Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. Athletics' rookie shortstop Jacob Wilson has taken the big leagues by storm. Wilson, 23, has a .347 The shortstop's batting average is second in MLB behind New York Yankees superstar Aaron Judge (.361) and has the second lowest strikeout-rate in baseball at 6.8%.

The scene at OneLegacy in Asuza on a recent Friday morning would have been familiar to anyone who's been in a hospital intensive care unit. Three adults were tucked into hospital beds, still and apparently asleep, with ventilators and other machines of artificial life doing the work that their bodies couldn't do. If you didn't know better, you'd think all the tubes and wires and boxes and screens were designed to save the lives of these patients, but it was too late for that. Instead, the machines were keeping oxygenated blood circulating through soon-to-be-donated organs of three people who had recently been declared brain dead. OneLegacy, you see, is not a hospital. And the kidneys, livers and other organs in those three bodies could save the lives of up to 24 people.

As various city agencies and mobility operators navigate toward innovative mobility solutions, there is a need for strategic flexibility in well-timed investment decisions in the design and timing of mobility service regions, i.e. cast as "real options" (RO). This problem becomes increasingly challenging with multiple interacting RO in such investments. We propose a scalable machine learning based RO framework for multi-period sequential service region design & timing problem for mobility-on-demand services, framed as a Markov decision process with non-stationary stochastic variables. A value function approximation policy from literature uses multi-option least squares Monte Carlo simulation to get a policy value for a set of interdependent investment decisions as deferral options (CR policy). The goal is to determine the optimal selection and timing of a set of zones to include in a service region. However, prior work required explicit enumeration of all possible sequences of investments. To address the combinatorial complexity of such enumeration, we propose a new variant "deep" RO policy using an efficient recurrent neural network (RNN) based ML method (CR-RNN policy) to sample sequences to forego the need for enumeration, making network design & timing policy tractable for large scale implementation. Experiments on multiple service region scenarios in New York City (NYC) shows the proposed policy substantially reduces the overall computational cost (time reduction for RO evaluation of > 90% of total investment sequences is achieved), with zero to near-zero gap compared to the benchmark. A case study of sequential service region design for expansion of MoD services in Brooklyn, NYC show that using the CR-RNN policy to determine optimal RO investment strategy yields a similar performance (0.5% within CR policy value) with significantly reduced computation time (about 5.4 times faster).