Combined influence of traffic conditions, driving behavior, and type of road on fuel consumption. Real driving data from Madrid Area

Boggio-Marzet, Alessandra; Monzón, Andrés; Rodríguez-Alloza, Ana María; Wang, Yang

doi:10.1080/15568318.2020.1871128

Cited by 16 publications

(18 citation statements)

References 48 publications

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“…In Ozkan and Yao (2021), fuel consumption of vehicles was predicted as per driving behaviour and vehicle dynamics. Boggio-Marzet et al (2021) examined the mixed effect of traffic congestion, road type and driving behaviour on fuel consumption and found good correlation among these attributes. Ping et al (2019) suggested a two-level macroscopic model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Few approaches referred by (Gadde et al , 2019; Hsu et al , 2017; Boggio-Marzet et al , 2021; Liu et al , 2016; Magaña and Munoz-Organero, 2015; Mubarak and Al-Samari, 2020; Sarkan et al , 2019) considered engine rotation per minute (RPM), cruise time, engine idle time as parameters. Few approaches considered the effect of socio-demographics attributes as done by (Mubarak and Al-Samari, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…It was demonstrated that driving style is correlated with fuel consumption, and an efficient driving style can result in fuel saving of 15%–20%. NNs (Boggio-Marzet et al , 2021; Meseguer et al , 2017) suffer from vanishing gradient problem and are not able to retain long-term information about some attributes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Visibility at night might affect driving adversely. These surrounding conditions affect vehicle speed and driving behaviour to a great extent (Boggio-Marzet et al , 2021; Yao et al , 2020). Fuel economy, on the other hand, varies with speed and driving behaviour.…”

Section: Introductionmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A machine learning pipeline for fuel-economical driving model

Jain

Mittal

2021

IJICC

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“…When deployed, all the modes are integrated and adapt changes to the vehicle functionality, but none intervenes with the driver behavior vector (DBV) (speed, longitudinal acceleration [LOT], lateral acceleration [LAT], yaw rate [YAR], and cabin air temperature [CAT]). The DBV is the user's prerogative in real-time, and it is known that DBV holds more than a 30% share in affecting vehicle engine performance (Boggio-Marzet et al, 2021). Hence, in this research, a novel "intelligent vehicle drive mode" (IVDM) was proposed, which correlates to type 3 feature, which predicts the DBV by obliging the user's command and inputs in real-time to augment the vehicle engine performance.…”

Section: Introductionmentioning

confidence: 99%

Intelligent vehicle drive mode which predicts the driver behavior vector to augment the engine performance in real-time

Kolachalama

Malik

2022

DCE

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In this article, a novel drive mode, “intelligent vehicle drive mode” (IVDM), was proposed, which augments the vehicle engine performance in real-time. This drive mode predicts the driver behavior vector (DBV), which optimizes the vehicle engine performance, and the metric of optimal vehicle engine performance was defined using the elements of engine operating point (EOP) and heating ventilation and air conditioning system (HVAC). Deep learning (DL) models were developed by mapping the vehicle level vectors (VLV) with EOP and HVAC parameters, and the trained functions were utilized to predict the future states of DBV reflecting augmented vehicle engine performance. The iterative analysis was performed by empirically estimating the future states of VLV in the allowable range of DBV and was fed into the DL model to predict the performance vectors. The defined vehicle engine performance metric was applied to the predicted vectors, and thus optimal DBV is the instantaneous output of the IVDM. The analytical and validation techniques were developed using field data obtained from General Motors Inc., Warren, Michigan. Finally, the proposed concept was quantified by analyzing the instantaneous engine efficiency (IEE) and smoothness measure of the instantaneous engine map (IEM).

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