Sign Optimization Model for Rail Transit: A Big Data Approach

The subway station has a large passenger flow and strong mobility, and subway-oriented signs have become the necessary core support for the subway system. The paper first takes the complaint data of Beijing Metro as the research object and uses Word2vec methods to effectively prove the phenomenon of subway-oriented identification. Next, it builds a MIP model for the subway-oriented identification system based on the optimization theory and analyzes the model. As an example, Dongzhimen Station of the subway has optimized the spatial layout of its guide signs. Dongzhimen station is an important transportation hub, there are some sign problem, such as long transfer distance, narrow transfer space, complicated traffic conditions, and obvious passenger flow aggregation. The amount of induced information in the area is the objective function, and an optimization model for the optimization of signs is established to help scientifically find the optimal placement of guide signs from multiple candidate positions. The model can theoretically reduce the guidance errors caused by the guidance marks, and will be verified by real cases in practical applications, thereby verifying the effectiveness of the model. The research results can provide reference and reference for subway passenger flow guidance.