Can we Learn from Outliers? Unsupervised Optimization of Intelligent Vehicle Traffic Management Systems

Tom Mertens, Marwan Hassani

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

Vehicle traffic flow prediction is an essential task for several applications including city planning, traffic congestion management and smart traffic light control systems. However, recent solutions suffer in outlier situations where traffic flow becomes more challenging to predict. In this work, we address the problem of predicting traffic flow on different intersections in a traffic network under the realistic assumption of having outliers. Our framework, called OBIS, applies an existing LOF-based approach to detect outliers on each intersection in the network separately. Based on the spatio-temporal interdependencies of these outliers, we infer the correlations between intersections in the network. We use these outlier-based correlations then to improve the predictability of existing traffic flow prediction systems by selecting more relevant inputs for the prediction system. We show that our framework considerably improves the performance of LSTM-based models both under outlier scenarios and also under normal traffic. We test our framework under two real-life settings. In the first, we show how improving the predictability using our framework reduces the overall delays of vehicles on an intersection with a smart traffic light control system. In the second, we demonstrate how OBIS improves the predictability of a real dataset from four trajectories of intersections in the city of The Hague. We share the latter dataset together with an implementation of our framework.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part VI
EditorsMassih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
PublisherSpringer
Pages521-537
Number of pages17
ISBN (Electronic)978-3-031-26422-1
ISBN (Print)978-3-031-26421-4
DOIs
Publication statusPublished - 18 Mar 2023
Event2022 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022 - World Trade Center, Grenoble, France
Duration: 19 Sept 202223 Sept 2022
https://2022.ecmlpkdd.org/

Publication series

NameLecture Notes in Computer Science (LNCS)
Volume13718
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2022 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022
Abbreviated titleECML PKDD
Country/TerritoryFrance
CityGrenoble
Period19/09/2223/09/22
Internet address

Keywords

  • Correlations
  • Dimensionality reduction
  • Outlier detection
  • Traffic flow prediction

Fingerprint

Dive into the research topics of 'Can we Learn from Outliers? Unsupervised Optimization of Intelligent Vehicle Traffic Management Systems'. Together they form a unique fingerprint.

Cite this