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).

本文介绍了在自动语音识别（ASR）的语境中的声学模型的新型深度学习架构，称为MixNet。除了在LSTM-HMM中的DNN-HMM和存储器单元中的完全连接层之外，该模型使用基于专家（MOE）的混合的两个附加层。在输入时操作的第一个Moe层基于预定义的广义语音类，并且在倒数第二层操作的第二层基于自动学习的声学类。在自然语音中，不同声学类的分布在分布中是不可避免的，这导致帧间错误分类。如果经过修改的传统架构，则预期ASR精度将改进，以使其更适合于占这种重叠。 MixNet正在开发牢记这一点。通过散点图进行的分析验证了MOE确实改善了转化为更好ASR精度的类之间的分离。实验在大型词汇ASR任务上进行，表明，与传统模型，即DNN和LSTM分别提供了13.6％和10.0％的单词误差速率，即使用SMBR标准训练。与用于电话分类的现有方法相比（由EIGEN等人），我们所提出的方法产生了显着的改善。