Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network

Kranti Kumar · Manoranjan Parida · Vinod Kumar KATIYAR

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Traffic Prediction and Management Techniques Traffic control and management Hydrological Forecasting Using AI

Abstract

This study applies Artificial Neural Network (ANN) for short term prediction of traffic flow using past traffic data. The model incorporates traffic volume, speed, density, time and day of week as input variables. Speed of each category of vehicles was considered separately as input variables in contrast to previous studies reported in literature which consider average speed of combined traffic flow. Results show that Artificial Neural Network has consistent performance even if time interval for traffic flow prediction was increased from 5 minutes to 15 minutes and produced good results even though speeds of each category of vehicles were considered separately as input variables.

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References

1998Forecasting with artificial neural networks:2005Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach 2004Short‐term traffic forecasting: Overview of objectives and methods 2006Short-Term Freeway Traffic Flow Prediction: Bayesian Combined Neural Network Approach 2011Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm 2001An object-oriented neural network approach to short-term traffic forecasting 2005Dynamic Wavelet Neural Network Model for Traffic Flow Forecasting 2005Urban transport crisis in India

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2020Long-Term Traffic Prediction Based on LSTM Encoder-Decoder Architecture

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