Combined Dense Urban Traffic Surveillance and Principal Component Analysis for Intelligent Transportation Systems

Balasubramaniam, Anandkumar; Paul, Anand; Afghah, Fatemeh

doi:10.1109/tencon.2019.8929589

Cited by 2 publications

(1 citation statement)

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“…Eliciting the usage patterns of the EVs helps to understand the complete usage of the EV charging patterns, car appliances usage, and number of passengers utilizing the EVs. To elicit the patterns, we apply NMF, a dimensionality reduction technique [17][18][19][20] on the EV usage dataset from the smart-grid smart-city (SGSC) project [21,22]. is analysis helps to identify the EV usage patterns by the passengers and business people involved in the SGSC project.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Usage Pattern Analysis Using Nonnegative Matrix Factorization in Renewable EV-Smart Charging Grid Environment

Balasubramaniam

Paul

et al. 2022

Mathematical Problems in Engineering

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The global utilization of electric vehicles (EVs) is exponentially increasing due to the increased availability of cost-efficient EVs and infrastructure managements for the EVs. In spite of the increasing usage of EVs, the problem of EV usage patterns’ analysis and implementing sustainable infrastructure for the EV transportation is still under development. In addition to this, there is a challenging problem of long waiting hours in traffic signals. This study deals with these problems by proposing an architecture that includes EV usage pattern analysis using nonnegative matrix factorization (NMF) technique and renewable solar-powered wireless smart charging grid to effectively utilize or mitigate the long traffic signal waiting hours. The insights from the EV usage patterns are analyzed and presented showing the importance of usage pattern analysis alongside to the presented architecture of renewable solar-powered wireless EV-smart charging grid. These implementations improvise the usage of the EVs and enhancing the transportation experience, which in turn leads to the development of sustainable smart transportation.

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Section: Introductionmentioning

confidence: 99%