## 1 Introduction  
As urbanization progresses, it directly results in the continuous expansion of cities, accompanied by significant increases in traffic flow. Although the development of today's intelligent transportation systems and computer technology has matured, their contribution to alleviating traffic congestion and reducing traffic accidents remains insufficient. Traffic flow prediction plays a vital role in the intelligent traffic control and management system, which is crucial for alleviating urban traffic congestion and reducing traffic accidents. Generally, traffic flow data collected by sensors installed on roads at predetermined intervals often exhibit time-series distribution characteristics. Lane occupancy prediction is a crucial indicator within traffic flow analysis. It refers to the percentage of lanes vehicles occupy on a roadway at any given time. Such predictions inform the degree of roadway congestion and traffic flow density. Lane occupancy prediction differs from traffic flow prediction by focusing specifically on traffic congestion and accidents resulting from lane occupancy [1]. Accurate predictions enable traffic management departments to comprehend road congestion scenarios better and implement timely measures to mitigate congestion [2]. Real-time monitoring of lane occupancy allows traffic managers to adjust traffic signals, guide flows, and employ strategies to alleviate congestion and enhance road efficiency. It serves as a vital reference for transportation planners and designers. Accurate forecasts aid planners in assessing road network capacity traffic demand and identifying bottleneck locations for informed planning, design, and improvement decisions. The outcomes of lane occupancy prediction facilitate rational planning of new roads, expansion of existing ones, and adjustment of traffic flow distribution strategies to meet future demand. Furthermore, it is crucial for traffic safety management. Accurate lane occupancy predictions enable traffic management and emergency response agencies to identify potential crash risk areas and congestion points proactively. Accurate prediction of lane occupancy facilitates the implementation of appropriate measures, including increasing police patrols, installing temporary traffic signs, and adjusting traffic routes to prevent accidents or mitigate adverse impacts on traffic flow. Recent advancements in intelligent transportation systems and computer technology have significantly enhanced lane occupancy prediction methods. However, external factors such as weather conditions, traffic accidents, and traffic control influence lane occupancy rates owing to the openness and complexity of urban transportation systems [3]. Accurate lane occupancy prediction necessitates datasets with high-quality attributes, and the rapid advancement of sensor technology has facilitated more accessible data collection, thereby providing ample datasets for prediction. Data collectors, installed on or beneath road-sides, gather vehicle-related information, distributing it according to predefined intervals to exhibit time-series characteristics. However, holidays, weather, and other uncertain factors may affect the collected data, complicating lane occupancy rate prediction. Initially, traditional traffic flow prediction methods, including the Kalman filter and Markov models, were simple but ineffective under complex road conditions. As urbanization intensified, the increasing complexity and variability of urban roads led to the gradual abandonment of these methods. Subsequently, the Autoregressive Integrated Moving Average (ARIMA) model became a commonly used method for lane prediction. The ARIMA model attempts to uncover time-series patterns through autocorrelation and differencing, utilizing these patterns for future predictions [4]. Vector Autoregressive (VAR) models, akin to ARIMA, are favored in traffic flow forecasting for their high flexibility.