The impact of socio-demographic characteristics and driving behaviors on fuel efficiency

doi:10.1016/j.trd.2020.102565

Transportation Research Part D: Transport and Environment

Volume 88, November 2020, 102565

https://doi.org/10.1016/j.trd.2020.102565 Get rights and content

Highlights

•
Impacts of human factors on fuel consumption vary in different traffic conditions.
•
Frequent commuters drive more economically than others in peak hours.
•
Female consumes more fuel per mile than male mainly in peak hours.
•
Driving faster with low speed variance and acceleration is always fuel-efficient.

Abstract

Fuel efficiency intuitively differs between drivers, depending on their socio-demographic characteristics and driving behavior, and can change even for the same driver in different driving environments. In this work, we examine the impact of socio-demographic characteristics, driving behavior on fuel efficiency using naturalistic driving data collected from a busy freeway in Austin, Texas. Our results show that during off-peak period, fuel efficiency is negatively correlated with commute frequency. Furthermore, fuel efficiency is also closely related to commute purpose, age and gender. During peak period, female drivers’ fuel efficiency is worse, while correlations with other socio-demographic characteristics become less prominent. In terms of driving behavior, high-speed drivers with higher average speeds and less speed variance achieve the best fuel efficiency during both peak and off-peak hours. Our study sheds lights on specific eco-friendly driving behavior and education can be targeted at those who tend to consume more fuel.

Introduction

Increased car ownership around the world has led to more consumption of oil resources and higher level of greenhouse gas emissions. In order to effectively increase driving fuel efficiency, we must first determine the factors that influence it.

There are four main factors that affect vehicle fuel efficiency (Zhou et al., 2016): a vehicle’s mechanical characteristics, the naturalistic driving environment, the traffic environment and a driver’s characteristics. The first three factors are all non-human factors and have been widely studied. The influence of a vehicle’s mechanical characteristics depends on its engine power, weight, and brake system (Ji et al., 2016, Sawulski and Ławryńczuk, 2019). Naturalistic driving environments vary by geographic location and time (Ostrouchov, 1978, Jägerbrand and Sjöbergh, 2016). Traffic environments also affect vehicle fuel efficiency, and depend on factors such as road grade, road gradient, traffic congestion, etc. (Calle-Laguna et al., 2019, Faria et al., 2019).

However, a driver’s characteristics also have an influence on fuel efficiency. Studying the human factors on fuel efficiency is of important practical significance whether from the macro-scale of saving petroleum resources or the micro-scale of economizing on individual fuel costs. Fuel efficiency has been found to vary by 2–27% for different drivers (Sivak and Schoettle, 2012, Carrese et al., 2013, Stillwater et al., 2017, Lois et al., 2019). The median variance is around 4–6%, indicating huge potential of energy saving if drivers could be trained in economical driving behavior.

There exist many relevant studies on the impact of human factors. However, we notice obvious knowledge gaps:

(1)
Amongst existing researches, available socio-demographic characteristics of drivers are limited, such as gender, age and driving experience. Those socio-demographic characteristics are not enough to fully depict and embody the differences of drivers.
(2)
There are currently few researches on detailed comparison on driver’s fuel efficiency under various traffic conditions (i.e., peak period vs. off-peak period). Very likely drivers’ fuel efficiency may vary in various traffic conditions.
(3)
Third, there is also a lack of research which considers both the impact of comprehensive socio-demographic characteristics and driving behaviors on driving fuel efficiency.

The aforementioned knowledge gaps allow a more comprehensive analysis of the impact of human factors on driving fuel efficiency. In this study, we choose two distinct traffic conditions (i.e., peak period vs. off-peak period) to interpret the different impact of comprehensive socio-demographic characteristics and driving behaviors on fuel efficiency separately.

We take the following steps to obtain drivers’ socio-demographic characteristics and driving behavior. First, we collect naturalistic driving data and socio-demographic characteristics of participants with a smartphone app. We extract second-by-second vehicle trajectories on a busy freeway during peak and off-peak periods on normal weekdays. The trajectories are then used to calculate fuel efficiency using the FASTSim model. Next, we correlate the socio-demographic characteristics with fuel efficiency to find drivers who consume the least amount of fuel under specific driving conditions. Finally, we cluster drivers based on their driving behavior and correlate those clusters with fuel efficiency.

