Neural Models for Benchmarking of Truck Driver Fuel Economy Performance

Alwyn J. Hoffman

2019

Abstract

The transport industry is a primary contributor towards emissions that impact climate change. Fuel economy is also of critical importance to the profitability of road freight transport operators. Empirical evidence identified a variety of factors impacting fuel consumption, including route inclination, payload and truck driver behaviour. This creates the need for accurate fuel usage models and objective methods to distinguish the impact of drivers from other factors, in order to enable reliable driver performance assessment. We compiled a data set for 331 drivers completing 7332 trips over 21 routes to obtain evidence of the impact of route, payload and driver behaviour on fuel economy. We then extracted various regression and neural models for fuel economy and used these models to remove the impact of route inclination and payload, allowing the impact of driver performance to be measured more accurately. All models demonstrated significant out-of-sample predictive ability. Neural models in general outperformed regression models, while amongst neural models radial basis models slightly outperformed multi-layer perceptron models. The significance of compensating for factors not controlled by the driver was verified by demonstrating large differences in driver performance ranking before and after compensating for route inclination and payload.

Paper Citation

in Harvard Style

Hoffman A. (2019). Neural Models for Benchmarking of Truck Driver Fuel Economy Performance. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA; ISBN 978-989-758-384-1, SciTePress, pages 379-390. DOI: 10.5220/0008065703790390

in Bibtex Style

@conference{ncta19,
author={Alwyn J. Hoffman},
title={Neural Models for Benchmarking of Truck Driver Fuel Economy Performance},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA},
year={2019},
pages={379-390},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008065703790390},
isbn={978-989-758-384-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - Volume 1: NCTA
TI - Neural Models for Benchmarking of Truck Driver Fuel Economy Performance
SN - 978-989-758-384-1
AU - Hoffman A.
PY - 2019
SP - 379
EP - 390
DO - 10.5220/0008065703790390
PB - SciTePress