  Public-transit systems face a number of operational challenges: (a) changing
ridership patterns requiring optimization of fixed line services, (b)
optimizing vehicle-to-trip assignments to reduce maintenance and operation
codes, and (c) ensuring equitable and fair coverage to areas with low
ridership. Optimizing these objectives presents a hard computational problem
due to the size and complexity of the decision space. State-of-the-art methods
formulate these problems as variants of the vehicle routing problem and use
data-driven heuristics for optimizing the procedures. However, the evaluation
and training of these algorithms require large datasets that provide realistic
coverage of various operational uncertainties. This paper presents a dynamic
simulation platform, called Transit-Gym, that can bridge this gap by providing
the ability to simulate scenarios, focusing on variation of demand models,
variations of route networks, and variations of vehicle-to-trip assignments.
The central contribution of this work is a domain-specific language and
associated experimentation tool-chain and infrastructure to enable
subject-matter experts to intuitively specify, simulate, and analyze
large-scale transit scenarios and their parametric variations. Of particular
significance is an integrated microscopic energy consumption model that also
helps to analyze the energy cost of various transit decisions made by the
transportation agency of a city.
