Environmental effects of driving style: impact on fuel consumption

Lasocki, Jakub; Boguszewski, Karol

doi:10.1051/e3sconf/201910000043

Cited by 7 publications

(13 citation statements)

References 19 publications

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“…Through these linear models, they provide the relative importance for each of the features in the fuel consumption, as well as the r2 value for each of the models tested in order to evaluate them. Similarly, in [20] the authors study the impact on the fuel of several features inferred related to driving behaviour through the analysis of the data from two different vehicles. One of these features is the Driving Style Indicator (DSI), which is the difference between the average positive acceleration of a vehicle minus the average of the negative acceleration divided by the average speed.…”

Section: Machine Learning For Connecting Input Features To Vehicle Fu...mentioning

confidence: 99%

Vehicle Fuel Optimization Under Real-World Driving Conditions: An Explainable Artificial Intelligence Approach

Barbado,

Corcho

2021

Preprint

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Funding informationFuel optimization of diesel and petrol vehicles within industrial fleets is critical for mitigating costs and reducing emissions. This objective is achievable by acting on fuel-related factors, such as the driving behaviour style.In this study, we developed an Explainable Boosting Machine (EBM) model to predict fuel consumption of different types of industrial vehicles, using real-world data collected from 2020 to 2021. This Machine Learning model also explains the relationship between the input factors and fuel consumption, quantifying the individual contribution of each one of them. The explanations provided by the model are compared with domain knowledge in order to see if they are aligned. The results show that the 70% of the categories associated to the fuel-factors are similar to the previous literature.With the EBM algorithm, we estimate that optimizing driving behaviour decreases fuel consumption between 12% and 15% in a large fleet (more than 1000 vehicles).

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Section: Machine Learning For Connecting Input Features To Vehicle Fu...mentioning

confidence: 99%

Vehicle Fuel Optimization Under Real-World Driving Conditions: An Explainable Artificial Intelligence Approach

Barbado,

Corcho

2021

Preprint

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“…Yao et al (2020) utilised Pearson correlation as feature selection by analysing Pearson correlation between driving behaviour and fuel consumption. Lasocki and Boguszewski (2019) studied the impact of different driving styles on the fuel economy and introduced a new metric termed driving style indicator (DSI). DSI measures ratio of difference in average positive and negative acceleration to average vehicle velocity of the vehicle.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Limitations observed in existing approaches are as follows:-Models (Rios-Torres et al , 2019; Yao et al , 2020; Lasocki and Boguszewski, 2019; Hoffman and van der Westhuizen, 2019) used average or maximum values of different predictor parameters for the prediction of fuel consumption. The proposed approach computes driving behaviour parameters for a sub-trip, which helps to compute fine-grained fuel consumption.Many approaches do not study environmental parameters (Yao et al , 2020), missing traffic conditions (Boggio-Marzet et al , 2021; Magaña and Munoz-Organero, 2015), missing road type (Rios-Torres et al , 2019; Lasocki and Boguszewski, 2019; Hoffman and van der Westhuizen, 2019) and missing time of travel (Yao et al , 2020). Most of the works (Ozkan and Yao, 2021; Lasocki and Boguszewski, 2019) study fuel consumption for specific scenarios such as car-following and lane-changing scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The proposed approach computes driving behaviour parameters for a sub-trip, which helps to compute fine-grained fuel consumption.Many approaches do not study environmental parameters (Yao et al , 2020), missing traffic conditions (Boggio-Marzet et al , 2021; Magaña and Munoz-Organero, 2015), missing road type (Rios-Torres et al , 2019; Lasocki and Boguszewski, 2019; Hoffman and van der Westhuizen, 2019) and missing time of travel (Yao et al , 2020). Most of the works (Ozkan and Yao, 2021; Lasocki and Boguszewski, 2019) study fuel consumption for specific scenarios such as car-following and lane-changing scenarios. The proposed model provides a generic framework to optimise fuel consumption.Many existing linear regression models (Gadde et al , 2019; Yao et al , 2020) assume a linear relationship between response and predictor variables.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many approaches do not study environmental parameters (Yao et al , 2020), missing traffic conditions (Boggio-Marzet et al , 2021; Magaña and Munoz-Organero, 2015), missing road type (Rios-Torres et al , 2019; Lasocki and Boguszewski, 2019; Hoffman and van der Westhuizen, 2019) and missing time of travel (Yao et al , 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

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A machine learning pipeline for fuel-economical driving model

Jain

Mittal

2021

IJICC

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PurposeA cost-effective way to achieve fuel economy is to reinforce positive driving behaviour. Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy. Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration. A single-step application of machine learning (ML) is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy. The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.Design/methodology/approachThis research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars. The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data, and the second step detects abnormal fuel economy in relation to contextual information. Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model. The contextual anomaly is detected by following two approaches, kernel quantile estimator and one-class support vector machine. The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour. Any error beyond a threshold is classified as an anomaly. The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection. The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder, and the performance of both models is compared. The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.FindingsA composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder. Both models achieve prediction accuracy within a range of 98%–100% for prediction as a first step. Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%–100%, whereas the one-class support vector machine approach performs within the range of 99.3%–100%.Research limitations/implicationsThe proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns. However, it can be extended to correlate driver's physiological state such as fatigue, sleep and stress to correlate with driving behaviour and fuel economy. The anomaly detection approach here is limited to providing feedback to driver, it can be extended to give contextual feedback to the steering controller or throttle controller. In the future, a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.Practical implicationsThe suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts. It can also be used as a training tool for improving driving efficiency for new drivers. It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.Originality/valueThis paper contributes to the existing literature by providing an ML pipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values. The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy. The main contributions for this approach are as follows: (1) a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption. (2) Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

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