# Efficient and Safe Strategies for Intersection Management: A Review
## 3 Efficiency
### Jam Detection and Congestion Avoidance
#### 3.1.1 Jam Detection
Finally, a texture-based target tracking method is used to track each detected target and then apply a virtual loop detector to calculate the traffic flow. However, when it comes to disasters and significant events, collecting data can be complicated whether it is background or foreground. Using aerial images can solve this problem. Therefore we use satellites to obtain high-resolution images and analyze traffic density based on photos to estimate whether congestion will occur. Ariel images enhancement techniques, such as interpolation algorithms, decimated wavelet with interpolation, wavelet zero paddings (WZP), and hybrid wavelet decomposition (HWD), can improve the calculation accuracy of traffic density [23]. On the highway, sensor technology is widely used because of the high traffic volume and fast speed, and the use of image processing technology to determine whether the blockage is rare. Sensors are classified as invasive and non-invasive. Non-invasive technologies are based on video image processing, optics, and ultrasound as well as lidar and microwave radar. The inductive loop, magnetometer, and pressure switch types are three main sorts of the current intrusive technologies. Inductive loop (IL) sensors installed at selected points of road infrastructure can be used for anonymous vehicle tracking in the application to efficient traffic control and calculates the passing vehicle at a certain point. However, various signal conditioning systems (SCS) cause disparities between the vehicles magnetic signature (VMS). An approach is proposed to increase compatibility between VMS from impedance SCS and generator-based SCS. A proper electrical model of a generator-based SCS is used to produce a compatible VMS from an impedance SCS [24]. In [25], the authors propose a method based on the single-chip microcomputer inductive loop vehicle sensor, which uses an automatic calibration algorithm based on the k-means clustering. The circuit is simple, and the signal processing does not require intensive computing power and can be implemented on a low-cost microcontroller. Inductive loops are a cost-effective solution, but when they are installed on poor road surfaces, the failure rate is high, resulting in reduced road life and obstructing traffic during maintenance and repair. Magnetic sensors are not affected by weather conditions and are suitable for large-scale deployment and time-sustainable detection for traffic information acquisition, thus providing more stable and reliable detection data than other technologies. The authors [26] introduce a multi-functional wireless magnetic sensor and propose an alternative, efficient and accurate detection method to solve the drift named \"Wake effect\" caused by the weather environment and the traffic flow environment. Another paper improves the performance of SCAN and Decision Algorithm (SDA) [27].The new algorithm, based on magnetic sensors, adds a two-pass moving average filter to improve the signal to noise ratio (SNR) after analog-to-digital conversion. The improved mathematical capabilities allow us to capture additional features of vehicle orientation and classification. The performance of the proposed algorithm is evaluated by field real-time experiments in a designated road section. The results show that the roadside magnetic sensors can improve vehicle detection, counting, travel time index, and classification in low speed and crowded traffic conditions. In addition, since acoustic sensors are cheaper than other types of vehicle sensors, and acoustic characteristics are robust to the environment changes like light, weather, etc., cross-microphone arrays are used [28] to collect roadside acoustic signals. The built-in lane detection module then automatically detects the lane position. Finally, based on the collected signals and the detected lanes, different measurement indexes reflecting road conditions and traffic quality are derived. Probe-vehicle-based techniques mainly use two technologies: GPS-based and smartphone-based technologies.