Generalized Poisson Hidden Markov Model for Overdispersed or Underdispersed Count Data

George, Sebastian; Jose, Ambily

doi:10.15446/rce.v43n1.77542

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…The model's viability was tested on data, and sensitivity tests were conducted to assess its correlation with actual trips. In the Generalized Poisson (GP-1) model, linear regression analysis is used to model and predict count-based data [36]. Gaussian or normal distribution is a common choice when the data exhibit continuous and symmetrical characteristics.…”

Section: Comparative Analysismentioning

confidence: 99%

Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

Isaac,

Saha

2023

Sustainability

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This paper presents an analysis of two methodologies that can be used to predict refueling behavior. Both models aim to provide insights into hydrogen-fuel vehicle users’ refueling patterns and behaviors. The first model leverages probabilistic transitions between refueling states to simulate and predict the refueling behavior of hydrogen vehicle users. In contrast, the GP-1 model employs Gaussian processes to capture the underlying patterns and uncertainties in hydrogen-fuel vehicle refueling behavior, taking into consideration additional factors such as weather conditions and the time of the year. The model demonstrates statistical significance and accuracy in predicting trips while identifying the insignificance of precipitation and high ambient temperatures. The methodologies, findings, strengths, and limitations of the two models were tested and compared to identify their relevant contributions. By contrasting their methodologies and evaluating their predictive performance, using performance metrics such as accuracy, precision, and recall values, this study provides valuable insights into the strengths and limitations of each approach. Limitations include assuming a stationary refueling process and excluding external factors and limitations related to data availability, as well as the absence of a specific focus on hydrogen-fuel vehicles. By understanding the differences and similarities between these two models, this paper aims to provide a unique perspective on gaps and further requirements for accurate prediction and modeling of refueling behavior to guide policymakers, infrastructure planners, and stakeholders in making informed decisions regarding the design and optimization of hydrogen refueling infrastructure.

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Section: Comparative Analysismentioning

confidence: 99%

Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

Isaac,

Saha

2023

Sustainability

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“…The model aims to forecast refuelling trips based on weather and time of year [27,28,30]. This study contributes to the existing literature by selecting and designing an effective SML predictive model that can predict HFV refuelling behaviour in relation to weather and time of year (day of week, month) [31]. The development of models for HFVs is essential because of the scarcity of data specific to these vehicles [3,13,18,32].…”

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confidence: 99%

Forecasting Hydrogen Vehicle Refuelling for Sustainable Transportation: A Light Gradient-Boosting Machine Model

Isaac,

Saha

2024

Sustainability

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Efficiently predicting and understanding refuelling patterns in the context of HFVs is paramount for optimising fuelling processes, infrastructure planning, and facilitating vehicle operation. This study evaluates several supervised machine learning methodologies for predicting the refuelling behaviour of HFVs. The LightGBM model emerged as the most effective predictive model due to its ability to handle time series and seasonal data. The selected model integrates various input variables, encompassing refuelling metrics, day of the week, and weather conditions (e.g., temperature, precipitation), to capture intricate patterns and relationships within the data set. Empirical testing and validation against real-world refuelling data underscore the efficacy of the LightGBM model, demonstrating a minimal deviation from actual data given limited data and thereby showcasing its potential to offer valuable insights to fuelling station operators, vehicle manufacturers, and policymakers. Overall, this study highlights the potential of sustainable predictive modelling for optimising fuelling processes, infrastructure planning, and facilitating vehicle operation in the context of HFVs.

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Machine Learning for Metabolic Networks Modelling: A State-of-the-Art Survey

Biba,

Vajjhala

2022

Studies in Big Data

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Generalized Poisson Hidden Markov Model for Overdispersed or Underdispersed Count Data

Cited by 4 publications

References 13 publications

Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

Analysis of Refueling Behavior Models for Hydrogen-Fuel Vehicles: Markov versus Generalized Poisson Modeling

Forecasting Hydrogen Vehicle Refuelling for Sustainable Transportation: A Light Gradient-Boosting Machine Model

Machine Learning for Metabolic Networks Modelling: A State-of-the-Art Survey

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