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Travel-Time Prediction Methods: A Review

  • Mengting Bai
  • Yangxin Lin
  • Meng Ma
  • Ping Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11344)

Abstract

Near-future Travel-time information is helpful to implement Intelligent Transportation Systems (ITS). Travel-time prediction refers to predicting future travel-time. Researchers have developed various methods to predict travel-time in the past decades. This paper conducts a review focusing on literatures, including techniques proposed recently. These methods are categorized as model-based and data-driven methods. We elaborate two common model-based methods, namely queuing theory and cell transmission model. Data-driven methods are categorized as parametric models (linear regression, autoregressive integrated moving average model and Kalman filter) and non-parametric models (neural network, support vector regression, nearest neighbors and ensemble learning). These methods are compared from data, prediction range and accuracy. In addition, we discuss several solutions to overcome shortcomings of existing methods, and highlight significant future research challenges.

Keywords

Travel-time prediction Model-based Data-driven Parametric Non-parametric 

Notes

Acknowledgement

This work is supported in part by National Key R&D Program of China No. 2017YFB1200700 and National Natural Science Foundation of China No. 61701007.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Software and MicroelectronicsPeking UniversityBeijingChina
  2. 2.National Engineering Research Center for Software EngineeringPeking UniversityBeijingChina
  3. 3.Key Laboratory of High Confidence Software Technologies (PKU)Ministry of EducationBeijingChina

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