On-road transportation is an important source of greenhouse gas (GHG) and air pollutant emissions among all economic sectors [1
]. Numerous studies have investigated the factors that influence the amount of vehicle emissions and fuel consumption and proposed control strategies accordingly. From the perspective of vehicles, technologies such as emission after-treatment and vehicle fleet electrification can lead to a substantial decrease in emissions and fuel consumption [4
]. Methods that mitigate traffic congestion, such as adaptive signal timing control, countdown signal timers, predictive cruise control, and connected automated vehicles (CAVs) can also effectively control traffic emissions through shorter delays and less stop-and-go operations [9
]. Compared to the above-mentioned methods, adopting ecological driving behaviour (eco-driving) is a more cost-effective method because it does not require the upgrade of the vehicle powertrain or the reconstruction of infrastructure. Instead, vehicle operational emissions and fuel consumption can be decreased by correcting the driving trajectory.
Many eco-driving studies of road transport focus on the trajectory control of vehicles through connected and autonomous vehicle (CAV) technologies. Scenario-specific studies were proposed to optimize speed and acceleration profiles when vehicles were approaching signalized intersections or driving on highway links. Look-ahead traffic conditions, signal control information, and road geometries are delivered to CAVs through V2X (Vehicle to X communication), and vehicle speed can be dynamically adjusted with the objective of optimal fuel consumption [13
]. However, the speed–time profile determined by eco-driving algorithms cannot be practiced precisely when vehicles are operated by human drivers. Therefore, it is crucial to effectively guide drivers to modify their aggressive driving behaviour, thus approaching the optimal driving operation. The definition of eco-driving behaviour is varied among previous studies. In general, it can be summarized as the following “Golden Rules” [16
], including keeping a steady speed, anticipating surrounding traffic flow, braking smoothly, reducing long-time idling, and avoiding the overuse of auxiliary devices. In the real world, however, due to diverse drivers’ habits and varied traffic conditions, more practical eco-driving guidance should be provided on operating vehicles properly with the purpose of saving energy and reducing emissions instead of strategic guidelines.
This paper aims to present an overview of existing studies on eco-driving guidance for human-driving vehicles of road transport, focusing on the effectiveness and acceptance of the guidance. Different presence formats, their effects on drivers’ behaviour, and associated influencing factors are discussed. Based on the overview, this paper concludes with challenges and research gaps in existing studies, highlighting efforts that can be made in the future to develop an effective and acceptable eco-driving guidance system.
2. Designs of Eco-Driving Guidance
Existing research presents two types of eco-driving guidance: static training and dynamic guidance. The major difference between them is the use of real-time driving evaluation. The design of systems and the experiments conducted in existing studies are presented in Figure 1
. For static training, driving suggestions are provided to drivers through training courses, videos, or education brochures. These suggestions are based on the widely recognized definition of eco-driving, such as the “Golden Rules”. Drivers are first trained by self-learning or coaches; then, they apply the eco-driving knowledge to their daily driving activities. For dynamic guidance, however, driving suggestions are generated based on real-time driving behaviour analysis. Monitoring devices, such as Global Positioning Systems (GPSs) and On-Board Diagnostics (OBD), are equipped in-vehicle. Instantaneous speed and acceleration trajectories are collected. Some experiments also record driving operations such as braking pedal, accelerator pedal, and gear shifting. In this case, energy consumption is usually recorded due to the accessibility of the CAN-BUS data. The analysis of energy consumption or emissions during the recorded time period is then conducted, and corresponding driving suggestions (or warnings) can be generated. The term “dynamic” means that driving suggestions are subject to change with the periodic evaluation of emission reductions or energy savings. In this section, two types of eco-driving guidance strategies and representative examples are discussed in terms of aspects of their effectiveness and drivers’ acceptance with a focus on dynamic guidance.
2.1. Static Eco-Driving Training Based on Pre-Determined Guidelines
Static eco-driving training provides driving suggestions based on widely recognized eco-driving guidelines (such as the “Golden Rules”) to drivers through training courses, videos, brochures, and in-field practice with coaches. The training effect is evaluated by monitoring drivers’ behaviour and comparing fuel consumption or emissions before/after the training session. Eco-driving training programs have been implemented within different jurisdictions worldwide [16
]. The change in driving behaviour is measured by comparing fuel consumption and emissions or by comparing key indicators of eco-driving, such as the average speed, acceleration or deceleration rate, frequency of idling, and engine speed (rotation per minute, RPM). Eco-driving training programs are affected by a large range of factors, such as participating vehicle types, road geometry, drivers’ socio-demographic characteristics, and the tested time span; therefore, their effects on behavioural change, energy savings, and emission reductions greatly vary among programs. Table 1
lists existing literature testing the effect of static eco-driving guidance.
