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Time Series Traffic Flow Prediction with Hyper-Parameter Optimized ARIMA Models for Intelligent Transportation System

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Title Time Series Traffic Flow Prediction with Hyper-Parameter Optimized ARIMA Models for Intelligent Transportation System
 
Creator Kumar B, Praveen
K, Hariharan
 
Subject ARIMA
Forecast
Grid search
Road transport
Time series prediction
 
Description 408-415
Intelligent Transportation System (ITS) has become the need of the day to manage heavy traffic problems due to the
exponential growth of road transportation. This is also very much essential for building the smart cities and to improve the
comfort of the vehicle drivers. The electric and autonomous vehicles are going to be the future transport systems for which
we need an intelligent traffic management system. This requires a lot of growth in infrastructure. The integration of
technologies such as Sensors, Internet of Things (IoT), Cloud Computing, etc. has to be done for this. The traffic prediction
is one of thekey requirement for establishing the ITS. In this paper we present our study on ARIMA model with optimized
hyper-parameter using grid search technique for traffic flow predictions. The model validation is done on the whole day
traffic flow, morning and evening peak time traffic flow datasets. The prediction results show good performance metrics
with RMSE of 8.953, 11.007 and 11.837 for those three datasets.
 
Date 2022-04-05T11:34:04Z
2022-04-05T11:34:04Z
2022-04
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/59432
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.81(04) [April 2022]