Dynamic bus substitution strategy for bunching intervention

Petit, Antoine; Ouyang, Yanfeng; Lei, Chuanhu

doi:10.1016/j.trb.2018.06.001

Cited by 52 publications

(26 citation statements)

References 18 publications

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“…Empirical observation during field work found that this does not reflect the average motion of buses. Further examination on the velocity distribution at single sites (as observed in Figure 16) also shows the unrealistic propensity of zero 22 Complexity velocities occurring in the middle of a long stretch of road. A plausible explanation for this artefact is that the positioning data sent by the buses was interrupted for a significant period of time while the bus was in motion.…”

Section: Appendixmentioning

confidence: 96%

“…Bus bunching occurs when the distance between two or more buses reduces to near zero. In traffic studies [1][2][3][4][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], bus bunching is often examined through the headway between two buses, i.e., the forward distance or time of a vehicle to its leading vehicle. Bunching occurs naturally as a consequence of the interaction between buses and passengers [2,4,5] after discounting the effects of other traffic.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Appendixmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…With the increasing congestion on urban roads and the scarcity of the available land for road construction, more and more cities are seriously considering changing the mode of travel. Public transport is a cost-effective transit mode which is highly recommended by authorities and researchers [1][2][3][4][5][6]. Improving the service levels of public transits would encourage more citizens to choose public transport for their travels instead of private vehicles, which will alleviate traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

Novel Design Method for Bus Approach Lanes with Bus Guidance and Priority Controls for Prioritizing Through and Left-Turn Buses

Shu

Zhao

Han

2019

Journal of Advanced Transportation

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Transit priority is a useful way of improving transit operations in urban networks. However, the through and left-turn buses are rarely prioritized simultaneously at isolated intersections in the existing studies. This paper presents a variable bus approach lane design with a bus guidance and priority control model, which can reduce the delay of both the through and left-turn buses. The variable bus approach lanes can be dynamically used for the through and left-turn buses during the various periods of a signal cycle by the integrated design of geometric layouts and signal timing. A detailed bus guidance and priority control optimization model is formulated to guide the buses entering the appropriate bus approach lanes, and it provides optimal signal priorities for buses. The effectiveness of the proposed method is validated by a case study and numerical experiments. The results show that, on average, the total passenger delay can be reduced by 5% for every 30 veh/h and 40 veh/h increase in the volume of through buses and left-turn buses, respectively. Moreover, a comparison between the proposed method and the conventional transit priority method reveals that significant improvements can be made in reducing delays using the proposed method even at intersections with high degree of the saturation.

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“…Since the 1960s, the scientific community has noted a longstanding problem with buses: the pervasiveness of bus bunching [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][98][99][100]. Bus bunching occurs when the headway of two or more buses reduces to zero, where in traffic studies, headway is defined as the forward distance or time of vehicle to its leading vehicle.…”

Section: Chaptermentioning

confidence: 99%

“…In Chapters 3, 4 and 5 we address a longstanding problem of the bus system: the pervasiveness of bus bunching. While there have been numerous studies on solving the bus bunching problem [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60], most of which does not consider the specific bus dynamics. In a bus system, the effects of bus dynamics and passenger boarding are not independent of each other.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

From cellular automata to agent-based models : addressing urban issues

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Dynamic bus substitution strategy for bunching intervention

Cited by 52 publications

References 18 publications

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

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

Novel Design Method for Bus Approach Lanes with Bus Guidance and Priority Controls for Prioritizing Through and Left-Turn Buses

From cellular automata to agent-based models : addressing urban issues

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