2.2. Dynamic Guidance Based on Real-Time Driving Operations
Static eco-driving training sessions deliver pre-determined eco-driving rules to drivers and require drivers to apply them during real practice, while the effectiveness of the learning process is questionable, and behavioural change toward eco-driving is hard to maintain for a long time [34
]. Instead, dynamic eco-driving guidance evaluates real-time driving performance based on driving operations, energy efficiency, and emissions. It generates personalized driving suggestions to drivers via in-vehicle or mobile driving assistance. The in-vehicle assistance device comprises data sensors (such as OBD and GPS) and a module delivering the feedback to drivers. Dynamic eco-driving guidance can be developed using two methods. First, the system records and evaluates on-road driving behaviour and sends a periodic personal driving report through in-vehicle devices or users’ mobile devices. Second, the system generates instantaneous information, such as warnings or speed suggestions, and presents it to drivers through visualized, acoustic, or haptic notifications. In this way, real-time and specific feedback can be provided to drivers according to their driving habits and on-road performance. Existing studies proposed different forms of the information module, varied by the type of feedback and the method to deliver the information. Consequent energy savings and emission reductions depend on the acceptance and practice of drivers, which are related to not only the information type but also drivers’ personality and psychological factors. The results of the use of dynamic eco-driving guidance in existing studies are presented in Table 2
2.2.1. Periodic Reports and Feedback
A periodic driving report usually includes the evaluation of driving behaviour and suggestions for eco-driving improvement based on real driving records, and the report is regularly pushed to the in-vehicle devices or users’ mobile devices. It can be regarded as a special type of eco-driving training, as a periodic report is more dynamic and customized to drivers’ individual habits. The frequency of the feedback can be daily [37
], weekly [45
], monthly [37
], or varied by drivers [49
], and it is an essential factor that affects the learning process of drivers toward eco-driving skills. For example, Ando and Nishihori [37
] revealed that compared to daily feedback, sporadic feedback pushed monthly or weekly may have better performance in encouraging drivers to adopt eco-driving behaviour. In contrast, Wu et al. [48
] presented a negative linear relationship between fuel consumption and the frequency of drivers checking the driving feedback.
2.2.2. Combined Sensory Methods and Their Acceptance
Studies and applications have integrated the eco-driving guidance into the vehicle’s human–machine interface (HMI), and the information is delivered through various sensory methods. Visualized suggestions include practical driving recommendations (such as the optimal speed and acceleration), warnings of improper behaviour (such as speeding, excessive braking, or long-time idling), indicators showing the gap between current behaviour and the optimal one, and indicators of environmental performances (such as energy efficiency or emissions). Existing research has also tested auditory notifications and haptic touch in dynamic eco-driving guidance with expectations of enhanced guidance effects [39
Haptic touch (including haptic stiffness and haptic force) is the most effective and easy-to-do method. Through pressure on the acceleration pedal or the braking pedal, drivers can easily sense the guidance and follow the instructions [51
]. With multiple types of presences being examined (such as dashboard message and colour scale), the visualized system has been reported to be impractical and distractive with additional workload [38
]. The auditory system is less disregarded than its visualized alternatives [65
], while its user experience is less satisfying [51
]. Although eco-driving guidance through haptic touch causes less distraction [66
], safety concern is still one of its disadvantages [63
]. The influence of these sensory methods on users’ acceptance, potential safety issues, and environmental benefits need to be further discussed.
2.2.3. Gamification Design in Dynamic Eco-Driving Guidance
Due to the strength in experiential learning environments and the higher satisfaction levels of users [58
], the concept of “gamification” has gradually attained attention in the eco-driving guidance design. The gamification design provides intrinsic motivation and extrinsic motivation to users to improve their behaviour [69
], and the elements in existing applications are points (scores), progress feedback, and socialization [70
]. Significant reductions in CO2
emissions and energy consumption can be observed in drivers who utilize gamified guidance or additional incentives [53
However, the effect of gamification is debatable. Fitz-Walter et al. [73
] observed that a gamified design improved drivers’ satisfaction with the guiding system, while significant behavioural changes did not occur. Some studies also oppose the gamified design as they have shown insignificant results from financial incentives (for example, [26
]). Furthermore, some researchers have expressed their worries about the negative impact of gamification: the behavioural change encouraged by monetary incentives cannot be sustained upon removal [75
], and peer competition through a ranking scheme may lead to an overly competitive situation and cheating in practice [36
]. In the current stage, gamification designs used in driving guidance are mainly limited to traditional PBLs (Points, Badges, and Leaderboards), while other elements and drivers’ acceptance of these elements have seldom been investigated [76
]. A systematic design of gamified eco-driving guidance should be further explored, and the long-term effect of various gamified elements should be compared.
