# Prediction of Freeway Traffic Breakdown Using Artificial Neural Networks
## 2 Related Work
They showed that the machine learning method outperformed the probabilistic models in terms of short-term breakdown prediction with an accuracy of 98% from the machine learning method, compared to 65% accuracy from the probabilistic model. [PERSON] and [PERSON] [18] predicted the occurrence of traffic breakdown by building a neural network to forecast speed. Then they used the Bayesian approximation to compute the probability of the breakdown event. Their model has an accuracy of 89% in predicting the occurrence of breakdowns and can evaluate the uncertainty of the predictions. Compared to traditional probabilistic models, the advantage of machine learning lies in its ability to accommodate more features and assess the impact of each feature on model performance through self-learning. Machine learning algorithms have been used in various aspects of the field of transportation engineering. [PERSON] et al. [19] combined the autoregressive integral moving average (ARIMA) and long-short-term memory (LSTM) neural network to predict the short-term traffic flow rate and achieve the average test error at 6.5%. [PERSON] et al. [20] used ANN to estimate traffic accident frequencies and have shown that the neural network can provide results close to the true value. Although a machine learning algorithm has been used to predict the occurrence of traffic breakdowns (e.g., [11]), previous research did not consider the traffic condition of the on-ramps when building breakdown prediction models. On-ramp traffic has been shown to have an impact on mainline traffic flow, especially when the inflow rate of ramps is high. Additionally, merging traffic tends to increase the probability of traffic breakdowns [15; 21]. Consequently, dynamic ramp meters generally use the traffic density of the mainline as a trigger threshold to ensure that the density of the mainline remains within an acceptable range [22]. By stabilizing traffic flow, ramp meters reduce emissions. [PERSON] et al. [23] compared traffic conditions before and after ramp meters were installed and demonstrated that hourly CO\({}_{2}\) can have 7.3% reduction. However, due to the stochastic nature of traffic breakdown, the existing ramp meter design cannot account for the probability of a breakdown. Therefore, in this paper, neural networks were used to predict the probability of breakdown events considering the spatial and temporal characteristics of the traffic conditions of upstream segments, downstream segments, and on-ramps. By incorporating ramp flows into the breakdown prediction model, the proposed approach sheds light on the new ramp meter design aimed at reducing the probability of flow breakdown on the mainline. The remainder of the paper is organized as follows. The next section presents the data description and traffic breakdown identification method, followed by the methodologies of feature selection, machine learning model, hyperparameter, and performance measurements. Then, a discussion of the results of model performance is presented. The final section discusses concluding remarks and future research.