flows and efficient data transfer. At the same
time, through machine learning (ML) algorithms, prediction of traffic flow has reached its new
dimension but still usage of conventional machine learning algorithms needs improvisation in terms
of prediction accuracy. Further so far, the literature work has not investigated for an Indian road
scenario which serves as the motivation for this research study. Henceforth, this paper proposes a
modified (ML) approach referred to as “AdaBoost Triggered Extreme Learning Machine
algorithm” (ABT-ELM) for predicting high accurate traffic flows in Puducherry Union Territory,
India. In this work, ELM predictor is trained using the training samples obtained through the Adaboost algorithm. This algorithm is applied to obtain the ensemble weights of each layer of ELM
predictor which is responsible for the improved prediction levels. Nearly two months of real-time
traffic datasets from union territory of Puducherry, India are analysed and it as well serves as the
test data-set for evaluating the proposed algorithm, using SUMO with OMNET++ platforms. The
empirical study demonstrates that the proposed hybrid ABT-ELM learning approach outperforms
classical learning approaches in terms of accuracy, precision and recall. Henceforth, it is
suggested that the proposed ABT-ELM learning approach is highly promising and suitable for
traffic prediction and management systems in Indian scenario.

Published

2020-10-15

Issue

Section

Articles