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Deep Learning Based Smart Traffic Management System

EasyChair Preprint no. 9634

34 pagesDate: January 30, 2023


Artificial intelligence can be used to develop smart solution for traffic management that are effective decision-makers.The use of technology has profoundly changed the field of intelligent transport systems (ITS). Newer developments such as the internet of thing (IoT) based smart smoke detecting sensors or the surveillance camera feeds, and social media, along with increased demand for infrastructure, have opened up many possibilities. In response, STMS is taking advantage of artificial intelligence (AI) to create data-driven solutions that can help with traffic management decisions. Our proposed smart traffic management platform (STMP) AI techniques have certain limitations which this new AI technology aims to address, by making use of advanced machine learning techniques such as online incremental unsupervised based machine learning, and deep sequence based model learning. These techniques will help to improve predictive accuracy and automate decision-making processes, so that large data streams and traffic volatility can be better managed.The current artificial intelligence techniques used in isolation have limitations when it comes to developing a comprehensive platform for big data streams that are challenging due to their dynamicity, high frequency of unlabeled data from multiple sources, and  the traffic condition’s volatility over time.

Keyphrases: CNN, computer vision, deep learning, object detection, Smart Traffic Management, traffic flow management

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Md Masoom Anwar and Mohamed Salim and Md Anash},
  title = {Deep Learning Based Smart Traffic Management System},
  howpublished = {EasyChair Preprint no. 9634},

  year = {EasyChair, 2023}}
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