Using simulation model as a tool for analyzing bus service reliability and implementing improvement strategies

Moosavi, Seyed Mohammad Hossein; Ismail, Amiruddin; Yuen, Choon Wah

doi:10.1371/journal.pone.0232799

Cited by 13 publications

(10 citation statements)

References 65 publications

(66 reference statements)

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“…However, not all technical measures can provide stable transit services because passenger behaviour influences the service quality of bus systems [85]. erefore, scholars have considered a series of instances when exploring on-time performance, which has a strong relationship with bus timetables [86]. As a case of bus holding control, time control point rules that were applied in a previous study to evaluate network reliability were also used to solve dynamic engineering problems [23,[87][88][89][90].…”

Section: Operational Strategy For Bus System Servicesmentioning

confidence: 99%

Reliability Analysis of Bus Timetabling Strategy during the COVID-19 Epidemic: A Case Study of Yixing, China

Gao

Zheng

et al. 2021

Discrete Dynamics in Nature and Society

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How to meet the daily demand for resident transport while limiting the transmission of infectious diseases is a problem of social responsibility of urban transport systems during major public health emergencies. Considering the novel coronavirus pneumonia epidemic (COVID-19), a bus timetable system based on the “if early, wait, and if late, leave soon” strategy is proposed. Based on public transport vehicle constraints in this system, the concept of reliability is introduced to evaluate public transport timetable systems, and a model based on an event tree is built to calculate the failure rate of urban bus timetables. Then, the public transport situation in Yixing city is used as an example to perform confirmatory analysis, and the fluctuations in the reliability of the bus timetable during the novel coronavirus pneumonia epidemic are discussed. The research results show that the method proposed in this paper can obtain the overall failure rate of urban bus timetable by traversing the calculation of each round-trip interval and achieve an accurate evaluation of the reliability of bus timetables. During the early, middle, and more recent stages of the COVID-19 outbreak, the failure rate of bus timetables in Yixing city initially decreased and then increased. In the early stage of the outbreak, the failure rate of the Yixing bus timetable was 7.8142. However, the failure rate decreased to 4.3306 and 5.0160 in the middle and late stages of the epidemic, respectively. In other words, the failure rate of the public transport network in the middle and late stages decreased by 44.58% and 35.81%, respectively, compared with that in the early stage. Thus, during major health emergencies, such as the novel coronavirus pneumonia outbreak, the reliability of the urban bus timetable system can be improved by at least 35%, and cross-infection at bus stations can be prevented. The research results verify the feasibility and reliability of the implementation of bus timetabling strategies during major health emergencies.

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Section: Operational Strategy For Bus System Servicesmentioning

confidence: 99%

Reliability Analysis of Bus Timetabling Strategy during the COVID-19 Epidemic: A Case Study of Yixing, China

Gao

Zheng

et al. 2021

Discrete Dynamics in Nature and Society

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“…In addition, simulations have been less used techniques in modeling transportation systems, especially shared systems. A simulation model of bus service has been developed in the paper (Moosavi et al 2020) to fill the gap of PT reliability. This research has intended to cover both passengers' and operators' perceptions of reliability: waiting time, on-board crowding level from the passengers' perspective, and so on.…”

Section: Simulation Approachmentioning

confidence: 99%

On understanding the impacts of shared public transportation on urban traffic and road safety using an agent-based simulation with heterogeneous fleets: a case study of Casablanca city

et al. 2022

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Simulation and computer modeling have a key role in understanding transportation systems. Focusing on the main system, real-time retrieval of outputs based on mutual interactions of the whole autonomous entities makes the agent-based simulation very promising. This paper deals with an agent-based simulation to investigate and evaluate the potential impacts of implementing Shared Public Transports (SPT) in urban areas. Such a system is intended to integrate the two flows of passengers and containerized freights in Public Transportation (PT) patterns towards more sustainable, efficient, and socially suitable mobility. The proposed model is coupled with a stochastic process in order to provide a range of real-world data of Casablanca city (Morocco) based on institutional surveys. In this respect, two urban transportation systems of freight are tested: (1) the conventional transportation system, (2) the SPT system with heterogeneous fleets. In an effort to sustain efficient and safe movements, this paper examines SPT performances according to a set of key evaluation metrics. Results show that PT stopping remains the most relevant factor when evaluating metrics of the number of waiting containers and waiting time of demand by rates of 82.440% and 62.580%, respectively. Such a waiting containers metric is significantly affected by the volume of demand to transport per time slot by a rate of 78.140%. Under SPT, traffic congestion is the main factor to consider in managing PT with a rate of 65.690% in order to reduce potential accidents. However, demand volume could increase the on-street illegal parking metric by 90.070%. More details are provided below.

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“…The framework we used to develop simulation model is based on our previous study [67], and we added sensitivity analysis and adopting suitable strategy based on sensitivity results to our simulation model. Figure 3 presents the activity diagram of the simulation algorithm.…”

Section: B Simulation Model Concept and Architecture Designmentioning

confidence: 99%

“…In this study, four different indicators of bus service reliability were selected based on the results reported in [67], [69] regarding the bus service reliability. According to these comprehensive studies, four reliability indicators were selected to cover the passengers and agencies" perspectives of reliability: Waiting time, on-board crowding from passengers" perspective, Headway Regularity Index at Stops (HRIS), and Bunching/Big Gap percentage from operators" perspective.…”

Section: G Measuring Bus Service Reliabilitymentioning

confidence: 99%

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

et al. 2020

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Service quality is a significant concern for both providers and users of public transportation. It is crucial for transit agencies to clearly recognize the causes of unreliability before adapting any improvement strategy. However, evaluation of main causes of bus service unreliability has not been investigated well. Existing studies have three main limitations in context of recognizing causes of service unreliability. First, public transport networks and traffic condition are highly complex systems and most of the existing models are not capable to accurately determine the relationship between service irregularity and impact factors. Second, definition of "Big data" has been neglected and most of the studies only focused on one source of large scale data set to determine the causes of unreliability. Third, bus service unreliability can impact the users" perception toward the public transport, significantly. It has been recommended by number of studies that bus service reliability should be evaluated from both service providers" and users" perspective. However, the impact of service unreliability from passengers" perception is not well investigated, yet. Consequently, we proposed a novel simulation-based sensitivity analysis to evaluating main causes of bus service unreliability using a combination of three different sources of big data. Moreover, for the first time we developed a simulation model in R studio which is an open source and powerful coding environment. According to the results, the level of reliability in Route U32 showed the highest sensitivity to headway variations. Waiting time can be decreased by 61% if only bus operators can reduce the headway variation by 25% of the actual observed data. Big gap and bus bunching could be almost disappeared by decreasing headway variations. Moreover, the terminal departure policy could significantly improve the passenger waiting time. Waiting time can be decreased by 36% when almost all the buses depart the terminal on-time.

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Using simulation model as a tool for analyzing bus service reliability and implementing improvement strategies

Cited by 13 publications

References 65 publications

Reliability Analysis of Bus Timetabling Strategy during the COVID-19 Epidemic: A Case Study of Yixing, China

Reliability Analysis of Bus Timetabling Strategy during the COVID-19 Epidemic: A Case Study of Yixing, China

On understanding the impacts of shared public transportation on urban traffic and road safety using an agent-based simulation with heterogeneous fleets: a case study of Casablanca city

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

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