Analysis of bus travel characteristics and predictions of elderly passenger flow based on smart card data

Preferential public transport policies provide an important social welfare support for travel by the elderly. However, the travel problems faced by the elderly, such as traffic congestion during peak hours, have not attracted enough attention from transportation-related departments. This study proposes a passenger flow prediction model for the elderly taking public transport and validates it using bus smart card data. The study incorporates short time series clustering (STSC) to integrate the elements of the heterogeneity of bus trips taken by the elderly, and accurately identifies the needs of elderly passengers by analysing passenger flow spatiotemporal characteristics. According to the needs and characteristics of passenger flow, a short time series clustering Seasonal Autoregressive Integrated Moving Average (STSC-SARIMA) model was constructed to predict passenger flow. The analysis of spatiotemporal travel characteristics identified three peak periods for the elderly to travel every day. The number of people traveling in the morning peak was significantly larger compared to other periods. At the same time, compared with bus lines running through central urban areas, multi-community, and densely populated areas, the passenger flow of bus lines in other areas dropped significantly. The study model was applied to Lhasa, China. The prediction results verify that the model has high prediction accuracy and applicability. In addition to the initial application, this predictive model provides new directions for bus passenger flow forecasting to support better public transport policy-making and improve elderly mobility.