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Using on-board logging devices to study the long-term impact of an eco-driving course
Abstract
In this paper the long-term impact of an eco-driving training course is evaluated by monitoring driving behavior and fuel consumption for several months before and after the course. Cars were equipped with an on-board logging device that records the position and speed of the vehicle using GPS tracking as well as real time as electronic engine data extracted from the controller area network (CAN). The data includes mileage, number of revolutions per minute, position of the accelerator pedal, and instantaneous fuel consumption. It was gathered over a period of 10 months for 10 drivers during real-life conditions thus enabling an individual drive style analysis. The average fuel consumption four months after the course fell by 5.8%. Most drivers showed an immediate improvement in fuel consumption that was stable over time, but some tended to fall back into their original driving habits.
... In fact, individuals perceive pro-environmental driver behaviors as involving high behavioral costs [39]. In line with this, studies on the effectiveness of eco-driving interventions have produced mixed results [40][41][42]. For example, Af Wåhlberg [43] found that although an eco-driving training for bus drivers showed strong effects on driving behavior during training, the effects did not transfer into individuals' everyday life. ...
... For example, Af Wåhlberg [43] found that although an eco-driving training for bus drivers showed strong effects on driving behavior during training, the effects did not transfer into individuals' everyday life. This aligned with other studies showing that the effects of an eco-driving training on behavior could not be translated into long-term pro-environmental driving behavior [40,41]. ...
... However, the effects have been mixed so far. For example, as indicated, eco-driving training courses have motivated driving behavior change only temporarily [40,41,43]. In line with this, other studies have shown that single informational interventions (interventions influencing knowledge, attitudes, norms, or motivations) were not successful to change rather costly pro-environmental behaviors [51]. ...
... Nous présentons ici une méthode pour résoudre de manière analytique le problème d'optimisation défini ci-dessus (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). La principale caractéristique de cet algorithme est de déterminer les profils de vitesses optimaux constants par morceaux avec un coût de calcul considérablement réduit par rapport aux méthodes classiques. ...
... Ceci est réalisé en déplaçant la demande d'énergie des PHEV à des moments où la demande d'énergie est faible ; -d'aplatir le profil de charge global qui se réalise non seulement en déplaçant la demande de charge dans le temps mais aussi en contrôlant l'intensité, c'est-à-dire le taux de charge de la batterie. [75] Dans leur étude, Chen et al. [93] montrent que pour une fonction convexe générale (. ), un profil de remplissage de vallée est optimal pour le problème de planification des EV 10 . De même, Gan et al. [75] proposent un algorithme décentralisé pour planifier de manière optimale la charge des EVs. ...
... Pour cela, il suffit d'évaluer le temps supplémentaire passé dans les phases transitoires et de corriger la contrainte en temps initiale dans le problème d'optimisation (cf. (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). En résolvant à nouveau le problème d'optimisation avec l'algorithme SRTO, un nouveau profil de vitesse optimal constant par morceaux peut être déterminé ( SRTO4) qui tient compte cette fois-ci du temps passé dans les phases transitoires. ...
L’éco-conduite a été identifié comme l’un des moyens efficaces pour l’économie d’énergie dans le domaine des véhicules terrestres. Le gain potentiel en consommation ainsi que sa facilité de mise en œuvre, rendent cette solution très recherchée dans le milieu industriel pour à la fois améliorer la consommation des véhicules mais aussi satisfaire les utilisateurs. Cette thèse contribue au développement d’un système actif d’aide à l’éco-conduite pour assister le conducteur dans son économie d’énergie. Ce système s’appuie sur une optimisation énergétique et tient compte de l’interaction du conducteur avec le véhicule et son usage (la route). Nous avons tout d’abord développé un modèle multi-variable de style de conduite pour représenter le conducteur humain par un modèle virtuel. L’identification des paramètres de ce modèle a permis de caractériser trois styles de conduite sur plusieurs cas d’usage et de reproduire de manière assez fidèle les trois niveaux de consommation de carburant. Considérant les cas d’usage péri-urbains et autoroutiers, le problème d’optimisation de la trajectoire sur des critères énergétiques a été reformulé afin de déterminer un profil de vitesse constant par morceaux minimisant la consommation d’énergie, tout en respectant la durée de trajet désirée et les limitations de vitesse. Le profil de vitesse optimal fournit des vitesses cibles, informations du premier ordre pour réduire la consommation. Plusieurs extensions ont été ensuite introduites dans la trajectoire optimale afin d’y intégrer l’anticipation des phases de décélération et les phases d’accélération. L’originalité principale de cette approche est le temps de calcul extrêmement faible, tout en obtenant des résultats très proches des résultats optimaux issus de méthodes classiques d’optimisation (ex. programmation dynamique). Afin d’aller encore plus loin dans l’éco-conduite, nous avons étudié la possibilité de réduire la consommation d’énergie en intégrant des stratégies de conduite telle que le ''swaying'' qui consiste en une oscillation de la vitesse du véhicule autour d’une vitesse moyenne. Nous avons alors pu montrer que, « en théorie », il existe bien des paramètres permettant de réduire la consommation de cette manière. Le système actif d’aide à l’éco-conduite a donc été développé en conjuguant les deux aspects précédents. Il se base sur le partage de la commande moteur entre le conducteur humain et un contrôleur optimal. Des niveaux de partage variables ont été établis afin de représenter différents niveaux d’économie d’énergie et d’intervention sur la conduite du conducteur. Enfin, ce système d’aide actif a été testé expérimentalement sur un simulateur de conduite.
