- author = "Aniko Ekart and Alina Patelli and Victoria Lush and Elisabeth Ilie-Zudor",
- title = "Genetic Programming with Transfer Learning for Urban Traffic Modelling and Prediction",
- booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020",
- year = "2020",
- editor = "Yaochu Jin",
- pages = "paper id24137",
- address = "internet",
- month = "19-24 " # jul,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, ITS, Transfer Learning, Symbolic Regression, Intelligent Transportation, Traffic Prediction",
- isbn13 = "978-1-7281-6929-3",
-
URL = "
https://eprints.sztaki.hu/10019/",
-
DOI = "
doi:10.1109/CEC48606.2020.9185880",
- size = "8 pages",
- abstract = "Intelligent transportation is a cornerstone of smart cities' infrastructure. Its practical realisation has been attempted by various technological means (ranging from machine learning to evolutionary approaches), all aimed at informing urban decision making (e.g., road layout design), in environmentally and financially sustainable ways. In this paper, we focus on traffic modelling and prediction, both central to intelligent transportation. We formulate this challenge as a symbolic regression problem and solve it using Genetic Programming, which we enhance with a lag operator and transfer learning. The resulting algorithm uses knowledge collected from other road segments in order to predict vehicle flow through a junction where traffic data are not available. The experimental results obtained on the Darmstadt case study show that our approach is successful at producing accurate models without increasing training time.",
-
notes = "Motor road traffic. Induction loop embbeded under road
surfaces at junctions, missing data due to sensor
faults. Darmstadt Germany.
https://darmstadt.ui-traffic.de/faces/TrafficData.xhtml
GENTLE python gplearn. {"}memory mechanism{"} transfer
learning between GP runs? 15 or 30 generations, GP run
time ~2 minutes.
Transfer Learning as a way to cope with missing data.
https://wcci2020.org/
Aston University, United Kingdom; Hungarian Academy of Sciences, Hungary",
