The Weekend Contrast Changes with the Hour of Application
Abstract
The question concerns hour-specific weekend contrasts in traffic. The secondary ordinary least squares analysis contains 40,564 observations and uses cluster covariance. The coefficient on weekend is 0.6964, with a 95 percent interval from 0.6583 to 0.7345. Hour-specific weekend terms describe a conditional timing contrast. The full weekend effect at an hour includes its main effect and the relevant interaction; the reported interaction alone is not that complete contrast. Contextual Gaussian-process selection suggests local action checks, while the present regression has not compared alternative operating policies. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
References
- S. Cakmak, Y. Wang, S. Gao, and E. Zhou, “Contextual Ranking and Selection with Gaussian Processes and Optimal Computing Budget Allocation,” ACM Transactions on Modeling and Computer Simulation, vol. 34, no. 2, pp. 1–24, 2024.
- S. Cakmak, E. Zhou, and S. Gao, “Contextual Ranking and Selection with Gaussian Processes,” Proceedings of the 2021 Winter Simulation Conference, pp. 1–12, 2021.
- John Hogue. Metro Interstate Traffic Volume. UCI Machine Learning Repository, 2019. Dataset. https://doi.org/10.24432/C5X60B.
- White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817–838. https://doi.org/10.2307/1912934.
- Cameron, A. Colin, and Douglas L. Miller. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, vol. 50, no. 2, 2015, pp. 317–372. https://doi.org/10.3368/jhr.50.2.317.
- Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.
- Ankenman, Bruce, Barry L. Nelson, and Jeremy Staum. “Stochastic Kriging for Simulation Metamodeling.” Operations Research, vol. 58, no. 2, 2010, pp. 371–382. https://doi.org/10.1287/opre.1090.0754.
- Kim, Seong-Hee, and Barry L. Nelson. “A Fully Sequential Procedure for Indifference-Zone Selection in Simulation.” ACM Transactions on Modeling and Computer Simulation, vol. 11, no. 3, 2001, pp. 251–273. https://doi.org/10.1145/502109.502111.
- Frazier, Peter I., Warren B. Powell, and Savas Dayanik. “A Knowledge-Gradient Policy for Sequential Information Collection.” SIAM Journal on Control and Optimization, vol. 47, no. 5, 2008, pp. 2410–2439. https://doi.org/10.1137/070693424.
- Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.