... Shifting up as soon as possible increases the engine load rate and helps control the engine speed in the economic zone [26]. Beusen et al. [39,40] studied the gear information of drivers whose fuel consumption had been significantly reduced after eco-driving training and found that the average shift point changed significantly (moved closer to the optimal 2000 revolutions per minute (r/min)). Choosing the right shift point (generally, 2000 r/min-2500 r/min) and driving the vehicle using the highest gear possible are effective methods to reduce fuel consumption for conventional vehicles. ...
... The earliest eco-driving education began in the early 1980s, mainly in Europe, Japan, Australia, and North America, and carried out the propaganda and education of ecological driving behavior [14,17,[39][40][41][42]. The fuel-saving effect after eco-driving training has also been verified. ...
... For example, different drivers have different abilities to accept new knowledge, and before the training, the participants' fuel-saving awareness is different. Beusen et al. [40] found that there are big differences in the impact of eco-driving education between different drivers, and 20% of driver participants did not achieve a reduction in fuel consumption. Meanwhile, the results of Barla et al. [94] also showed that drivers' responses to eco-driving training were very uneven, with only about half of the participants achieving statistically significant fuel reductions. ...
Constrained by traditional fuel-saving technologies that have almost reached the limit of fuel-saving potential, the difficulty in changing urban congestion, and the low market penetration rate of new energy vehicles, in the short term, eco-driving seems to be an effective way to achieve energy-saving and emissions reduction in the transportation industry. This paper reviews the energy-saving theory and technology of eco-driving, eco-driving capability evaluation, and the practical application of eco-driving, and points out some limitations of previous studies. Specifically, the research on eco-driving theory mostly focuses on a single vehicle in a single scene, and there is a lack of eco-driving research for fleets or regions. In addition, the parameters used to evaluate eco-driving capabilities mainly focus on speed, acceleration, and fuel consumption, but external factors that are not related to the driver will affect these parameters, making the evaluation results unreasonable. Fortunately, vehicle big data and the Internet of Vehicles (V2I) provides an information basis for solving regional eco-driving, and it also provides a data basis for the study of data-driven methods for the fair evaluation of eco-driving. In general, the development of new technologies provides new ideas for solving some problems in the field of eco-driving.
... Coaching the drivers on eco-driving techniques can contribute to reductions in fuel consumption of 5.5-16.9% (Beusen et al., 2009;Sullman et al., 2015;Beloufa et al., 2017;Ayyildiz et al., 2017;Pinchasik et al., 2021). Many studies have pointed out that the drivers tend to forget the eco-driving techniques learned through training sessions and get back to their old driving habits in a short period, thus showcasing no long term benefits of such trainings (Beusen et al., 2009;Beloufa et al., 2017). ...
... (Beusen et al., 2009;Sullman et al., 2015;Beloufa et al., 2017;Ayyildiz et al., 2017;Pinchasik et al., 2021). Many studies have pointed out that the drivers tend to forget the eco-driving techniques learned through training sessions and get back to their old driving habits in a short period, thus showcasing no long term benefits of such trainings (Beusen et al., 2009;Beloufa et al., 2017). In a driving simulator experiment by Daun et al. (2013), displaying a few eco-driving advisory messages to the drivers has improved their fuel savings up to 12%. ...
