# Risk Propagation Mechanism and Prediction Model for the Highway Merging Area
###### Abstract  
The merging area is one of the most accident-prone areas on highways. After an accident occurs, the risk will propagate along the main road over a certain range and time. Therefore, the study of the propagation mechanism of accident risk will help to quantify the driving risk in this region. An effective risk prediction model is important for improving traffic control measures in this specific area. In this study, simulation experiments were conducted in SUMO (Simulation of Urban Mobility) to obtain the accident and risk propagation data in merging areas. Firstly, the Gaussian plume model was optimized for the merging area situation to determine and divide the impact range of the accidents. Then, different accident scenarios in the merging area and downstream were simulated with different input flow rates to study the time and speed of risk propagation in the three-level affected areas. Finally, LSTM (long short-term memory) and RNN (recurrent neural network) models were built to predict the accident risk in the merging area. The results showed that the LSTM model had higher accuracy. This study provides an innovative insight into the propagation process of merging area accidents. It is of benefit to the development of post-accident control measures. traffic accidents; merging area; risk propagation; risk prediction; LSTM +
Footnote †: journal: 8 July 2023  
the cumulative arrival-departure graph by describing the number of queued vehicles as a function of time. They determined the impact range of upstream congestion on bottleneck sections. However, the model was only suitable for studying unsaturated situations. [PERSON] [5] provided a spatial impact calculation method for highway accidents based on basic traffic flow analysis principles. The method considered the imbalance between traffic demand and supply as the cause of traffic accidents. [PERSON] et al. [6] predicted the duration of accidents on highways using an integrated accident online management tool based on rigorous statistics and queuing models. [PERSON] et al. [7] used the BIP algorithm to quantify the delay caused by highway accidents. They identified factors that caused such delays by predicting the reliability of random accident events with circular detectors. [PERSON] et al. [8] proposed a prediction method for the spatial impact propagation of traffic events on highways. They improved the application method of the traditional traffic flow fluctuation theory by quantifying the traffic flow operation characteristics. [PERSON] et al. [9] developed a method for predicting the range of accident impact by determining the traffic flow redistribution on surrounding diversion routes. They used the inverse calculation of the OD matrix and estimated the total travel time under induced conditions to provide guidance for traffic diversion measures. [PERSON] et al. [10] established a prediction model for the impact range of sudden events by using the index of the variation index and deviation of the total travel time of vehicles in the region as a criterion. [PERSON] et al. [11] calculated the traffic wave speed and queue length to study the diffusion range of traffic accidents in different time periods. [PERSON] et al. [12] established an estimate model for the radiation range of traffic flow under accident conditions based on TransModeler simulation. [PERSON] [13] constructed a staged model to predict the duration of highway traffic accidents. They extensively used historical traffic data to predict the time of accident reporting and handling. [PERSON] et al. [14] predicted the scope of accident impact based on the delay time of the accident-generated queue length and the recovery time of the queue length. They proposed several post-accident traffic organization measures. [PERSON] et al. [15] quantitatively determined the accident impact area in three levels (point, line, and surface) based on maintenance requirements, traffic flow theory, and travel times.