# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 1 Introduction
used Wi-Fi technology to collect non-passenger data on pedestrians, vehicles, and buildings in cities. The collected results were displayed on the map as reference data for travel [17]. [PERSON] et al. designed a pedestrian pressure monitoring system using Wi-Fi technology and positioning based on Received Signal Strength Indicator (RSSI) [18]. [PERSON] et al. proposed a positioning and tracking scheme based on Wi-Fi location big data to obtain a target's usual location through statistical analysis [19]. [PERSON] et al. proposed a distance-based outdoor pedestrian positioning method using Wi-Fi to estimate high-precision distance [20]. Wi-Fi has also been used to detect vehicle paths. The authors in [21] studied the Wi-Fi patrol line obstacle avoidance car. The research aimed to improve the quality of people's daily lives and play a cornerstone role in automatic driving and cargo transportation in terms of intelligent vehicle environment detection technology. The authors in [22] proposed an approach to industrial vehicle flow analysis to improve factory transportation management. The efficiency of internal logistics was improved with the Wi-Fi-based technique for monitoring industrial vehicles. The authors in [23] proposed an intelligent parking management system based on Wi-Fi positioning and Bluetooth control technologies to manage vehicles entering or exiting a parking lot. Owing to its convenience, practicability, and low cost, such a parking system using Bluetooth identification based on Wi-Fi positioning was worth popularizing. The authors in [24] described an approach to monitoring pedestrian and vehicle activities in outdoor urban environments based on the analysis of wireless probe requests. Load tests demonstrated a sensor's ability to maintain a probe request capture rate above 95% over a wide range of probe request emission rates. Wi-Fi data technology can be further applied to the traffic field by linking individual movement behavior with traffic commute behavior. The authors in [25] measured bus passenger load by detecting the periodic network detection activity of Wi-Fi devices in \"smartphones\" and concluded that Wi-Fi activity was indeed correlated with passenger flow. The authors in [26; 27] estimated the number of bus passengers based on the Wi-Fi data survey and the extracted passenger volume data. The authors in [28] proposed the construction concept of \"a traffic network detection platform based on Bluetooth and Wi-Fi wireless technologies\", which can detect vehicle speed through wireless devices. The detection accuracy of this platform reached 85-90%. The authors in [29] used Wi-Fi data acquisition technology to estimate the occupancy rate of public transportation and analyzed the operational efficiency of bus lines based on it. The authors in [30] designed and implemented a traffic flow monitoring system based on Wi-Fi data acquisition technology, which performed well in road sections with a relatively simple environment. The authors in [31] deployed Wi-Fi data acquisition devices on buses, used the maximum expectation algorithm to screen bus passengers effectively, and conducted a preliminary study on public transport passenger flow estimation. The authors in [32] used Wi-Fi services provided on buses to estimate real-time urban traffic conditions and verified that Wi-Fi data collection technology in urban areas was an excellent supplement to existing traffic conditions collection. In the above studies, Wi-Fi data acquisition technology has primarily been applied to relatively straightforward scenarios, such as public transportation systems, where travel behaviors are straightforward and traffic information exhibits high substitutability. As a result, its broader applications remain limited. However, given the increasing complexity of urban road networks and the growing diversity of commuter behaviors, Wi-Fi data acquisition holds significant untapped potential for more advanced traffic management and analysis.