Abstract
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 interarrival 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 times of the commuters. However, when the buses have different velocities, only the centralized-pulsing scheme consistently outperforms the control scenario where the buses periodically bunch together.
Highlights
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. e 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 interarrival time of passengers at the bus stops
With exception of the r2emp(0) ∈ [0.8, 0.9) bin, the distance of all aggregated trajectories was found to be within the empirical horizon. is reflects a close correspondence between the dynamical behavior found in the empirical system and that captured by the EMB model
We propose the empirically based, Monte Carlo bus-network (EMB) model, which is an empirical agentbased model designed as a test bed for potential intervention strategies to reduce bus bunching. rough that, we have studied three classes of intervention strategies
Summary
In order to validate our model, we split a day into 4 segments with breakpoints at 1000 h, 1400 h, and 2020 h. e reason for this split is that bus drivers in NTU shuttle services work in shift which typically start and end at these breakpoints. E reason for this split is that bus drivers in NTU shuttle services work in shift which typically start and end at these breakpoints Such splitting corresponds to the different passenger arrival rates throughout the day. We are able to examine the validity of the EMB model under the conditions when the number of buses is constant, and the mean passenger arrival rates can be described by a set of λj. E aggregated empirical trajectories (denoted as r2emp) (Figure 4, in blue) show a general trend of r2 increasing over time, especially for low initial r2. With exception of the r2emp(0) ∈ [0.8, 0.9) bin, the distance of all aggregated trajectories was found to be within the empirical horizon. is reflects a close correspondence between the dynamical behavior found in the empirical system and that captured by the EMB model
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