  Bus transit systems are the backbone of public transportation in the United
States. An important indicator of the quality of service in such
infrastructures is on-time performance at stops, with published transit
schedules playing an integral role governing the level of success of the
service. However there are relatively few optimization architectures leveraging
stochastic search that focus on optimizing bus timetables with the objective of
maximizing probability of bus arrivals at timepoints with delays within desired
on-time ranges. In addition to this, there is a lack of substantial research
considering monthly and seasonal variations of delay patterns integrated with
such optimization strategies. To address these,this paper makes the following
contributions to the corpus of studies on transit on-time performance
optimization: (a) an unsupervised clustering mechanism is presented which
groups months with similar seasonal delay patterns, (b) the problem is
formulated as a single-objective optimization task and a greedy algorithm, a
genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm
are employed to solve it, (c) a detailed discussion on empirical results
comparing the algorithms are provided and sensitivity analysis on
hyper-parameters of the heuristics are presented along with execution times,
which will help practitioners looking at similar problems. The analyses
conducted are insightful in the local context of improving public transit
scheduling in the Nashville metro region as well as informative from a global
perspective as an elaborate case study which builds upon the growing corpus of
empirical studies using nature-inspired approaches to transit schedule
optimization.