This work helps provide guidance for promoting economical driving behavior and improving fuel efficiency. Targeted driving education can be delivered to worse fuel-efficient driver groups.

The rest of this paper is organized as follows. Section 2 reviews related literature and gaps in previous research. Section 3 describes data collection and pre-processing. Section 4 discusses the classification of drivers based on socio-demographic characteristics and driving behavior. Section 5 compares drivers’ fuel efficiency during peak and off-peak periods, and Section 6 concludes this study and proposes reasonable suggestions for traffic management and driving behavior.

Section snippets

Literature review

A significant amount of fuel is consumed by gasoline vehicles around the world. The level of fuel efficiency is influenced by both non-human and human factors.

Naturalistic driving data collection

Naturalistic driving refers to participants driving according to their daily needs and driving habits. On-board data acquisition systems monitor and record each driver's driving information (Holden et al., 2017). This data collection process is relatively costly, and the data screening process is complex, but naturalistic driving data can reveal drivers’ real driving behavior under actual traffic conditions. Therefore, in order to ensure the reliability and representativeness of our analysis

Driver classification with Socio-Demographic characteristics

The main obstacle for examining the relationships between socio-demographic characteristics and fuel efficiency is the lack of appropriate tools to collect detailed personal information in a continuous and systematic way. However, for each driver participated in the Metropia project, personal socio-demographic characteristics are collected through a mini-survey that included 11 questions (shown in Table 1). The Metropia app pushes the questions to drivers one at a time, which reduce the

Impact of Drivers’ Socio-Demographic characteristics

To find the relationships between the drivers’ socio-demographic characteristics and fuel efficiency, we perform multiple linear regression analysis with R Studio. We use the socio-demographic characteristics as the independent variables, and the fuel efficiency (MPG) as the dependent variable.

Since more than half of the drivers did not answer Question 10 (How many vehicles does your household own?) in the questionnaire, this characteristic is not included in the multiple linear regression

Conclusion

Understanding human factors that can lead to higher fuel efficiency is a vital topic for drivers, car manufacturers and traffic management authorities. We use naturalistic driving data and the FASTSim simulation tool to quantify drivers’ fuel efficiency levels. Correlations between drivers’ socio-demographic characteristics, driving behavior and fuel efficiency are investigated.

The main conclusions of this paper are summarized below:

1.
More socio-demographic characteristics are significantly

Acknowledgements

This study is supported by the National Key Research and Development Plan of China under Grant [2018YFB1600805], the Science and Technology Commission of Shanghai Municipality under Grant [19DZ1208800], the Shanghai Sailing Program under Grant [19YF1451200], and the National Science Foundation of China under Grant [52002279].

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View full text

The impact of socio-demographic characteristics and driving behaviors on fuel efficiency

Highlights

Abstract

Introduction

Section snippets

Literature review

Naturalistic driving data collection

Driver classification with Socio-Demographic characteristics

Impact of Drivers’ Socio-Demographic characteristics

Conclusion

Acknowledgements

Transp. Res. Part D: Transp. Environ.

Transp. Res. Interdisciplinary Perspect.

Procedia - Social Behav. Sci.

Transp. Res. Procedia

Atmos. Environ.

Energy Procedia

Transp. Res. Part D: Transp. Environ.

Int. J. Transp. Sci. Technol.

Appl. Energy

Transp. Res. Part D: Transp. Environ.

Transp. Res. Part D: Transp. Environ.

Transp. Res. Part C: Emerg. Technol.

Inf. Sci.

Transp. Policy

Transp. Res. Part D: Transp. Environ.

Transp. Res. Part D: Transp. Environ.ent

ISPRS Int. J. Geo-Inf.

Transp. Res. Part D: Transp. Environ.

Transp. Res. Procedia

Transp. Res. Part D: Transp. Environ.