  In this paper, we propose an Empirically-based Monte Carlo Bus-network (EMB)
model as a test bed to simulate intervention strategies to overcome the
inefficiencies of bus bunching. The EMB model is an agent-based model which
utilizes the positional and temporal data of the buses obtained from the Global
Positioning System (GPS) to constitute: (1) a set of empirical velocity
distributions of the buses, and (2) a set of exponential distributions of
inter-arrival time of passengers at the bus stops. Monte Carlo sampling is then
performed on these two derived probability distributions to yield the
stochastic dynamics of both the buses' motion and passengers' arrival. Our EMB
model is generic and can be applied to any real-world bus network system. In
particular, we have validated the model against the Nanyang Technological
University's Shuttle Bus System by demonstrating its accuracy in capturing the
bunching dynamics of the shuttle buses. Furthermore, we have analyzed the
efficacy of three intervention strategies: holding, no-boarding, and
centralized-pulsing, against bus bunching by incorporating the rule-set of
these strategies into the model. Under the scenario where the buses have the
same velocity, we found that all three strategies improve both the waiting and
travelling time of the commuters. However, when the buses have different
velocities, only the centralized-pulsing scheme consistently outperform the
control scenario where the buses periodically bunch together.
