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

Abstract

<abstract> <p>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.</p> </abstract>

RETRACTED: Rail transit corridor design modeling based on TOD project development

“…The steady development of rail transit is the inevitable choice for China and other large cities around the world. [1][2][3] The high-quality public transport system in the city provides convenient travel services for the residents, meanwhile attracts more traffic participants to live. 4 Considering the coordinated development of TOD urban land and solving the problem of TOD project site selection, Reasonably plan the high-density development traffic corridor is extremely important.…”

Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators

“…The Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model is used to predict the frequency of passenger flows [22]. The Short Time Series Clustering-Seasonal Autoregressive Integrated Moving Average (STSC-SARIMA) model was built to predict bus public transportation passenger flows with high prediction accuracy and applicability [23]. An integrated model to accurately predict short-term passenger flows for public bus transportation using Multivariable Linear Regression (MLR), K-Nearest Neighbor (KNN), XGBoost, and Gated Recurrent Unit (GRU) with results that can enrich the short-term passenger flow prediction system and provide effective data support [24].…”