Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile
By M. Munizaga, C. Palma. 2012
530 citations recorded by the source corpus.
Subfields: Transportation
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Transportation
Citations in the corpus
- A survey of data fusion in smart city applications (2019, background): “origin-destination matrices and service level obtained from big data) [139], [202] and more advanced transport modeling tools enabled by big data [203] have brought new opportunities.”
- Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system (2012, methodology): “During a typical weekday, the demand of the bus and rapid transit systems have distinct spatio-temporal characteristics, which have been captured using smart card data, as shown in [7] and [8] respectively.”
- Mining smart card data for transit riders’ travel patterns (2013, background): “Most previous smart-card-based research into transit traveler behaviors has focused on transfer point estimation and on origin and destination inference (Munizaga and Palma, 2012).”
- Understanding Urban Human Mobility through Crowdsensed Data (2018, background): “Context-fitting problems include mapping trips to origin-destination pairs [14] and estimating the alighting locations of smartcard data based on boarding information [15].”
- Crowding valuation in urban tram and bus transportation based on smart card data (2018, background): “Batarce et al. (2015) estimate a mixed SP/RP model for the case of Santiago, Chile, combining a SP survey with pictured crowding levels and revealed passenger route choice based on smart card data.”
- Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, unknown): “portation passenger travel behaviour [19]–[22].”