Objective The existing static chart analysis methods fail to intuitively demonstrate the time-variability of passenger flow data with time or the spatial asymmetry varying with spatial attributes. They cannot effectively extract the laws of station attributes and passenger travel characteristics from massive traffic passenger flow big data. Therefore, it is necessary to explore a visualization method for urban rail transit passenger flow big data.

Method Taking the passenger flow data of Shanghai rail transit network in April 2015 as the research object, the data is first pre-processed to eliminate noise data irrelevant to urban rail transit trips. Next, visualization methods for the time characteristic distribution of urban rail transit passenger flow are analyzed from three aspects: unbalanced passenger flow, line passenger flow, and station passenger flow. Finally, visualization methods for the space characteristic distribution of urban rail transit passenger flow are analyzed from three aspects: spatial distribution of line/station passenger flow, section passenger flow distribution, and OD (origin-destination) passenger flow spatial distribution.

Result & Conclusion  The use of different types of charts (bar charts, treemap-like charts, spider web diagram, dynamic rendering maps, etc.) for multi-dimensional and intuitive visualization enables a better understanding of the spatio-temporal distribution characteristics of urban rail transit passenger flow. By analyzing potential information and mining the value from historical and current data, accurate prediction of the complex dynamics for future passenger flow can be achieved.