  The efficiency and reliability of real-time incident detection models
directly impact the affected corridors' traffic safety and operational
conditions. The recent emergence of cloud-based quantum computing
infrastructure and innovations in noisy intermediate-scale quantum devices have
revealed a new era of quantum-enhanced algorithms that can be leveraged to
improve real-time incident detection accuracy. In this research, a hybrid
machine learning model, which includes classical and quantum machine learning
(ML) models, is developed to identify incidents using the connected vehicle
(CV) data. The incident detection performance of the hybrid model is evaluated
against baseline classical ML models. The framework is evaluated using data
from a microsimulation tool for different incident scenarios. The results
indicate that a hybrid neural network containing a 4-qubit quantum layer
outperforms all other baseline models when there is a lack of training data. We
have created three datasets; DS-1 with sufficient training data, and DS-2 and
DS-3 with insufficient training data. The hybrid model achieves a recall of
98.9%, 98.3%, and 96.6% for DS-1, DS-2, and DS-3, respectively. For DS-2 and
DS-3, the average improvement in F2-score (measures model's performance to
correctly identify incidents) achieved by the hybrid model is 1.9% and 7.8%,
respectively, compared to the classical models. It shows that with insufficient
data, which may be common for CVs, the hybrid ML model will perform better than
the classical models. With the continuing improvements of quantum computing
infrastructure, the quantum ML models could be a promising alternative for
CV-related applications when the available data is insufficient.
