Avoiding the crowds: understanding Tube station congestion patterns from trip data

111 citations recorded by the source corpus.

How researchers see this paper

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

Knock Knock, Who's There? Membership Inference on Aggregate Location Data (2017, background): “Both datasets contain about one month of location data, and have been used often in ubiquitous computing [5, 29] and location privacy [22, 27] research.”
A literature survey on smart cities (2015, background): “Ceapa et al. [95] systematically analyzed passenger congestion patterns on the London Underground, and proposed a technical solution for passenger congestion.”
Does crowding affect the path choice of metro passengers (2015, background): “A difficulty was, as also noted in Ceapa et al. (2012) and Jang (2010), the real path choice of a passenger is hidden; the actual transfer station(s) chosen by passengers are not readily determined when there are multiple connections of similar costs. The pervading Smart Card automated fare collecti”
What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, methodology): “ a unique trip and consists of the following ﬁelds: (anonymized) oyster card id, start time, touch-in station id, end time, and touch-out station id. (Note that the same dataset has also been used in [7,28,37]). Pre-processing & Sampling. We discard trips from March 29– 31, 2010 to obtain exactl”