A High-Volume Event before Ranking Service Alternatives
Abstract
The question concerns illustrative high-volume hour. The secondary ordinary least squares analysis contains 40,564 observations and uses cluster covariance. The coefficient on rain is -0.0060, with a 95 percent interval from -0.0094 to -0.0025. The high-volume event is illustrative rather than a congestion or engineering standard. Adoption, avoidance, and top-group membership require a final action and loss beyond this count indicator. Independent evaluation would need to measure what a chosen service alternative does under these conditions. The event association alone does not demonstrate such a decision effect. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
References
H. Xiao, S. Gao, and L. H. Lee, “Simulation Budget Allocation for Simultaneously Selecting the Best and Worst Subsets,” Automatica, vol. 84, pp. 117–127, 2017.
S. Gao and W. Chen, “A New Budget Allocation Framework for Selecting Top Simulated Designs,” IIE Transactions, vol. 48, no. 9, pp. 855–863, 2016.
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.