Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system

By Lijun Sun, Der-Horng Lee, A. Erath, Xianfeng Huang. 2012

142 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Sebastien Blandin

In Transportation

1. Sebastien Blandin
2. Ichiro Sakata
3. Junichiro Mori

Citations in the corpus

• Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics (2016, background): “These studies serve as a building block for nation-wide simulation engines [24, 32], which can in turn support enhanced response to incidents [12, 16, 33].”
• Representation learning for geospatial areas using large-scale mobility data from smart card (2016, methodology): “Modeling the characteristics of spatial areas using mobility data Several studies on modeling and predicting people flow with the mobility data have been recently conducted for several purposes of city planning, disaster prevention, and advertising [3, 10, 16].”