research-article

Assessment of ARIMA-based prediction techniques for road-traffic volume

Authors:

Vinay B. Gavirangaswamy,

Gagan Gupta,

Ajay Gupta,

Rajeev AgrawalAuthors Info & Claims

MEDES '13: Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems

Pages 246 - 251

https://doi.org/10.1145/2536146.2536176

Published: 28 October 2013 Publication History

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Abstract

Studies related to public transportation systems help the commuting public by increasing road safety and circulation. These result in optimized traffic flow, shorter origin-destination travel time and reduced incident rate. Vehicular Ad-Hoc Network uses a number of sensors to gather data on the road. Intelligent Transportation Systems (ITS) draw inference from the gathered data. In this paper we discuss our experience of using Auto Regressive Integrated Moving Average (ARIMA) based techniques emphasizing on the integration of short-range and long-range dependencies of the historical traffic volume. We also analyze traffic data for patterns across different types of roads and derive computational complexity of ARIMA. Finally, improvements are identified for better prediction. We empirically show that SARIMA and ARIMA-GARCH exhibit similar road traffic prediction. ARIMA-GARCH is better than ARIMA and SARIMA for prediction, with stable model order across different historical traffic volumes. We further analyzes model orders across different types of roads and historical traffic volume; and its implications for practical applicability in ITS.

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Gan TXu BLi J(2023)Transportation Flow Prediction Based on Graph Attention Echo State NetworkProceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things10.1145/3603781.3603907(708-713)Online publication date: 26-May-2023
https://dl.acm.org/doi/10.1145/3603781.3603907
Kumar ASunitha R(2023)MuSeFFF: Multi-Stage Feature Fusion Framework for Traffic PredictionIntelligent Systems with Applications10.1016/j.iswa.2023.200227(200227)Online publication date: Apr-2023
https://doi.org/10.1016/j.iswa.2023.200227
Wang SZhang YFu ETang S(2022)Multiscale Backcast Convolution Neural Network for Traffic Flow Prediction in The Frequency DomainApplied Sciences10.3390/app12231191212:23(11912)Online publication date: 22-Nov-2022
https://doi.org/10.3390/app122311912
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Published In

MEDES '13: Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems

October 2013

358 pages

ISBN:9781450320047

DOI:10.1145/2536146

Conference Chairs:
Latif Ladid
University of Luxembourg Interdisciplinary Center for Security, Luxembourg
,
Antonio Montes
Centro de Tecnologia da Informação Renato Archer, Brazil
,
General Chair:
Peter A. Bruck
Research Studios Austria Forschungsgesellschaft mbH, Austria
,
Program Chairs:
Fernando Ferri
CNR, Italy
,
Richard Chbeir
UPPA University, France

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Association for Computing Machinery

New York, NY, United States

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Published: 28 October 2013

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MEDES '13: Fifth International Conference on Management of Emergent Digital EcoSystems

October 28 - 31, 2013

Luxembourg, Luxembourg

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MEDES '13 Paper Acceptance Rate 56 of 122 submissions, 46%;

Overall Acceptance Rate 267 of 682 submissions, 39%

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Gan TXu BLi J(2023)Transportation Flow Prediction Based on Graph Attention Echo State NetworkProceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things10.1145/3603781.3603907(708-713)Online publication date: 26-May-2023
https://dl.acm.org/doi/10.1145/3603781.3603907
Kumar ASunitha R(2023)MuSeFFF: Multi-Stage Feature Fusion Framework for Traffic PredictionIntelligent Systems with Applications10.1016/j.iswa.2023.200227(200227)Online publication date: Apr-2023
https://doi.org/10.1016/j.iswa.2023.200227
Wang SZhang YFu ETang S(2022)Multiscale Backcast Convolution Neural Network for Traffic Flow Prediction in The Frequency DomainApplied Sciences10.3390/app12231191212:23(11912)Online publication date: 22-Nov-2022
https://doi.org/10.3390/app122311912
Luo ZWang XYang Z(2022)An Improved Recursive ARIMA Method with Recurrent Process for Remaining Useful Life Estimation of BearingsShock and Vibration10.1155/2022/90104192022(1-16)Online publication date: 21-Feb-2022
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Goudarzi SKama MAnisi MSoleymani SDoctor F(2018)Self-Organizing Traffic Flow Prediction with an Optimized Deep Belief Network for Internet of VehiclesSensors10.3390/s1810345918:10(3459)Online publication date: 15-Oct-2018
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A Study of the USDX Predication Based on ARIMA and GARCH Models

Forecasting Performance Measures for Traffic Safety Using Deterministic and Stochastic Models

A fuzzy seasonal ARIMA model for forecasting

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A Study of the USDX Predication Based on ARIMA and GARCH Models

Forecasting Performance Measures for Traffic Safety Using Deterministic and Stochastic Models

A fuzzy seasonal ARIMA model for forecasting

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