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Effective long-term travel time prediction with fuzzy rules for tollway

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Abstract

Advanced traveller information system is an important intelligent transportation systems application area, which provides information to transport users and managers in order to improve the efficiency and effectiveness of the transportation system, in the face of increasing congestion in urban cities around the world. So far very limited research attention has been focused on long-term travel time prediction (i.e. predicting greater than 60 min ahead). Long-term travel time forecasts can play a critical role in journey planning decisions for both private road users and logistics operators. In this paper, we have considered a fuzzy neural network incorporated with both imprecise and numerical information and developed a hybrid long-term travel time prediction model, which shows the better prediction capability than naive methods and highlights the importance of different data variables. The model combines the learning ability of neural networks and the knowledge extraction ability of fuzzy inference systems. The model was validated by using travel time data compiled from electronic toll tags on a 14 km length section of the CityLink tollway in Melbourne, Australia. The validation results highlight the ability of the fuzzy neural network model to accommodate imprecise and linguistic input information, while providing reliable predictions of travel times up to a few days ahead.

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Author information

Author notes

  1. Jun Shen

    Present address: University of Wollongong, Northfields Ave, Wollongong, NSW, 2522, Australia

Authors and Affiliations

  1. Bureau of Transport Statistics, Transport for NSW, Sydney, NSW, 2000, Australia

    Ruimin Li

  2. Institute of Transport Studies, Department of Civil Engineering, Monash University, Clayton, VIC, 3800, Australia

    Geoffrey Rose

  3. School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, 2500, Australia

    Huaming Chen & Jun Shen

Authors
  1. Ruimin Li
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  2. Geoffrey Rose
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  3. Huaming Chen
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  4. Jun Shen
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Corresponding author

Correspondence to Jun Shen.

Glossary

ITS

Intelligent transportation systems

AVI

Automatic vehicle identification

EFuNN

Evolving fuzzy neural network

GPS

Global positioning system

BTM

Burnley to Moreland

MTB

Moreland to Burnley

ANNs

Artificial neural networks

ME

Mean error

MRE

Mean relative error

MAE

Mean absolute error

MARE

Mean absolute relative error

Fuzzy sets

Very small (VS), small (S), medium (M), large (L), very large (VL)

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Li, R., Rose, G., Chen, H. et al. Effective long-term travel time prediction with fuzzy rules for tollway. Neural Comput & Applic 30, 2921–2933 (2018). https://doi.org/10.1007/s00521-017-2899-6

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  • DOI: https://doi.org/10.1007/s00521-017-2899-6

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