# Quantum Machine Learning in Transportation: A Case Study of Pedestrian Stress Modelling

arXiv: https://arxiv.org/abs/2507.01235 · HTML: https://mlsanity.com/paper/2507.01235/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Tree Tensor Networks for Generative Modeling](https://mlsanity.com/paper/1901.02217/) (2019)
- [Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces](https://mlsanity.com/paper/2009.00298/) (2020)
- [A supervised hybrid quantum machine learning solution to the emergency escape routing problem](https://mlsanity.com/paper/2307.15682/) (2023)
- [Data re-uploading in Quantum Machine Learning for time series: application to traffic forecasting](https://mlsanity.com/paper/2501.12776/) (2025)
- [Quantum Computing in Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2406.00862/) (2024)
- [Solving The Vehicle Routing Problem via Quantum Support Vector Machines](https://mlsanity.com/paper/2308.04849/) (2023)
- [Decoding Pedestrian Stress on Urban Streets using Electrodermal Activity Monitoring in Virtual Immersive Reality](https://mlsanity.com/paper/2408.11769/) (2024)
- [QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems](https://mlsanity.com/paper/2503.01916/) (2025)

## Abstract (arXiv)

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.