Time-to-Green predictions: A framework to enhance SPaT messages using machine learning

Alexander Genser, Lukas Ambühl, Kaidi Yang, Monica Menendez, Anastasios Kouvelas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, efforts were made to standardize Signal Phase and Timing (SPaT) messages. Such messages contain the current signal phase with a prediction for the corresponding residual time for all approaches of a signalized intersection. Hence, the information can be utilized for the motion planning of human-driven/autonomously operated individual or public transport vehicles. Consequently, this leads to a more homogeneous traffic flow and a smoother speed profile. Unfortunately, adaptive signal control systems make it difficult to predict the SPaT information accurately. In this paper, we propose a novel machine learning approach to forecast the time series of residual times. A prediction framework that utilizes a Random Survival Forest (RSF) and a Long-Short-Term-Memory (LSTM) neural network is implemented. The machine learning models are compared to a Linear Regression (LR) model. For a proof of concept, the models are applied to a case study in the city of Zurich. Results show that the machine learning models outperform the LR approach, and in particular, the LSTM neural network is a promising tool for the enhancement of SPaT messages.

Original languageEnglish (US)
Title of host publication2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728141497
DOIs
StatePublished - Sep 20 2020
Event23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Rhodes, Greece
Duration: Sep 20 2020Sep 23 2020

Publication series

Name2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020

Conference

Conference23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
CountryGreece
CityRhodes
Period9/20/209/23/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Information Systems and Management
  • Modeling and Simulation
  • Education

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