Bayesian Modelling of Travel Times on the Example of Food Delivery: Part 2 - Model Creation and Handling Uncertainty

Abstract

The e-commerce sector is in a constant state of growth and evolution, particularly within its subdomain of online food delivery. As such, ensuring customer satisfaction is critical for companies working in this field. One way to achieve this is by providing accurate delivery time estimation. While companies can track couriers via GPS, they often lack real-time data on traffic and road conditions, complicating delivery time predictions. To address this, a range of statistical and machine learning techniques are being employed, including neural networks and specialized expert systems, with different degree of success. One issue with neural networks and machine learning models is their heavy dependence on vast, high quality data. To mitigate this issue we propose two Bayesian generalized linear models to predict time of the delivery. Utilizing a linear combination of predictor variables, we generate practical range of outputs with Hamiltonian Monte Carlo sampling method. These models offer a balance of generality and adaptability, allowing for tuning with expert knowledge. They were compared with PSIS-LOO and WAIC criteria. Results show that both models accurately estimated delivery times from the dataset, while maintaining numerical stability. Model with more predictor variables proved to be more accurate.