# A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency
## 1 Introduction  
With the development of the economy, there are more and more vehicles in the urban road network. Traffic decongestion is increasingly becoming a daily challenge for urban traffic management. Especially in big cities where there are more people and cars, the road conditions are more complicated and there are often temporary emergencies (e.g., traffic accidents, temporary road control, and large events such as concerts and exhibitions). If the manager is unable to rapidly guide the vehicle to modify the route to guide the traffic to achieve a balanced state, it will make the local congestion continue to spread, thus affecting the smooth flow of the large area of the road network. Such temporary emergencies are unpredictable, accidental, and irregular. After an emergency, it is crucial for the system to be able to calculate and adjust the travelers' planned routes in time and guide the transportation system to rapidly return to the original traffic equilibrium [1]. Traffic equilibrium means that no traveler in a transportation system can unilaterally reduce his travel impedance by changing his path [2]. Existing traffic forecasting and route planning methods make it difficult to meet the actual needs of prediction accuracy and decision-making efficiency in such emergency scenarios. Firstly, the impact of such emergencies on the traffic state is affected by the local road network structure and the activities of the surrounding travelers, which makes it difficult to devise a generalized method for prediction. Traffic equilibrium is one of the main research objectives in traffic assignment modeling and route planning, which has already produced more results [3; 4; 5]. For example, the static traffic assignment (STA) typically assigns the current real-time origin-destination data (OD) used, such as Logit probabilistic stochastic methods [6; 7; 8; 9], to estimate the road impedance, which can quantify the impact of real-time OD on the road network. However, the traffic on the entire road network changes dynamically. Such static methods are difficult to adapt to the real road network at the time of emergencies. In addition, in reality, information on accurate and dynamic road network traffic flow is usually difficult to obtain in real time. Existing dynamic traffic assignment (DTA) studies usually depict the dynamic distribution of traffic flows based on linear or constant different metrics [10; 11; 12; 13], optimization objectives [14], or probabilistic approaches [15]. But because many factors are difficult to quantify in emergencies, it is difficult to describe the complex and variable dynamic traffic flow distribution relying only on simple variables or probability distributions. Therefore, route planning after an emergency should consider not only real-time traveler OD information (referred to as micro-information), but also real-time and future information about the overall background traffic flow (referred to as macro-information). We concluded that it is difficult to consider either micro or macro transportation information in isolation to achieve accurate prediction and planning after an emergency event. Second, in terms of model performance, while existing flow allocation models are well structured and reasonably well explained, they often imply a large number of variables and constraints. The solution algorithms are often computed iteratively by constantly choosing the optimal path. Only after continuous adjustment within the system can the algorithm reach an equilibrium state. Due to the excessive number of iterations, the computation time of these algorithms is too long, and the ability of real-time planning and decision-making is greatly reduced. And the re-planning of travelers' routes in emergency scenarios requires high algorithm performance for real-time decision-making. The traditional iteration-based approach is difficult to adapt to this rapid planning and decision-making requirement.