Quantum Machine Learning in Transportation: A Case Study of Pedestrian Stress Modelling
This work addresses pedestrian stress modeling for transportation safety, but it is incremental as it applies existing quantum methods to a new dataset with modest gains.
The paper tackled modeling pedestrian stress from skin conductance responses using quantum machine learning, achieving a test accuracy of 55% with a Quantum Neural Network, which outperformed a Quantum Support Vector Machine at 45%.
Quantum computing has opened new opportunities to tackle complex machine learning tasks, for instance, high-dimensional data representations commonly required in intelligent transportation systems. We explore quantum machine learning to model complex skin conductance response (SCR) events that reflect pedestrian stress in a virtual reality road crossing experiment. For this purpose, Quantum Support Vector Machine (QSVM) with an eight-qubit ZZ feature map and a Quantum Neural Network (QNN) using a Tree Tensor Network ansatz and an eight-qubit ZZ feature map, were developed on Pennylane. The dataset consists of SCR measurements along with features such as the response amplitude and elapsed time, which have been categorized into amplitude-based classes. The QSVM achieved good training accuracy, but had an overfitting problem, showing a low test accuracy of 45% and therefore impacting the reliability of the classification model. The QNN model reached a higher test accuracy of 55%, making it a better classification model than the QSVM and the classic versions.