Traffic Flow Prediction and Application of Smart City Based on Industry 4.0 and Big Data Analysis

EasyChair Preprint 9479

Abstract

The prediction of traffic accidents is important for improving transportation safety as well as route safety. The problem is also difficult because the causes are complex and differing such as mechanical problems of the vehicle, negligence of the driver and even the factors that can cause traffic accidents vary from place to place. For example, the main factors leading to traffic accidents in an urban region with busy local roads may be very different from those in a rural expressway, and accidents are rare events. It is difficult to accurately predict individual accidents due to the lack of sufficient samples. In this paper, we perform an in-depth study of the problem of traffic accident prediction using the Convolutional Long ShortTerm Memory (ConvLSTM) neural network model. A number of detailed features such as weather, environment, road conditions and traffic volume

Keyphrases: Smart, Traffic Accident Prediction, city, deep learning

@booklet{EasyChair:9479,
  author    = {Wiam El Bouchti},
  title     = {Traffic Flow Prediction and Application of Smart City Based on Industry 4.0 and Big Data Analysis},
  howpublished = {EasyChair Preprint 9479},
  year      = {EasyChair, 2022}}