Traffic breakdown occurs when the speed of traffic rapidly decreases from free-flow to low speeds. When a breakdown occurs, vehicles are forced to rapidly decelerate, leading to delays and safety hazards [1]. This speed reduction can extend to kilometers [2]. It is a critical issue in urban areas where high volumes of vehicles often lead to breakdowns, cause traffic delays, and increase emissions. Thus, it is crucial for transportation engineers and traffic system operators to understand the causes of traffic breakdown and develop strategies to manage and mitigate it.

Researchers have conducted studies and analysis on traffic breakdown events. By analyzing the traffic flow before breakdown events, researchers have constructed probabilistic models linking the traffic flow and the probability of the occurrence of a breakdown event. As the available traffic data becomes more abundant, researchers have discovered that traffic breakdown occurrences are related not only to traffic flow rate but also to other factors. However, previous probabilistic models usually considered only one variable. With the advancements in machine learning techniques in recent years, researchers have started using neural networks to predict traffic breakdown events and have achieved high accuracy in predicting their occurrences. Given the advantages of neural networks, the model can incorporate multiple variables considering temporal and spatially correlated data. Currently, no research considers the impact of traffic flow from ramps on breakdown occurrences using neural networks.

Previous studies have shown that the occurrence of traffic breakdowns is stochastic in nature ^{[3][4][5][6][7]}[3,4,5,6,7]. Furthermore, a breakdown event can occur at different flow levels rather than at a predetermined threshold value (i.e., capacity). In particular, the probability of a breakdown occurrence follows an ‘S’ shape as a function of traffic flow [7]. Parametric ^[5][8][5,8] and nonparametric ^[9][10][9,10] methods have been adopted to represent the survival rate based on pre-breakdown flow rates.

Due to the diversity in traffic design and traffic characteristics specific to each location, several methods have been used to identify breakdowns in previous research. First, a speed threshold was used to identify breakdown events in [6]. The threshold is determined for each study site based on geometric and traffic conditions. Dong and Mahmassani [5] defined the threshold speed as 10 mph below the free flow speed. Filipovska and Mahmassani [11] used a 20% threshold below the prevailing free flow speed. Second, traffic breakdowns were identified by sudden speed drops. For example, ref. [6] proposed using a speed drop of 6 miles per hour and below 45 miles per hour in consecutive time intervals of 5 min as a threshold to detect a breakdown event. The third method, known as the volume-occupancy correlation method, identifies breakdowns by requiring a correlation between traffic volume and occupancy that satisfies a minimum threshold over a sustainable period of time [12].

Once researchers have selected the breakdown identification method and gained access to additional data, they initiated the search for factors other than traffic volume that influence the occurrence of traffic breakdowns and found that in addition to flow rates, other factors, such as adverse weather conditions [13], incident [14] and merging behavior [15] have been found to influence probabilities of traffic breakdown. Maze et al. [13] investigated the relationship between weather, traffic density and capacity, and they found that in adverse weather conditions, drivers tend to increase their following distances and decrease their driving speeds, resulting in a decrease in throughput. This has also been supported in the research of Kamiska and Chalfen [16]. They highlighted the direct impact of vehicle spacing on speed by simulation. Specifically, when time headway increases from 1 s to 4 s, the average travel time will experience a 35.6% increase. By constructing a probability model of traffic breakdown, they found that, at the same levels of traffic flow, the probability of breakdown occurring on a rainy day is significantly higher than on a sunny day [4]. The other factor that has been shown to have an impact on breakdown events is incident. Wright et al. [14] analyzed the impact of traffic incidents on travel time reliability on freeways using historical incident data. The analysis revealed that an incident on the shoulders increases the probability of traffic breakdowns because they reduce the capacity of a freeway segment and generate a temporary bottleneck. Furthermore, in the merge segments of the freeways, the traffic condition is influenced by ramp traffic. Therefore, the merging behavior and the flow rate on the ramp can affect the characteristics of traffic breakdown such as the critical flow rate and the phenomenon of congestion [15].

Because the occurrence of traffic breakdown can be attributed to various factors, researchers have included multiple features in building breakdown prediction models. For example, speed and occupancy were used to construct a bivariate Weibull distribution to model the probability of breakdown [17]. In recent years, machine learning algorithms have been used to predict the occurrence of breakdowns and have achieved high accuracy ^[11][18][11,18]. In particular, Filipovska and Mahmassani [11] proposed a machine learning algorithm to predict the occurrence of traffic breakdowns considering spatial and temporal correlations in traffic data. 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. Zechin and Cybis [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. Lu 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%. Alqatawna 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][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. Bae et al. [23] compared traffic conditions before and after ramp meters were installed and demonstrated that hourly CO₂ 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, 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.