# Efficient and Safe Strategies for Intersection Management: A Review
## 3 Efficiency
### Jam Detection and Congestion Avoidance
#### 3.1.2 Congestion Avoidance  
With the increasing traffic congestion, the need for congestion avoidance technology in traffic management is growing. These technologies utilize various methods and strategies to avoid traffic congestion. These commonly used techniques include using a prediction algorithm, VANET, and signal timing optimization to predict and avoid congestion at common road network bottlenecks. The driver can be informed by using prediction algorithm that the route is changed from a crowded area to a non-crowded area so that strategies such as vehicle diversion have also been proven to be an excellent choice to avoid congestion. [PERSON] et al. [31] propose the actual urban traffic simulative model (AUTM) to predict and avoid traffic jams. The map and transfer (MT) transformation method in AUTM is used to obtain the actual urban cellular space and to optimize vehicle dynamics taking advantage of spatial evolution rules. The authors conduct a large number of experimental simulations in several real cities and the results show that the accuracy of traffic congestion prediction exceeds 89%. Nevertheless, how to apply the dynamic strategy of traffic lights to improve traffic flow is not considered. In the research [32], the authors propose an advanced dynamic traffic network prediction and navigation model. Compared with the traditional shortest path algorithm which considers the static network as the core, the first part of this guiding method considers the potential traffic jam, gives the optimal driving suggestion for different times of day, designs the equilibrium Markov chain model, and this method is used to dispatch vehicles to alleviate urban congestion. Inspired by the pooling operation in deep learning, a representation framework [33] for traffic congestion data in urban road traffic network is proposed. The framework consists of a grid-based urban road traffic network partitioning and a pooling operation that reduces multiple values to aggregate values. We also recommend using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are converted and represented as a series of matrices that are used as inputs to the Spatio-temporal Congestion Prediction Network (STCN) to evaluate the validity of representation in predicting traffic congestion. [PERSON] et al. [34] proposed a fast and reliable traffic congestion detection method based on deep residual learning and video dynamic modeling of motion trajectory. This method makes use of motion and deep texture features effectively, overcomes the limitations of existing methods, and uses an effective representation learning method to capture the underlying structure in traffic video by modeling the evolution of texture features. This representation provides a significant improvement in detection results under all weather conditions. The basic idea of the prediction algorithm is to predict based on previous historical data, which is not friendly to the real-time nature of traffic. The full application of VANET solves this problem. A real-time queue length estimation method based on connected vehicle data is proposed [35]. The probe data is used to identify the stop state of the connected vehicles (CVs). On this basis, a queue length time series based on the stop time and the position of CVs is established to describe the queuing process of the intersection. Considering the statistical average traffic flow, the queue length time series and the stop state of arrival characteristics of the real-time CV in the current cycle, it is serviceable to use the linear fitting method to predict the critical queuing time and the Markov model to estimate the real-time queue length. Another paper studies queue length estimation for networked vehicles equipped with distance measurement sensors [36]. By extending the previous formula, a simple plug and play model is established to estimate queue length without requiring ground truth queue length.