# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 1 Introduction  
Intersections are bottleneck areas in urban road networks. Affected by the surrounding environment, pedestrians, and other complex factors, urban road intersections are prone to problems such as congestion and delay, which spread to the surrounding urban road network and seriously affect people's daily commuting. Addressing urban traffic challenges hinges on optimizing intersection performance. Traffic signal control plays a vital role by allocating right-of-way to conflicting traffic streams that cannot be spatially separated, thus managing their movement through time-based sequencing. Effective signal control strategies can dynamically adjust capacity in response to real-time traffic fluctuations, enhancing safety and efficiency while maximizing benefits for all road users [1, 2]. The accurate and timely acquisition of traffic information is the premise of optimizing intersection signal control schemes and alleviating traffic problems. Currently, there are several ways to obtain traffic information. [PERSON] et al. introduced an attention mechanism to capture the relationship between the importance distribution and the duration of historical time series to predict the traffic flow at intersections [3]. [PERSON] et al. proposed a deep-learning model based on Signal-control Refined Dynamic Traffic Graph (ScR-DTG) to predict the traffic volume of upcoming intersection movements [4]. [PERSON] et al. developed a vision-based real-time traffic flow monitoring system to calculate traffic flow information such as vehicle direction, total number, instantaneous speed, and average speed based on the predicted trajectory [5]. [PERSON] et al. proposed a method based on imagefusion for detecting traffic objects under low light conditions [6]. [PERSON] et al. proposed a method for traffic information detection based on satellite images [7]. [PERSON] et al. proposed an improved wavelet transform to detect the singularities of real-time traffic flow signals, which can predict traffic flow with higher prediction accuracy and lower computation cost [8]. [PERSON] et al. proposed a millimeter-wave radar for traffic flow detection, which can realize traffic behavior analysis and traffic flow parameter identification [9]. [PERSON] et al. proposed a new traffic flow prediction approach from the perspective of vehicle functions by using radio frequency identification (RFID) electronic license plate (ELP) data [10]. [PERSON] et al. built geo-reference locations for dynamic cooperative communication, simulated mixed traffic and connected vehicle traffic flow scenarios, and analyzed the improvement of commute time, queue, and congestion [11]. Wi-Fi data acquisition technology has high accuracy and strong adaptability in a small range and is almost unaffected by weather and road environments [12]. Owing to its short propagation distance and fast propagation attenuation, Wi-Fi is suitable for positioning based on the Received Signal Strength (RSS) [13]. Most urban residents' mobile phones, personal computers, and other mobile devices support Wi-Fi technology, and daily work and entertainment also focus on using Wi-Fi to connect to the internet. According to relevant statistics, 40% of mobile device users forget to turn off the Wi-Fi function after enabling it [14]. The combination of Wi-Fi positioning and spatiotemporal correlation analysis promotes the application of Wi-Fi data acquisition technology in various scenarios. Currently, the research and application of Wi-Fi data acquisition technology are relatively extensive, and there are many applications and deployments in buildings or public places. [PERSON] et al. used a large number of network Wi-Fi data analyses to extract personnel activity trajectories. This approach can provide better data support for facility management and planning activities in large buildings [15]. [PERSON] studied privacy problems for owners of wireless portable devices and how to address potential risks [16]. [PERSON] et al.