Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System

By Martin Trépanier, Nicolas Tranchant, Robert Chapleau. Journal of Intelligent Transportation Systems / Taylor & Francis, 2007

377 citations recorded by the source corpus.

Subfields: Automotive Engineering, Building and Construction, Transportation

View Paper: https://www.semanticscholar.org/paper/4a6ff34ee02278d2ca2bdb69d91a7e994f33f7dd

How researchers see this paper

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

In Automotive Engineering

1. Menno Yap
2. Lijun Sun
3. Xianfeng Huang
4. Der-Horng Lee
5. B. van Arem
6. Oded Cats
7. A. Erath

In Building and Construction

1. Menno Yap
2. Lijun Sun
3. Der-Horng Lee
4. B. van Arem
5. Oded Cats

In Transportation

1. Menno Yap
2. Lijun Sun
3. Xianfeng Huang
4. Der-Horng Lee
5. B. van Arem
6. Oded Cats
7. A. Erath

Citations in the corpus

• Crowding valuation in urban tram and bus transportation based on smart card data (2018, methodology): “Although there exist many destination inference algorithms in scientific literature (e.g. the well-known trip chaining algorithm as applied by Trépanier, Tranchant, and Chapleau 2007; Zhao, Rahbee, and Wilson 2007 and Wang, Attanucci, andWilson 2011), we removed all incomplete transactions, since…”
• Using smart card data to extract passenger's spatio-temporal density and train's trajectory of MRT system (2012, background): “Compared with other smart card data sources stated in [7] and [11], the most significant advantage of the smart card dataset is that it contains precise timing and location information for both boarding and alighting.”