# An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios
## 1 Introduction  
Transportation is an integral part of modern cities' development and growth, which is closely linked to residents' mobility [1], freight transportation [2], environmental sustainability [3], and more. Expressways, as a fundamental element of the transportation system, play a vital role in urbanization. In recent years, China's expressway network has grown significantly, which led to the expansion of its scale. However, this growth has also led to the development of traffic congestion, which is becoming increasingly severe. Traffic congestion can adversely affect the level of serviceability (LoS) of the road network, leading to poor traffic flow efficiency, environmental degradation, economic losses, and road safety concerns. It is essential to detect congested sections of the road promptly so that the transportation management departments can take immediate measures to alleviate traffic congestion and resolve traffic incidents within a specific area. The above actions will prevent more extensive traffic congestion caused by chain reactions. Traffic congestion detection involves evaluating the status and volume of vehicles on the road, which is typically determined indirectly using traffic flow parameters such as the number of vehicles, travel speed, queue length, and travel time. Many scholars from different disciplines and fields have analyzed how to use traffic flow parameters to detect traffic congestion and have achieved important research results in this area [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. Based on the technology used to extract traffic parameters, traffic congestion detection techniques can be classified into three categories: traffic congestion detection technology based on sensors [4; 5; 6], traffic congestion detection technology based on the Internet of Things (IoT) [7; 8],and traffic congestion detection technology based on machine vision [9; 10; 11; 12; 13]. Sensor-based detection methods typically require fixed sensors to obtain vehicle speed, and they calculate the average speed over a specific time to estimate road conditions and determine congestion levels. Although the existing sensor technology can detect vehicle information on the road relatively well, the magnetic field in the detection area is susceptible to external factors. The IoT is an emerging field in modern communication networks that has a significant impact on the development of smart cities [14; 15; 16]. Some studies in the literature utilize IoT technologies such as a Vehicular AdHoc Network (VANET) to detect traffic congestion. A VANET is a self-organized, structurally open communication network between vehicles that provides decentralized, multi-hop forwarding data transmission capabilities to collect and aggregate real-time speed and location information related to individual vehicles [17]. However, deploying VANET is quite expensive, which requires that vehicles must be equipped with on-board units, and roadside units must be installed along the road. Additionally, the high mobility of vehicles and dynamic changes in related topologies make wireless transmission challenging. Machine vision-based traffic congestion detection technology is a promising and cost-effective solution for surveillance traffic flow. Cameras can capture high-quality video sequences and are widely available and easy to maintain [18; 19]. With the growing deployment of cameras on expressways and the advancement of computer vision technology, machine vision-based traffic congestion detection has become feasible. Currently, many machine vision-based traffic congestion detection methods are designed for urban roads. However, expressways are relatively more enclosed compared to urban roads, and surveillance cameras on expressways usually have a larger viewing angle, resulting in images with larger background areas. Therefore, it needs to be verified whether traffic congestion detection methods based on urban roads can be directly used for detecting expressway congestion.