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 - 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
UR - http://www.scopus.com/inward/citedby.url?scp=85099657991&partnerID=8YFLogxK
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 -