The Integrated ANN-NPRT-HUB Algorithm for Rail-Transit Networks of Smart Cities: A TOD Case Study in Chengdu

Rail-transit hub classification in TOD refers to the categorization of transit stations based on their level of connectivity and ridership and the potential for development around them as part of a Transit-Oriented Development (TOD) strategy. TOD, as an essential concept in developing smart cities and public transportation accessibility, has attracted the focus of many policymakers. To this end, many research projects have been dedicated to classifying the rail-transit stations, although the necessity of integrated models for rail-transit hubs could have been mentioned in previous papers. Therefore, this parametric case study is directed to apply the Node-Place-Ridership-Time (NPRT) model to provide a logical classification model for Chengdu rail-transit hubs at the junctions of high-speed railway and subway stations. Multiple Linear Regression (MLR) provided a series of equations, including the effective parameters of the NPRT model. These equations were then verified by the Artificial Neural Network (ANN) to provide the effect of each node and place values on the integrated ridership of rail-transit hubs in different time periods. The results proved the consistent contribution of the integrated ANN-NPRT-HUB algorithm to the TOD concept for smart cities.

Automated summary

Rail-transit hub classification is an important part of Transit-Oriented Development (TOD) strategy, categorizing transit stations based on connectivity, ridership, and development potential. The concept of TOD, essential for developing smart cities and improving public transportation accessibility, has gained attention from policymakers. Despite previous research mentioning the need for integrated models, this case study applies the Node-Place-Ridership-Time (NPRT) model to classify Chengdu rail-transit hubs at high-speed railway and subway junctions.