Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data

By Jaimyoung Kwon, Benjamin Coifman, Peter J. Bickel. 2000

315 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/f9d88f50fc3f76e0047b134fa918f6c1a4a79a3f

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. Erik van Zwet
2. S. Waller
3. John Rice
4. Manwo Ng
5. Wai Yuen Szeto
6. Yiannis Kamarianakis
7. Poulicos Prastacos

In Control and Systems Engineering

1. S. Waller
2. John Rice
3. Manwo Ng
4. Wai Yuen Szeto
5. Erik van Zwet
6. Yiannis Kamarianakis
7. Poulicos Prastacos
8. W. Jin

In Transportation

1. Erik van Zwet
2. S. Waller
3. John Rice
4. Manwo Ng
5. Wai Yuen Szeto
6. Michael A. P. Taylor
7. Yiannis Kamarianakis
8. Poulicos Prastacos

Citations in the corpus

• A simple and effective method for predicting travel times on freeways (2004, methodology): “In the literature, we find spectral analysis [7], Kalman filtering [8], [9], linear models [10], [11], and autoregressive-integrated moving average (ARIMA) models [12], [13].”
• Distribution-free travel time reliability assessment with probability inequalities (2011, methodology): “…to estimate these moments (i.e., the moments of the travel times experienced by a single individual): simply use their sample counterparts based on, for example, loop detector data (e.g. as in Kwon et al, 2000) (cf. the method of moments in statistics, e.g. see Casella and Berger, 2002).”