## 1 Introduction  
The vehicles have increased a lot with the improvement of people's living standards. Traffic congestion will be inevitable when the number of vehicles exceeds the load capacities or traffic accidents. Traffic congestion is even more unacceptable in urban traffic networks. Not only descend or stop the speeds but also waste time, fuel, and increase air pollution. Therefore, many strategies or policies are proposed for the big problem. The paper [1] proposed an internet of vehicles (IOV) that combines advanced electronics and communication technology to form intelligent transportation systems. There are five different taxonomies of routing protocols presented. They are called heterogeneous network structures in IOV [1]. This structure can solve such problems as transport efficiency, traffic overload, and safety but the system is too big to complete immediately. [PERSON] et al. [2, 3] have presented the cell transmission model and four control strategies (single-line control, multiline control, area control, and diamond control) to verify efficiency, eliminating or reducing traffic jams in a two-way grid network. It would be beneficial to explore the applicability of these strategies in multi-way grid network scenarios. There are other strategies or algorithms to relieve or improve traffic jams. For example, using a neural network [4, 5, 6], fuzzy algorithm [7, 8, 9] or a detection algorithm without losing any significant traffic information with neural network and wavelet [10]. Color Petri net (CPN) [11, 12, 13, 14] is a visual software adapted to realize the dynamic variable state. The air, land, or sea traffic is dynamic and complicated. The CPN with the characteristics of sequence, causality, synchronization, and concurrency is suitable to illustrate the traffic state exactly. However, CPN using multi-set to form a model is not understood directly. Thus, timed Petri net (TPN) is a good choice because it has all the properties of CPN except multi-set. The TPN can use intuitive representation to model and easily demonstrate the dynamic traffic. The papers [15, 16, 17, 18, 19, 20, 21] can show the results. Therefore, our paper will select the tool of TPN to form the traffic signal model. Papers [16, 17, 18, 20, 21, 22] have proposed methods or algorithms to solve traffic congestion or improve transportation efficiency due to the number of vehicles exceeding road capacity. However, to our knowledge, no paper discusses how to address traffic accidents that cause major congestion when traffic flow is otherwise smooth. Typically, we can only wait for more accident response personnel to handle the resulting traffic jam immediately. This study [23] proposes a data-driven method to estimate and address traffic congestion between bus stops, using traffic indicators and self-organizing maps (SOMa) to identify patterns and rank congested areas. A case study validates its effectiveness, showing the potential for improving public transport through optimized network design. Paper [24] proposes a four-layer framework using technologies like IoT and data analytics to efficiently manage traffic congestion, featuring smart traffic lights and decision-making algorithms. The article [25] explores traffic congestion in Krasnoyarsk, focusing on modernizing outdated roundabouts with traffic lights and other upgrades to improve road capacity and reduce jams. The paper [26] discusses traffic congestion in Hanoi and Ho Chi Minh City, analyzing causes and proposing solutions while emphasizing the need for coordinated efforts from authorities and public feedback. All the above articles propose solutions to traffic congestion in a street, an area or a city. The reason is that the number of vehicles exceeds the capacity of the road. However, No one has discussed and proposed solutions in the event of a sudden traffic accident. Therefore, we proposed a study on the situation. Our paper categorizes sudden traffic accident scenarios into three types \(A\)-type, \(B\)-type, and \(C\)-type.