A model of bus bunching under reliability-based passenger arrival patterns

Fonzone, Achille; Schmöcker, Jan-Dirk; Liu, Ronghui

doi:10.1016/j.trc.2015.05.020

Cited by 50 publications

(13 citation statements)

References 28 publications

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“…Initially, six weighting strategies are introduced ( Figure 9 d) Passenger demand driven: on frequent lanes passengers usually do not consult the timetable [44]. In order to avoid bunching (causing increased waiting times, [3]) and minimize passenger waiting time. J = J hw + J p .…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Bus bunching has a well-established literature and several authors proposed different methods to overcome its adverse effect. [3] studied the effect of passenger arrival patterns on bunching, concluding that unexpected passenger demands and traffic disturbance are the root causes of bunching. Due to bunching the periodicity of arrivals fail and homogeneous service cannot be kept [2].…”

Section: Introductionmentioning

confidence: 99%

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Energy-aware predictive control for electrified bus networks

2019

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For an urban bus network to operate efficiently, conflicting objectives have to be considered: providing sufficient service quality while keeping energy consumption low. The paper focuses on energy efficient operation of bus lines, where bus stops are densely placed, and buses operate frequently with possibility of bunching. The proposed decentralized, bus fleet control solution aims to combine four conflicting goals incorporated into a multi-objective, nonlinear cost function. The multi-objective optimization is solved under a receding horizon model predictive framework. The four conflicting objectives are as follows. One is ensuring periodicity of headways by watching leading and following vehicles i.e. eliminating bus bunching. Equal headways are only a necessary condition for keeping a static, predefined, periodic timetable. The second objective is timetable tracking. A conflicting objective to the former is minimizing passenger waiting time. When more than the expected passengers are waiting for the bus it is desirable to haste the vehicle in order to prevent bunching. The final objective is energy efficiency. To this end, an energy consumption model is formulated considering battery electric vehicles with recuperation during braking. Different weighting strategies are compared and evaluated through realistic scenarios, realized in a validated microscopic traffic simulation environment. Simulation results suggest 3 − 8% network level energy saving compared to bus holding control while maintaining punctuality and periodicity of buses. Formulating the multi-objective cost functionBuses operate on a route based on their timetable. When the schedule is tight, their trajectory shall be carefully planned while considering the schedule, passenger demand, and energy efficiency.The proposed bus velocity control algorithm (referred hereafter as control algorithm) creates an optimal trajectory within a predefined prediction horizon, considering the schedule, the number of passengers waiting and the location of other buses. The trade-off in the optimization is the total energy consumed, which is modeled as a physical energy consumption model. During operation, due to irregular dwell times and traffic disturbances, they tend to get out of sync with the schedule and start bunching. The goal of the control algorithm is to ensure timetable and

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-aware predictive control for electrified bus networks

2019

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“…Therefore, the source of instability analyzed in this paper was any exogenous disruption that incremented the running time between two consecutive stops and the delays at signaled intersections. Here, the model can be further improved if stochastic passenger arrivals are considered, as presented in Bowman and Turnquist (1981) or Fonzone et al (2015).…”

Section: Discussionmentioning

confidence: 99%

“…This prediction is made considering that all variables are deterministic and known in advance (Delgado et al 2012) or even stochastic (Sáez et al, 2012;Cortés et al, 2010). Fonzone et al (2015) proposed bus overtaking at stops in order to accommodate better the waiting passengers in buses that didn't reach its vehicle capacity constraint.…”

Section: Introductionmentioning

confidence: 99%

Bus control strategies in corridors with signalized intersections

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Aymamí³

et al. 2016

Transportation Research Part C: Emerging Technologies

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This paper proposes a new dynamic bus control strategy aimed at reducing the negative effects of time-headway variations on route performance, based on real-time bus tracking data at stops. In routes with high demand, any delay of a single vehicle ends up causing an unstable motion of buses and producing the bus bunching phenomena. This strategy controls the cruising speed of buses and considers the extension of the green phase of traffic lights at intersections, when a bus is significantly delayed. The performance of this strategy will be compared to the current static operation technique based on the provision of slack times at holding points. An operational model is presented in order to estimate the effects of each controlling strategy, taking into account the vehicle capacity constraint. Control strategies are assessed in terms of passenger total travel time, operating cost as well as on the coefficient of headway variation. The effects of controlling strategies are tested in an idealized bus route under different operational settings and in the bus route of highest demand in Barcelona by simulation. The results show that the proposed dynamic controlling strategy reduces total system cost (user and agency) by 15–40% as well as the coefficient of headway variation 53–78% regarding the uncontrolled case, providing a bus performance similar to the expected when time disturbance is not presented.Peer ReviewedPostprint (author's final draft

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“…Therefore, it is no longer adequate to consider partial or deterministic measures in evaluating system performance. In recent years, the concept of reliability has become a valuable tool in transportation engineering (Arhin et al 2016;Fonzone et al 2015;Wang et al 2015). The reliability of bus travel time and road network needs to be introduced into the planning and management of the bus transit systems.…”

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confidence: 99%

Information entropy evaluation model of bus-line reliability considering the combination of bus stops and bus travel time

Lai

et al. 2022

Can. J. Civ. Eng.

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Evaluating the reliability of a bus line can provide useful information on the service performance of the city's bus transit system and the need for improvements. This paper presents a reliability evaluation model of a bus line based on information entropy. The model integrates the weighted bus-stop index and the variation index of bus travel time. To better determine the weights of the bus stop, six multiple indices were established based on three characteristics: passenger flow, bus stop properties, and the environment. The proposed model was applied to typical bus lines in Fuzhou, China. The results show that most of the bus stop reliabilities at the peak period were smaller than those at the off-peak period. Besides, for either the peak or off-peak period, the reliability shows a slight upward trend following a gradual decline after the first stop reflecting the influence of passenger flow, environment, and location factors generally.

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A model of bus bunching under reliability-based passenger arrival patterns

Cited by 50 publications

References 28 publications

Energy-aware predictive control for electrified bus networks

Energy-aware predictive control for electrified bus networks

Bus control strategies in corridors with signalized intersections

Information entropy evaluation model of bus-line reliability considering the combination of bus stops and bus travel time

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