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Effective long term travel time prediction with fuzzy rules for tollway

Journal Article


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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.

Authors


  •   Li, Ruimin (external author)
  •   Rose, Geoffrey (external author)
  •   Chen, Huaming (external author)
  •   Shen, Jun

Publication Date


  • 2018

Citation


  • Li, R., Rose, G., Chen, H. & Shen, J. (2018). Effective long term travel time prediction with fuzzy rules for tollway. Neural Computing and Applications, 30 (9), 2921-2933.

Scopus Eid


  • 2-s2.0-85013477422

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=7486&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6456

Has Global Citation Frequency


Number Of Pages


  • 12

Start Page


  • 2921

End Page


  • 2933

Volume


  • 30

Issue


  • 9

Place Of Publication


  • United Kingdom

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.

Authors


  •   Li, Ruimin (external author)
  •   Rose, Geoffrey (external author)
  •   Chen, Huaming (external author)
  •   Shen, Jun

Publication Date


  • 2018

Citation


  • Li, R., Rose, G., Chen, H. & Shen, J. (2018). Effective long term travel time prediction with fuzzy rules for tollway. Neural Computing and Applications, 30 (9), 2921-2933.

Scopus Eid


  • 2-s2.0-85013477422

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=7486&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6456

Has Global Citation Frequency


Number Of Pages


  • 12

Start Page


  • 2921

End Page


  • 2933

Volume


  • 30

Issue


  • 9

Place Of Publication


  • United Kingdom