Predicting Vehicle Refuelling Trips through Generalised Poisson Modelling

Nithin Isaac and Akshay Kumar Saha
Additional contact information
Nithin Isaac: School of Engineering, Howard College Campus, University of KwaZulu-Natal, Durban 4041, South Africa
Akshay Kumar Saha: School of Engineering, Howard College Campus, University of KwaZulu-Natal, Durban 4041, South Africa

Energies, 2022, vol. 15, issue 18, 1-18

Abstract: This paper presents a model to predict the number of refuelling trips by vehicles on any given day considering weather conditions and time of the year. The predicted refuelling trips were founded on count-based data, i.e., data that contain events that occur at a certain rate. The paper presents an algorithm developed using Python programming language and the statsmodels module to achieve this. The results indicate that the GP-1 model developed in this paper is statistically significant at the 95% confidence level as it was able to converge—however, precipitation and high ambient temperature conditions are considered statistically insignificant in this model. The viability of the model was further tested on the remaining 20% of the data. Sensitivity tests indicate that there is a good correlation between the actual trips and predicted trips when 70% of the data are used to train the model. Overall, the model presented can be used to predict the number of trips taken by vehicles to refuel as well as model future trends, accurately. This model, can in the future, be applied to predict the refuelling behaviour of alternative fuel vehicles such as hydrogen fuel vehicles, when such data become available.

Keywords: Poisson probability; trip counts; prediction model; refuelling; weather (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/18/6616/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/18/6616/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:18:p:6616-:d:911506

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().