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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
01.09.2017
DOI
https://doi.org/10.7307/ptt.v29i4.2279
ISSUE
2017 (Vol 29), Issue 4
LICENSE
Copyright (c) 2024 Li Linchao, Tomislav Fratrović, Zhang Jian, Ran Bin
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm

Authors:

Li Linchao
Southeast University

Tomislav Fratrović
University of Zagreb

Zhang Jian
Southeast University

Ran Bin
Schoocl of transportation

Keywords:highway congestion, traffic state, sensor data, speed prediction, incident, symbolic regression, genetic programming

Abstract

Due to the increase of congestion on highways, providing real-time information about the traffic state has become a crucial issue. Hence, it is the aim of this research to build an accurate traffic speed prediction model using symbolic regression to generate significant information for travellers. It is built based on genetic programming using Pareto front technique. With real world data from microwave sensor, the performance of the proposed model is compared with two other widely used models. The results indicate that the symbolic regression is the most accurate among these models. Especially, after an incident occurs, the performance of the proposed model is still the best which means it is robust and suitable to predict traffic state of highway under different conditions.

References

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How to Cite
Linchao, L. (et al.) 2017. Traffic Speed Prediction for Highway Operations Based on a Symbolic Regression Algorithm. Traffic&Transportation Journal. 29, 4 (Sep. 2017), 433-441. DOI: https://doi.org/10.7307/ptt.v29i4.2279.

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