Measuring fuel consumption in vehicle routing: new estimation models using supervised learning

Abstract

In this paper we propose and assess the accuracy of new fuel consumption estimation models for vehicle routing. Based on real-world shipping operation data consisting of instantaneous fuel consumption, time-varying speeds observations, and highfrequency traffic, we propose effective methods to estimate fuel consumption. By carrying out nonlinear regression analysis using supervised learning methods, namely Neural Networks, Support Vector Machines, Conditional Inference Trees, and Gradient Boosting Machines, we develop new models that provide better prediction accuracy than classical models. We correctly estimate consumption for time-dependent point-to-point routing under realistic conditions taking into account freight transportation operations during peak hour traffic congestion, stop-and-go driving patterns, idle vehicle states, and the variation of vehicle loads. Our methods provide a more precise alternative to classical regression methods used in the literature, as they are developed for a specific situation (fleet, drivers, geography, etc). Extensive computational experiments under realistic conditions show the effectiveness of the proposed machine learning consumption models, clearly outperforming macroscopic and microscopic consumption models such as the Comprehensive Modal Emissions Model (CMEM) and the Methodology for Estimating air pollutant Emissions from Transport (MEET). Based on sensitivity analyses we show that MEET underestimates real-world consumption by 24.94% and CMEM leads to an overestimation of consumption by 7.57% with optimized parameters. Our best machine learning model (Gradient Boosting Machines) exhibited superior estimation accuracy with a gap of only 1.70%.

Plug-in hybrid electric refuse vehicle routing problem for waste collection

“…Heni et al (2019) developed the traditional CMEM by considering both fixed speeds or different speeds over time for each arc. Recently, Heni et al (2021) provided effective machine learning tools to estimate fuel consumption by considering more realistic conditions than traditional CMEM, such as the time-varying speed and traffic frequency. Following this recent literature, we consider all the main factors, including vehicle speed, acceleration, deceleration, road gradient, environment, and traffic-related factors, as discussed in Demir et al (2014).…”

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

“…Machine learning is a classic data-driven fuel consumption prediction method [30] that includes support vector machine (SVM), random forest (RF), decision tree (DL), etc. For example, Heni et al [31] used SVM and gradient boosting machines to perform nonlinear regression analysis on data, and a large number of experiments based on real conditions have demonstrated the superiority of machine learning methods in fuel consumption prediction models. Hamed et al [5] established a functional relationship between vehicle speed and fuel consumption based on a support vector machine, with a R 2 of up to Machine learning is a classic data-driven fuel consumption prediction method [30] that includes support vector machine (SVM), random forest (RF), decision tree (DL), etc.…”

“…Hamed et al [5] established a functional relationship between vehicle speed and fuel consumption based on a support vector machine, with a R 2 of up to Machine learning is a classic data-driven fuel consumption prediction method [30] that includes support vector machine (SVM), random forest (RF), decision tree (DL), etc. For example, Heni et al [31] used SVM and gradient boosting machines to perform nonlinear regression analysis on data, and a large number of experiments based on real conditions have demonstrated the superiority of machine learning methods in fuel consumption prediction models. Hamed et al [5] established a functional relationship between vehicle speed and fuel consumption based on a support vector machine, with a R 2 of up to 0.97.…”

Optimal Fuel Consumption Modelling, Simulation, and Analysis for Hybrid Electric Vehicles

“…A case study for HEV energy consumption modelling and estimation is presented in [23], where a driver model was set up to control the vehicle and represent human behaviour. Lastly, a proposal to accurately estimate the fuel consumption of HEVs, depending on real-world data consisting of instantaneous energy consumption, time variation in speed, and high-frequency traffic, was presented in [24].…”