Assessing Bus Transport Reliability Using Micro-Simulation

Sorratini, José Aparecido; Liu, Ronghui; Sinha, Shalini

doi:10.1080/03081060802086512

Cited by 39 publications

(26 citation statements)

References 8 publications

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“…5, July 2011, 433Á442 (1990), Schiavone (1997) and Sawicki and Zak (2009), while repair scheduling and algorithms are discussed in Dutta et al (1986) and Haghami and Shafani (2002). Schedule reliability and its ties with maintenance are discussed in Guenthner and Sinha (1983), Dhillon and Rayapati (1984) and Sorratini et al (2008) and the computationally cumbersome topic of maintenance optimization is treated in Dekler (1996) and Maclean et al (2005). Finally, some specialized aspects of maintenance performance, inspection and vehicle retirement are discussed in Kavalec and Setiawan (1997) and Kazopoulo et al (2007).…”

Section: Introductionmentioning

confidence: 98%

Modeling transit vehicle repair duration and active service time

Karlaftis

2011

Transportation Planning and Technology

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The need for dependable and flexible models of transit vehicle maintenance has been well established in the literature as a means for improving daily operations, capital planning and service quality. Stemming from the practical need to predict the duration of maintenance activities and active service time for buses, this paper uses the principles of duration modeling to address two important questions: what is the duration of vehicle maintenance activities and, given that a bus is in active service, how long will it take? We extend previous work by including exogenous factors directly affecting maintenance duration and active service time in fully parametric duration models and examine such activities for the transit system in Athens (Greece). Results indicate that vehicle age, kilometers travelled and repair type are amongst the most important determinants of maintenance duration.

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Section: Introductionmentioning

confidence: 98%

Modeling transit vehicle repair duration and active service time

Karlaftis

2011

Transportation Planning and Technology

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“…Subsequently, due to the volatile nature of the system, any disparities in bus headways tend to increase over time, eventually resulting in bus bunching [1]. Due to bunching the periodicity of arrivals fail and homogeneous service cannot be maintained [2]. Bus bunching has a well-established literature and several authors proposed different methods to overcome its adverse effect.…”

Section: Introductionmentioning

confidence: 99%

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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“…Significant variations of demand (boarding and alighting) were detected both across bus stops and over time. Building their observed data into a microsimulation model of the bus corridor, Sorratini et al (2008) showed that the variability of the passenger demand distribution has the most significant impact on bus reliability measures.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Fonzone

Schmöcker

Liu

2015

Transportation Research Part C: Emerging Technologies

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If bus service departure times are not completely unknown to the passengers, non-uniform passenger arrival patterns can be expected. We propose that passengers decide their arrival time at stops based on a continuous logit model that considers the risk of missing services. Expected passenger waiting times are derived in a bus system that allows also for overtaking between bus services. We then propose an algorithm to derive the dwell time of subsequent buses serving a stop in order to illustrate when bus bunching might occur. We show that non-uniform arrival patterns can significantly influence the bus bunching process. With case studies we find that, even without exogenous delay, bunching can arise when the boarding rate is insufficient given the level of overall demand. Further, in case of exogenous delay, non-uniform arrivals can either worsen or improve the bunching conditions, depending on the level of delay. We conclude that therefore such effects should be considered when service control measures are discussed.

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Assessing Bus Transport Reliability Using Micro-Simulation

Cited by 39 publications

References 8 publications

Modeling transit vehicle repair duration and active service time

Modeling transit vehicle repair duration and active service time

Energy-aware predictive control for electrified bus networks

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

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