Analysis and Simulation of Intervention Strategies against Bus Bunching by means of an Empirical Agent‐Based Model

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.

Can bus bunching reduce waiting time?

“…Computing the average waiting time of the three cases with unrestricted capacity in the limit P = 0 in Eqs. ( 6), (11) and (17) we have:…”

“…Bus bunching is generally regarded as undesirable in bus systems [1][2][3][4][5][6][7][8][9][10][11][12][13]. As explained in Refs.…”

The complexity in transportation systems: the study of a bus loop

“…Bus bunching happens when two or more consecutive buses arrive at a bus stop at the same time, moving as a platoon. Without an active control [45,46,[51][52][53][54][55][56][57][58][59][60][61] or a specific design of bus stops [78][79][80][81][82][83][84]110], bunching is inevitable [43,45,46] and it causes longer waiting time and delays.…”

“…Buses are said to bunch if they travel in a platoon, one following the next. Bunched buses are common in real-world systems [46][47][48][49][50] and the bunched configuration is generally considered an inefficient setting that increases the waiting time of the commuters [45,46,[51][52][53][54][55][56][57][58][59][60][61]. Bus-bus interactions can also lead to inherent unpredictability in the schedule of buses [62][63][64][65][66][67],…”

“…While, to our knowledge, the literature in transportation engineering universally agrees that a staggered bus configuration offers better performance and lower waiting time as compared with a bunched configuration [45,46,[51][52][53][54][55][56][57][58][59][60][61], we present a new example in Chapter 3 of an empirically-inspired scenario in which bunched buses can outperform perfectly staggered buses in certain regimes. We consider a bus loop where a bus stop is connected to a train or mass rapid transit (MRT) station in which groups of passengers arrive periodically.…”