2.2.4. Optimized Driving Suggestions Considering Traffic States
To improve the anticipation of traffic and drivers’ acceptance of driving guidance, researchers integrated in-vehicle telecommunication systems to retrieve real-time traffic conditions, optimize instantaneous speed and acceleration, and generate eco-driving suggestions dynamically. Downstream signal state, location information, and the motion of surrounding vehicles can be captured from a digital map and a front camera, and optimized guidance shows a great fuel-saving potential (up to 41.9%) [17
]. In addition, by learning the historical driving trajectory of a driver, the shortest learning path can be optimized for the driver, which reduces the acceptance burden when practicing eco-driving operations [54
]. These studies demonstrate the environmental benefits of such optimized eco-driving guidance, while large-scale on-road tests are lacking in current studies.
2.3. Factors That Affect the Guidance Effectiveness
2.3.1. Vehicle Types and Road Types
Due to different vehicle powertrain and operation requirements, driving habits and associated behavioural changes vary among drivers of different vehicle types after static eco-driving training. The difference in CO2
emissions and fuel efficiency is more significant among gasoline vehicles than diesel vehicles and hybrid vehicles [21
]. In addition, vehicles with manual transmission perform better in terms of saving fuel than automated-transmission vehicles [28
The driving behaviour on city road segments and major arterials features frequent stop-and-go operations, which greatly influences fuel efficiency and emissions. In other words, the potential for eco-driving improvement on such roads can be more significant than that in highway links [78
]. Existing studies also prove that the energy savings and emission reductions after eco-driving training sessions are more significant on congested road links, such as arterial and local streets, while on highway links, where the speed is high and steady, the effect is less significant [22
2.3.2. Drivers’ Characteristics
Drivers’ socio-demographic characteristics lead to heterogeneous effects on fuel consumption and emissions among the population, while current studies have not reached a common agreement on how these factors are associated with the effectiveness of eco-driving training. More specifically, Ho et al. [19
] found that male drivers have more notable behavioural changes with lower average speeds after the eco-driving training program in Singapore, leading to higher fuel efficiency (15.98% versus 11.21% for female drivers). Unlike the findings of Ho et al., female drivers in the study by Barla et al. [28
], Quebec, Canada, had a higher probability of applying eco-driving techniques. Abuzo and Muromachi [23
] found that drivers from different jurisdictions performed differently after the same eco-driving guidance, revealing the potential impact of cultural background.
The eco-driving training program is easier to implement because of regular tests and training sessions organized by companies or local authorities. It is worth investigating the effect of eco-driving training in heavy-duty vehicles due to a remarkable share of emissions from these vehicles [79
]. Eco-driving guidance programs have been implemented among bus and truck drivers, showing significant fuel drops [24
]. However, several studies have illustrated the difficulty of modifying the driving behaviour and habits of experienced employed drivers due to insufficient monetary reward, less motivation, and strict service time restrictions. For example, Díaz-Ramirez et al. [27
] found that more experienced drivers are not likely to change their behaviour after receiving eco-driving guidance, while other socio-demographic characteristics (such as age and education levels) are not significant. Similar results can also be found in [26
2.3.3. Sustaining Eco-Driving Behaviour after the Guidance
Since eco-driving is not an automatized, natural, or “everyday” driving style [81
], drivers maintaining eco-driving behaviour instead of returning to their “old habits” is essential for the training to have a sustainable environmental benefit. Several studies compared short-term behavioural changes with long-term driving habits [28
], demonstrating a fading effect of the training along the time span. For example, Barla et al. [28
] assessed the fuel-saving effect of eco-driving training immediately after the training session and ten months after the session. The reduction in fuel consumption on arterials became 2.5% after ten months, compared to 4.6% immediately after the session. In addition, interruptions during driving can also lead to inconsistent eco-driving behaviour, stressing the difficulties of maintaining the training effect [81
]. These studies pose challenges in encouraging drivers to continuously adopt eco-driving behaviour after the guidance, which is crucial in retaining environmental benefits from behavioural changes.
