Lee D. Han

TL;DR: The OL-SVR model is compared with three well-known prediction models including Gaussian maximum likelihood (GML), Holt exponential smoothing, and artificial neural net models and suggests that GML, which relies heavily on the recurring characteristics of day-to-day traffic, performs slightly better than other models under typical traffic conditions, as demonstrated by previous studies.

TL;DR: A cost constrained e-bike sharing system design that can maintain a high level of system reliability (e-bike and battery availability) through optimal battery charging and distribution management is presented.

TL;DR: In a county-wide, large-scale evacuation case study, the one-destination model yields substantial improvement over the conventional approach, with the overall evacuation time reduced by more than 60 percent.

TL;DR: A modified version of a pattern recognition technique known as support vector machine for regression (SVR) to forecast AADT is presented and the performance of the SVR-DP was compared with those of Holt exponential smoothing (Holt-ES) and of ordinary least-square linear regression (OLS-regression).

TL;DR: This study employs empirical traffic speed data from radar detectors and vehicle probes and demonstrates that the overall predictions of the kriging-based imputation approach are accurate and reliable for all combinations of missing patterns and missing rates investigated.