Traffic load prediction for bridge construction based on Internet of Things and BIM

Abstract

With the development of urban transportation infrastructure, bridge construction often leads to traffic congestion and safety hazards. The traditional traffic load prediction fails to solve the dynamic change of traffic during construction. For this reason, this paper proposes a traffic load prediction and dynamic optimization method based on the integration of Building Information Modeling (BIM) and Internet of Things (IoT). Real-time traffic, bridge status and construction information are collected through IoT devices, and two-way data fusion is carried out by combining BIM model and real-time data. The real-time feedback provided by IoT optimizes the traffic flow prediction and adjusts the construction plan. Combining LSTM and NSGA-II optimization methods, a dynamic prediction and adjustment framework is constructed to significantly improve the accuracy of traffic prediction and management efficiency during construction.