Version 1
: Received: 20 June 2024 / Approved: 20 June 2024 / Online: 21 June 2024 (13:10:48 CEST)
How to cite:
Gibas, J.; Pomykacz, J.; Baranowski, J. Bayesian Modelling of Travel Times on the Example of Food Delivery: Part 1 - Spatial Data Analysis and Processing. Preprints2024, 2024061457. https://doi.org/10.20944/preprints202406.1457.v1
Gibas, J.; Pomykacz, J.; Baranowski, J. Bayesian Modelling of Travel Times on the Example of Food Delivery: Part 1 - Spatial Data Analysis and Processing. Preprints 2024, 2024061457. https://doi.org/10.20944/preprints202406.1457.v1
Gibas, J.; Pomykacz, J.; Baranowski, J. Bayesian Modelling of Travel Times on the Example of Food Delivery: Part 1 - Spatial Data Analysis and Processing. Preprints2024, 2024061457. https://doi.org/10.20944/preprints202406.1457.v1
APA Style
Gibas, J., Pomykacz, J., & Baranowski, J. (2024). Bayesian Modelling of Travel Times on the Example of Food Delivery: Part 1 - Spatial Data Analysis and Processing. Preprints. https://doi.org/10.20944/preprints202406.1457.v1
Chicago/Turabian Style
Gibas, J., Jan Pomykacz and Jerzy Baranowski. 2024 "Bayesian Modelling of Travel Times on the Example of Food Delivery: Part 1 - Spatial Data Analysis and Processing" Preprints. https://doi.org/10.20944/preprints202406.1457.v1
Abstract
Online food delivery services are rapidly growing in popularity, making customer satisfaction critical for company success in a competitive market. Accurate delivery time predictions are key to ensuring high customer satisfaction. While various methods for travel time estimation exist, effective data analysis and processing are often overlooked. This paper addresses this gap by leveraging spatial data analysis and preprocessing techniques to enhance the data quality used in Bayesian models for predicting food delivery times. We utilized the OSRM API to generate routes that accurately reflect real-world conditions. Next, we visualized these routes using various techniques to identify and examine suspicious results. Our analysis of route distribution identified two groups of outliers, leading us to establish an appropriate boundary for maximum route distance to be used in future Bayesian modelling. The spatial analysis revealed that these outliers were primarily deliveries to the outskirts or beyond the city limits. By refining the data quality through these methods, we aim to improve the accuracy of delivery time predictions, ultimately enhancing customer satisfaction.
Keywords
food delivery services; travel time estimation; spatial analysis; data preprocessing; Bayesian modeling
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
