A New Stochastic Model for Bus Rapid Transit Scheduling with Uncertainty

Filabadi, Milad Dehghani; Asadi, Afshin; Giahi, Ramin; Ardakani, Ali Tahanpour; Azadeh, A.

doi:10.3390/futuretransp2010009

Cited by 8 publications

(5 citation statements)

References 39 publications

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“…As elaborated on in Charandabi & Kamyar (2021C), the high volatility of cryptocurrency prices roots from maximal trading, that stems from uncertainty. Decision-making under under unforeseen events has been the focus of research in the last few decades (Asadi et al, 2022). More recently, (Dehghani Filabadi 2019), Filabadi (2022) proposed general mathematical models for addressing uncertainties in environments where data is sensitive to prediction errors.…”

Section: Literature Reviewmentioning

confidence: 99%

Cryptocurrency Price Prediction with Neural Networks of LSTM and Bayesian Optimization

Pour

Jafari²,

Lashgari

et al. 2022

EJBMR

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In this paper we present a price prediction for Bitcoin prices. The methodology used is a hybrid artificial neural network model of Long Short-Term Memory and Bayesian Optimization. This is a complex model with a high prediction power, which to our knowledge has not been applied to prediction of cryptocurrency prices to date. Following Charandabi and Kamyar (2021), we elaborate on previous methods used for prediction of cryptocurrency prices and build on their methodology. We conclude with detailed graphs and tables of optimization results.

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Section: Literature Reviewmentioning

confidence: 99%

Cryptocurrency Price Prediction with Neural Networks of LSTM and Bayesian Optimization

Pour

Jafari²,

Lashgari

et al. 2022

EJBMR

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“…The model increases the operational efficiency of BRT vehicles by 10% by reducing travel time. Filabadi et al [25] studied the perspective of a BRT controller and developed a stochastic mixed-integer nonlinear programming (MINLP) model for BRT scheduling of commuter waiting periods and commuter journey times in order to reduce total related time of commuters. Sevim et al [26] explored the scheduling of Istanbul's Metrobus fleet BRT system.…”

Section: Introductionmentioning

confidence: 99%

Linear programming model for scheduling bus rapid transit in Lagos State, Nigeria

Mogbojuri,

Olanrewaju,

Adeyeye

2023

Nig. J. Tech.

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The amount of time users have to wait influences their mode of transportation choice. Commuters don’t like to wait in the bus station, especially when the weather is terrible and for the purpose of keeping appointments. Scheduling buses to the various bus terminals will meet the commuter’s needs. The aim of the study is to reduce the waiting time of commuters at bus station and to assign buses to each route. A mathematical model called linear programming (LP) was developed to schedule buses to improve the smooth process of the Bus Rapid Transit (BRT) system. The linear programming model was applied to the Lagos Metropolitan Area Transport Authority (LAMATA) data in Lagos State. The proposed method generated three (3) shifts from 6 – 11am in the morning, 11 – 4pm in the midday and 4 – 9pm in the evening subject to two (2) shifts of fifteen (15) hours to reduce the number of hours buses operate per day and also allocated buses for each route for weekdays with five (5) hours per each shift instead of seven (7) hours buses operated per shift. A properly implemented strategy would significantly decrease passengers' long waits at bus stops, reduce the breakdown of buses, man-hours lost, and allows commuters to meet their various schedule for works, meetings, and other assignments.

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“…In recent years, various prediction methods have been proposed to increase the prediction accuracy [22][23][24][25]. However, prediction errors are inevitable and can lead to severe problems in highly sensitive applications [26]. To overcome wind power uncertainties, such uncertainties must be considered in OPF so that the solution is robust under variations of wind power availability.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the adjustable budget of uncertainty method [28] adjusts the solution degree of conservatism by changing the total amount of uncertainty in the model, and the total amount of uncertainty is modeled using a parameter called the budget of uncertainty. This method has been extensively studied [9,12,[23][24][25][26][27][28][29][30][31]. Alternatively, two-stage RO models [5,26,[32][33][34][35][36][37] have been used in power systems, where the first stage decisions are made before realizing the actual wind power, and the second stage decisions are "wait-and-see" decisions that can be adjusted after the actual wind power output is known.…”

Section: Introductionmentioning

confidence: 99%

“…This method has been extensively studied [9,12,[23][24][25][26][27][28][29][30][31]. Alternatively, two-stage RO models [5,26,[32][33][34][35][36][37] have been used in power systems, where the first stage decisions are made before realizing the actual wind power, and the second stage decisions are "wait-and-see" decisions that can be adjusted after the actual wind power output is known. Such two-stage RO models are computationally complex and difficult to implement in large power systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in the Optimal Power Flow Problem

Salama¹,

Khoshkhoo²,

Fakhrabadi³

et al. 2022

J Energ Power Technol

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Integrating large-scale wind energy in modern power systems necessitates high-efficiency mathematical models to address classical assumptions in power systems. In particular, two main assumptions for wind energy integration in power systems have not been adequately studied. First, nonlinear AC power flow equations have been linearized in most of the literature. Such simplifications can lead to inaccurate power flow calculations and result in technical issues. Second, wind power uncertainties are inevitable and have been mostly modeled using traditional uncertainty modeling techniques, which may not be suitable for large-scale wind power integration. In this study, we addressed both challenges: we developed a tight second-order conic relaxation model for the optimal power flow problem and implemented the novel effective budget of uncertainty approach for uncertainty modeling to determine the maximum wind power admissibility and address the uncertainty in the model. To the best of our knowledge, this is the first study that proposes an effective, robust second-order conic programming model that simultaneously addresses the issues of power flow linearization and wind power uncertainty with the new paradigm on the budget of uncertainty approach. The numerical results revealed the advantages of the proposed model over traditional linearized power flow equations and traditional uncertainty modeling techniques.

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A New Stochastic Model for Bus Rapid Transit Scheduling with Uncertainty

Cited by 8 publications

References 39 publications

Cryptocurrency Price Prediction with Neural Networks of LSTM and Bayesian Optimization

Cryptocurrency Price Prediction with Neural Networks of LSTM and Bayesian Optimization

Linear programming model for scheduling bus rapid transit in Lagos State, Nigeria

A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in the Optimal Power Flow Problem

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