Traffic&Transportation Journal
Sign In / Sign Up
SUBMIT
FOLLOW THE JOURNAL

Article

Short-Term Traffic Flow Uncertainty Prediction Based on Novel GM(1,1)
Xu Dong CAO, Qin SHI, Yi Kai CHEN, Chen Chen CHEN
Keywords:intelligent transportation systems, uncertainty quantification, novel GM model, smooth pre-processing, background value construction

Abstract

Anticipating uncertainty in short-term traffic flow is crucial for effective traffic management within intelligent transportation systems. Various methods for predicting uncertainty have been proposed and implemented. However, conventional techniques struggle to provide accurate forecasts when confronted with sparse data. Hence, this study focuses on developing an uncertainty prediction model for short-term traffic flow under limited data conditions. A novel grey model that considers the volatility of the traffic data is proposed, which extends the grey model (GM) by integrating two techniques: smooth pre-processing and background value construction. The performance of the proposed novel grey model is mainly illustrated by comparing the novel grey model with the traditional GM model. Our results, in terms of uncertainty quantification, demonstrate that the proposed model outperforms the GM model regarding mean kick-off percentage (KP), width interval (WI) and width amplitude.

References

[1] Shaygan M, et al. Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities. Transportation Research Part C: Emerging Technologies. 2022;145:103921. DOI:10.1016/J.TRC.2022.103921.

[2] Papageorgiou M, et al. Review of road traffic control strategies. Proceedings of the IEEE. 2004;91(12):2041–2042. DOI: 10.1109/JPROC.2003.819606.

[3] Medina-Salgado B, et al. Urban traffic flow prediction techniques: A review. Sustainable Computing: Informatics and Systems. 2022;35:100739. DOI: 10.1016/j.suscom.2022.100739.

[4] Deng JL. Control problems of grey systems. Systems & Control Letters. 1982;1(5):288–294. DOI: 10.1016/S0167-6911(82)80025-X.

[5] Chen CI, Hsin PH, Wu CS. Forecasting Taiwan’s major stock indices by the Nash nonlinear grey Bernoulli model. Expert Systems with Applications. 2010;37(12):7557–7562. DOI: 10.1016/j.eswa.2010.04.088.

[6] Wang Y, et al. A novel structure adaptive fractional derivative grey model and its application in energy consumption prediction. Energy. 2023;282:128380. DOI:

10.1016/J.ENERGY.2023.128380.

[7] Wu W, et al. Application of the novel fractional grey model FAGMO(1,1,k) to predict China’s nuclear energy consumption. Energy. 2018;165:223-234. DOI:

10.1016/J.ENERGY.2018.09.155.

[8] Duan H, Liu Y, Wang G. A novel dynamic time-delay grey model of energy prices and its application in crude oil price forecasting. Energy. 2022;251:123968. DOI:

10.1016/J.ENERGY.2022.123968.

[9] Duan H, Wang G. Partial differential grey model based on control matrix and its application in short-term traffic flow prediction. Applied Mathematical Modelling. 2023;116:763–785. DOI: 10.1016/J.APM.2022.12.012.

[10] Lippi M, Bertini M, Frasconi P. Short-term traffic flow forecasting: an experimental comparison of time-series analysis and supervised learning. IEEE Transactions on Intelligent Transportation Systems. 2013;14(2):871–82. DOI: 10.1109/TITS.2013.2247040.

[11] Kumar SV, Vanajakshi L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review. 2015;7(3):1–9. DOI: 10.1007/s12544-015-0170-8.

[12] Alghamdi T, et al. Forecasting traffic congestion using ARIMA modeling. 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). 2019. p. 1227–1232. DOI: 10.1109/IWCMC.2019.8766698.

[13] Vázquez JJ, et al. A comparison of deep learning methods for urban traffic forecasting using floating car data. Transportation Research Procedia. 2020;47:195–202. DOI:

10.1016/J.TRPRO.2020.03.079.

[14] Rajalakshmi V, Ganesh Vaidyanathan S. Hybrid time-series forecasting models for traffic flow prediction. Promet – Traffic&Transportation. 2022;34(4):537–549. DOI:

10.7307/ptt.v34i4.3998.

[15] Lee K, Rhee W. DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting. Transportation Research Part C: Emerging Technologies. 2022;145:103466. DOI:

10.1016/J.TRC.2021.103466.

[16] Ashley DJ. Uncertainty in the context of highway appraisal. Transportation. 1980;9(3):249–267. DOI: 10.1007/bf00153867.

[17] Brundell K. Sampling, specification and estimation as sources of inaccuracy in complex transport models-some examples analysed by Monte Carlo simulation and bootstrap, Proceedings of seminar F of the European Transport Conference 2000, 11–13 Sept.2000, Cambridge, UK. 2000. p. 225–237. http://worldcat.org/isbn/086050333X.

[18] Hugosson MB. Quantifying uncertainties in a national forecasting model. Transportation Research Part A: Policy and Practice. 2005;39(6):531–547. DOI: 10.1016/J.TRA.2005.02.010.

[19] Klir GJ. Uncertainty and information: Foundations of generalized information theory. New York, USA: John Wiley& Sons; 2006.

[20] Zhao Y, Kockelman KM. The propagation of uncertainty through travel demand models: An exploratory analysis. Annals of Regional Science. 2002;361:145–163. DOI:

10.1007/s001680200072.

[21] Rodier CJ, Johnston RA. Uncertain socioeconomic projections used in travel demand and emissions models: Could plausible errors result in air quality nonconformity? Transportation Research Part A: Policy and Practice. 2002;36(7):613–631. DOI: 10.1016/S0965-8564(01)00026-X.

[22] Boyce, AM, Bright, MJ. Reducing or managing the forecasting risk in privately-financed projects. Proceedings of the European Transport Conference 2003, 8–10 Oct.2003, Strasbourg, FRANCE. 2003. p. 19. http://worldcat.org/isbn/0860503429.

[23] Armoogum J. Madre JL, Bussiere Y. Measuring the impact of uncertainty in travel demand modelling with a demographic approach. IATSS Research. 2009;33(2):9–20. DOI:

10.1016/S0386-1112(14)60241-7.

[24] Yang M, Liu Y, You Z. The reliability of travel time forecasting. IEEE Transactions on Intelligent Transportation Systems. 2010;11(1):162–171. DOI: 10.1109/TITS.2009.2037136.

[25] Liu S, Lin Y. Grey information: Theory and practical applications. London, UK: Springer London; 2006. DOI: 10.1007/1-84628-342-6.

[26] Huang YL, Lin CT. Developing an interval forecasting method to predict undulated demand. Quality & Quantity. 2011;45:513–524. DOI: 10.1007/s11135-010-9317-9.

[27] Chen YY, Liu HT, Hsieh HL. Time series interval forecast using GM(1,1) and NGBM(1, 1) models. Soft Computing. 2019;23(5):1541–1555. DOI: 10.1007/s00500-017-2876-0.
Published
20.06.2024
Copyright (c) 2023 Xu Dong CAO, Qin SHI, Yi Kai CHEN, Chen Chen CHEN

Published by
University of Zagreb, Faculty of Transport and Traffic Sciences
Online ISSN
1848-4069
Print ISSN
0353-5320
SCImago Journal & Country Rank
Publons logo
© Traffic&Transportation Journal