- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
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. Show more
Book title2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
Pages / Article No.
Organisational unit08686 - Gruppe Strassenverkehrstechnik
02655 - Netzwerk Stadt und Landschaft D-ARCH
NotesConference lecture held on September 22, 2020. Due to the Corona virus (COVID-19) the conference was conducted virtually.
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