International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

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Research Paper | Information Technology | India | Volume 11 Issue 10, October 2022

Traffic Control and Management System for Smart City Using Geo-Social Networks

M. R Sumalatha | Lakshmi Harika Palivela | Srimathi Ravisankar | Kanimozhi Mahendran | Srilakshmi A

Abstract: Traffic prediction is vital in smart city traffic management and control system because of the ever-changing nature of the Traffic. Route planning and smart city traffic congestion reduction benefit from long-term traffic predictions. This challenge is difficult to solve in long-term traffic forecasting because of the complex and dynamic spatio-temporal interactions between various components of the road network. Traffic forecasting in smart cities is necessary for traffic management and public safety since it is impacted by a range of factors such inter-regional travel, activities such as social events, tolls, and VIP gatherings and weather. This novel model proposes a Spatio-Temporal Residual Neural Network model with External Fusion for Traffic Prediction System to forecast inflow and outflow, to handle temporal and spatial dependency, and to model the temporal properties and proposed a Graph Encoder and Decoder and Attention layer is introduced between the encoder and decoder. To decrease error propagation, transformation attention layer is inserted between the encoder and decoder. Traffic Monitoring system is modelled with SARIMA model to monitor and handle the seasonality in data. The Traffic congestion control and management is developed using Angular Fire and Firebase Database in which Location, Intensity of Traffic, Traffic Type is inputted and alerted to the drivers which will help them plan the route in advance and aid them prevent last minute hassle and the events is classified occurring before the accident and predict the traffic event and intensity by which will cause the traffic and prevents traffic jam. Users may extend their assistance by providing any updates they get in their surroundings, which will aid peer travelers in accommodating traffic and predicting if severe traffic jams will occur in the near future based on traffic incidents.

Keywords: Traffic Prediction, Smart City Traffic Management, Data Analytics, Geo-social Networks

Edition: Volume 11 Issue 10, October 2022,

Pages: 1127 - 1146

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