My Account | Careers | Downloads | Blog

PROJECT TITLE :

A Multitask Learning Model for Traffic Flow and Speed Forecasting

ABSTRACT:

Accurate short-term traffic state forecasting is beneficial to Intelligent Transportation Systems (ITS) research and applications.

This research offers a Deep Learning-based multitask learning Gated Recurrent Units (MTL-GRU) with residual mappings to improve forecasting accuracy. Feature engineering is used to identify the most informative features for forecasting in order to improve the MTL-performance.

GRU's The MTL-GRU can then estimate traffic flow and speed simultaneously, and performs better than other analogues, according to numerical results based on real-world datasets. Experiments also show that the MTL-GRU model, which is based on Deep Learning, can overcome the bottleneck created by larger training datasets while still gaining benefits.

The findings indicate that the suggested MTL-GRU model with residual mappings is capable of forecasting short-term traffic conditions.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Prediction of Traffic Flow via Connnected Vehicles ABSTRACT: We propose a framework for short-term traffic flow prediction (STP) so that transportation authorities can take early actions to control flow and prevent
PROJECT TITLE : Graph Attention Spatial-Temporal Network with Collaborative Global-Local Learning for Citywide Mobile Traffic Prediction ABSTRACT: It is becoming increasingly important for proactive network service provisioning
PROJECT TITLE : Spatio-Temporal Meta Learning for Urban Traffic Prediction ABSTRACT: It is very difficult to predict urban traffic because of three factors: 1) the complex spatio-temporal correlations of urban traffic, which include
PROJECT TITLE : Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting ABSTRACT: It is essential to have accurate traffic forecasting in order to improve the safety, stability, and overall effectiveness
PROJECT TITLE : Foresee Urban Sparse Traffic Accidents: A Spatiotemporal Multi-Granularity Perspective ABSTRACT: Due to the rapid pace of urbanization, car accidents have evolved into a significant threat to both health and development.

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry