Research on traffic travel time prediction based on machine learning

DOI

10.2991/978-94-6463-102-9_92How to use a DOI?

Keywords

Travel time; Regression algorithm; Random forest

Abstract

With the rapid development of economy, the pressure of traffic operation is increasing, and the improvement of intelligent transportation system is more urgent. In this paper, we focus our research on the topic of travel time prediction based on machine learning, and use the international data mining field event dataset as the research sample. First, the data are initially analyzed to extract the feature variables; then the prediction of travel time is further performed by using linear regression, ridge regression and random forest regression to compare the evaluation results. Based on the data test results, it is found that linear regression can achieve better prediction effect than ridge regression and random forest in the traffic data environment of highway. It can be seen that in the intelligent transportation system, although the complex model can improve the prediction accuracy, it does not necessarily achieve better prediction effect, which provides a reference basis for the selection of driving time prediction method in the transportation system.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - PeiTing Zhang
PY  - 2022
DA  - 2022/12/29
TI  - Research on traffic travel time prediction based on machine learning
BT  - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)
PB  - Atlantis Press
SP  - 893
EP  - 898
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-102-9_92
DO  - 10.2991/978-94-6463-102-9_92
ID  - Zhang2022
ER  -