TY - GEN
T1 - Time-to-Green predictions
T2 - 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
AU - Genser, Alexander
AU - Ambühl, Lukas
AU - Yang, Kaidi
AU - Menendez, Monica
AU - Kouvelas, Anastasios
N1 - Funding Information:
The authors would like to thank Felix Denzler from the city of Zurich for collaborating and to provide the data. M. Menendez acknowledges the support by the NYUAD Center for Interacting Urban Networks (CITIES) funded by Tamkeen under the NYUAD Research Institute Award CG001, and by the Swiss Re Institute under the Quantum CitiesTM initiative. L. Ambühl acknowledges the support by ETH Research Grant ETH-27 16-1 under the name of SPEED.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/20
Y1 - 2020/9/20
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85099657991&partnerID=8YFLogxK
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U2 - 10.1109/ITSC45102.2020.9294548
DO - 10.1109/ITSC45102.2020.9294548
M3 - Conference contribution
AN - SCOPUS:85099657991
T3 - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
BT - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 September 2020 through 23 September 2020
ER -