However, this method can only measure settlement in the form of step settlement, and it has low measurement efficiency. Moreover, it significantly affects the normal operation of traffic during the measurement process. In 1982, a research team composed of multiple countries and organized by the World Bank conducted a large-scale road roughness experiment in Brazil and introduced the International Roughness Index (IRI) as a measure of road roughness. The International Roughness Index (IRI) is commonly used to assess the smoothness of road surfaces [18]. Methods for measuring road roughness include the walking profiler produced by Australian Road Research Board (ARRB) [5], continuous roughness meters [6], and laser road roughness meters [7]. However, the walking profiler has relatively low efficiency as it requires manual pushing, and continuous roughness meters and laser road roughness meters have relatively low sensitivity to non-staggered-step settlement. [PERSON] et al. proposed a semi-supervised learning method to comprehensively evaluate the IRIs based on multi-vehicle vibration data. Although the errors of the model were significantly affected by the iteration order. The roughness of large-scale road and bridge transition sections can be quickly assessed [8]. With the development of information technology, some scholars have started to use information technology to measure road roughness. [PERSON] et al. compared a laser vehicle section meter and a smartphone with a pavement roughness test app. The correlation between the two was good on pavements in good condition but not rough conditions [9]. [PERSON] et al. analyzed the correlation between IRI measured using a smartphone and IRI measured using a roughometer. They also investigated the impact of vehicle speed on the correlation and determined a reliable speed range for estimating road surface roughness using a smartphone [10]. [PERSON] et al. developed an in-vehicle sensor-based pavement roughness detection system, which uses signal processing and machine learning-based methods to collect data to achieve model recognition of road anomalies. However, the method is susceptible to the background environment [11]. When vehicles pass over bridgeheads with differential settlement, it can lead to vehicle vibrations and oscillations. Therefore, some scholars evaluate the differential settlement of bridgeheads by studying the vibrations of vehicles. The vibration model of the car-body-seat system was established by [PERSON] et al. [PERSON] et al. assessed the differential settlement of roads by measuring the vibration response of the car-body-seat system [12]. [PERSON] et al. transformed the measured vertical acceleration of the vehicle into the acceleration of the on-spring mass in a standardized quarter car model. This acceleration was subsequently utilized to estimate the road roughness through an approximate calculation [13]. [PERSON] et al. used sensing elements such as accelerometers, microphones, radios, and GPS that come with smartphones to collect acceleration, vehicle bumps, and other data to assess differential settlement of roads and bridges [14]. The ISO International Vibration Standard [19] establishes a link between vehicle vibration and riding comfort. Some scholars reflect the differential settlement of bridgeheads by testing the physiological signs of drivers and passengers as the vehicle passes over them. [PERSON] et al. selected the driver's heart rate as an indicator to assess the impact of bridgehead bumping on vehicle performance. They observed a significant correlation between variations in the driver's heart rate and the severity of bridgehead bumping [15]. [PERSON] et al. utilized the vehicle dynamics simulation software Carsim to conduct simulations and found that bridgehead height differences significantly impact passenger comfort. They observed that when the height difference exceeded 20 mm and the vehicle speed exceeded 100 km/h, the comfort level decreased [16]. [PERSON] et al.