An Empirical Study of Light Railway Transit Ridership using Socio-economic Data Based on Block Group Level

Abstract

A direct demand model requires relatively little analysis time and incurs a low cost. It is also known to be useful for the preliminary screening of promising configurations or concepts. This study reviews direct demand models of 12 existing urban railways using demographic data based on a block group level which is approximately 1/24 of a traditional zone area. However, direct demand models are limited. Therefore, a new approach is suggested. The proposed method is based on a field study and an empirical analysis. The study finds factors that affect ridership at the station level. As a case study, the proposed approach is tested using 54 light railway transit stations. The results of this empirical study demonstrate its applicability to improve the error rates of the predicted ridership at the station level.

Digitalization for the environmental impact assessment of railway projects using drones and lidar

“…Recently, Fourth Industrial Revolution technologies such as drones, lidar, big data, and artificial intelligence (AI) have been used in various fields. The environmental assessment of railroad projects could benefit from the deployment of these technologies to collect environmental data and to build prediction models of environmental effects (Lee et al, 2015;Gyeonggi Research Institute, 2021).…”