3. Additional Workload Caused by Eco-Driving and Drivers’ Motivation
Existing studies conducted surveys to quantify the additional workload added by eco-driving instructions to investigate the cognitive load and acceptance of different types of eco-driving assistance. The subjective mental workload can be scored by the NASA Task Load Index (TLX) [83
], and system acceptance can be measured by the System Acceptance Scale (SAS) of Van der Laan, which uses Usefulness and Satisfaction as metrics [83
]. For example, Heyes et al. [77
] adopted the SAS to evaluate drivers’ acceptance of the real-time driving advice provided by an in-vehicle system. The scale of Satisfaction significantly improved after using the system, while the scale of Usefulness did not. The positive attitude towards eco-driving assistance also led to less fuel consumption (4.01%) than those who did not support the system. Hibberd et al. [63
] compared the workload among different types of dynamic sensory notifications. The results of SAS and NASA-TLX indicated that the haptic system is less distractive, with higher levels of Usefulness and Satisfaction than a visual–auditory assistance device.
In addition to survey-based experiments, real-time physiological measurement has been used in previous research to quantify the additional physiological workload caused by eco-driving. For instance, Ruscio et al. [84
] measured heart rate, blood volume pulse, and high-frequency power of heart rate to quantitatively present the workload change when providing in-vehicle eco-driving assistance to drivers who had not used it before. The measurements showed higher cognitive loads when drivers were instructed to follow eco-driving assistance, introducing potential driving risks. Ahlstrom and Kircher [65
] analysed glance behaviour with or without in-vehicle eco-driving guidance. The number and duration of glances varied when driving on different types of road, while mirror glances were reduced due to the in-vehicle guidance introducing more mental workload and higher risks during driving.
Limited studies investigated the inner motivation of drivers to implement improved driving behaviour. Existing research presents a significant variation in the effect of eco-driving guidance among drivers from the perspectives of both system acceptance and real-driving practice [85
]. The effect of socio-demographic factors, such as age, gender, and education level, varies across studies, and the results can be different [45
]. Lauper et al. [88
] described the adoption of eco-driving as the consequence of two psychological processes: the formation of the behavioural intention and the process of putting the intention into practice. They found that socio-demographic characteristics only show minor effects on the intention of eco-driving. Instead, drivers’ attitudes towards eco-driving and perceived behavioural control were shown to be the strongest predictor of eco-driving intention, and the action control was the strongest predictor of eco-driving practice. This study emphasizes that instead of socio-demographic factors, the inner motivation of drivers can be the most influential personal characteristic of eco-driving behaviour.
Environmental concerns, a positive attitude toward eco-driving, and an open mind to new technologies are possible inner motivation factors for drivers to practice on-road eco-driving [43
]. However, the opposite results have also been found in existing studies. For example, McIlroy and Stanton [87
] analysed the relationship between the attitude towards eco-driving and the actual practice of eco-driving through an online survey. The result showed that the knowledge of eco-driving and awareness of environmental issues do not necessarily cause eco-driving behaviour in the real world across genders, ages, and levels of education. A similar result was also revealed by Scott and Lawson [90
], that drivers usually do not apply fuel-saving driving operations although they have related guidelines in mind, and a gap exists between eco-driving knowledge and practice. As was illustrated by Pampel et al. [91
], driving interventions are required to maintain the intention to utilize eco-driving guidelines and put eco-driving into practice; otherwise, the drivers would not practice eco-driving behaviour in the real world. The studies mentioned above emphasize the challenge of encouraging eco-driving practices, which may need specific designs of eco-driving guidance systems and the possible involvement of intrinsic and extrinsic motivations with the consideration of the psychological processes of human beings.
This paper categorizes existing research about eco-driving guidance into two major types, static training and dynamic assistance, based on the use of real-time driving data to generate driving suggestions. Representative studies of both types are presented, and their effects on energy consumption and emissions are compared.
By summarizing the results of eco-driving experiments and surveys, challenges are proposed in this study. We conclude that static eco-driving training cannot ensure a sustainable change in driving behaviour, while whether regular incentives help maintain the training effect or not is debatable across studies. As “semi-dynamic” guidance, a periodic driving report leads to energy savings and emission reductions in a longer time span than pure training. The consequent behavioural change is affected by the feedback’s frequency and content. Comparing different sensory devices in dynamic guidance, it is shown that haptic touch feedback is less distractive with higher users’ acceptance than auditory and visualization devices. In terms of drivers’ characteristics, inexperienced and unprofessional drivers are more likely to adopt driving suggestions and change their behaviour, although exceptions exist (see [104
]). Socio-demographic factors are associated with the adaptiveness of the eco-driving guidance, while the influence is varied, and the intentions are unclear. Most existing studies suggest that environmental concerns and a positive attitude towards eco-driving improve the acceptance level of eco-driving guidance, while drivers still need reminders to put their eco-driving knowledge into practice. A systematic design of eco-driving guidance, including the suggestions’ content, illustration, and human–machine interaction, should be examined to assess the motivation factors of drivers’ intention to accept and practice these suggestions. In addition, to improve the effectiveness, personalized recommendations based on historical driving data and the learning curve should be investigated.