Daily Demand per Functioning Hour as a Different Operating Target
Abstract
The question concerns daily demand per functioning hour. The secondary ordinary least squares analysis contains 353 observations and uses HC3 covariance. The coefficient on temperature is 47.7602, with a 95 percent interval from 42.5504 to 52.9700. Dividing daily rentals by functioning hours changes the estimand from total activity to activity per available hour. The declared denominator matters where service availability differs. Offline application and covariate selection motivate forecasting this target before operation. The present daily association does not test a prepared rule on a new deployment period. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
References
J. Du, S. Gao, and C.-H. Chen, “Selecting the Optimal System Design under Covariates,” Proceedings of the 2019 IEEE International Conference on Automation Science and Engineering, pp. 547–552, 2019.
S. Gao, C. Li, and J. Du, “Rate Analysis for Offline Simulation Online Application,” Proceedings of the 2019 Winter Simulation Conference, pp. 3468–3479, 2019.
Seoul Bike Sharing Demand. UCI Machine Learning Repository, 2020. Dataset. https://doi.org/10.24432/C5F62R.
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.