In this work, a predictive eco-driving assistance system (pEDAS) with the goal to assist drivers in improving their driving style and thereby reducing the energy consumption in battery electric vehicles (BEVs) while enhancing the driving safety and comfort is introduced and evaluated. pEDAS in this work is equipped with two model predictive controllers (MPCs), namely reference-tracking MPC and car-following MPC, that use the information from onboard sensors signal phase and timing (SPaT) messages from traffic light infrastructure, and geographical information of the driving route to compute an energy-optimal driving speed. An optimal speed suggestion and informative advice are indicated to the driver using a visual feedback. Moreover, the warning alerts during unsafe car-following situations are provided through an auditory feedback. pEDAS provides continuous feedback and encourages the drivers to perform energy-efficient car-following while tracking a preceding vehicle, travel at safe speeds at turns and curved roads, drive at energy-optimal speed determined using dynamic programming in freeway scenarios, and travel with a green-wave optimal speed to cross the signalized intersections at a green phase whenever possible. Furthermore, to evaluate the efficacy of the proposed eco-driving assistance system, user studies were conducted with 41 participants on a dynamic driving simulator. The objective analysis revealed that the drivers achieved mean energy savings up to 10%, reduced the speed limit violations, and avoided unnecessary stops at signalized intersections by using pEDAS. Finally, the user acceptance of the proposed pEDAS was evaluated using the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The results showed an overall positive attitude of users and that the perceived usefulness
and perceived behavioral control were found to be the significant factors in influencing the behavioral intention to use pEDAS.
... Depending on which behaviors are included, fuel-efficient behaviors have consumption saving potentials varying around 5 and 45 % (af Wåhlberg, 2007;Alam & McNabola, 2014;Barkenbus, 2010;Beusen et al., 2009;Carrese et al., 2013;Sivak & Schoettle, 2012;Zarkadoula et al., 2007). Fuel consumption reductions are associated with CO 2 emission and pollutant reductions, which significantly contribute to climate change mitigation and everybody's health if a majority of people eco-drives (Barkenbus, 2010;Keyvanfar et al., 2018). ...
... This is in line with the outlined theoretical assumptions that state that goal-framed behavioral information should result in medium-term pro-environmental behavior change independent of framing content, because this information will motivate behavior changes temporarily by strengthening either personal intrinsic or extrinsic goals (Sheldon & Kasser, 1995). However, past studies also showed that behavioral changes influenced by monetary interventions were only weak and not persistent (af Wåhlberg, 2007;Beusen et al., 2009;Degraeuwe & Beusen, 2013). This is also in line with theoretical assumptions that suggest that only interventions highlighting meaningful intrinsic information (e.g., environmental and altruistic framing) activate deep motivational and cognitive processes (e.g., higher perceived worthiness). ...
... 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 [28,35,82], 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. ...
... In addition, a great number of questions remain to be answered: for example, how socio-demographic factors influence the guidance result, which type of guidance is more suitable for a specific group of drivers, and how to generate adaptive driving suggestions according to instantaneous traffic conditions and personal driving habits. The long-term effect of eco-driving guidance remains debatable: most studies present a fading impact of either static or dynamic eco-driving guidance, leading to an inconsistent energy-saving improvement (for example, [28,35,45,55,82]). ...
Eco-driving guidance refers to courses, warnings, or suggestions provided to human drivers to improve driving behaviour to enable less energy use and emissions. This paper reviews existing eco-driving guidance studies and identifies challenges to tackle in the future. We summarize two categories of current guidance systems, static and dynamic, distinguished by whether real-world driving records are used to generate behaviour guidance or not. We find that influencing factors, such as the content of suggestions, the display methods, and drivers’ socio-demographic characteristics, have varied effects on the guidance results across studies. Drivers are reported to have basic eco-driving knowledge, while the question of how to motivate the acceptance and practice of such behaviour, especially in the long term, is overlooked. Adaptive driving suggestions based on drivers’ individual habits can improve the effectiveness and acceptance while this field is under investigation. In-vehicle assistance presents potential safety issues, and visualized in-vehicle assistance is reported to be most distractive. Given existing studies focusing on the operational level, a common agreement on the guidance design and associated influencing factors has yet to be reached. Research on the systematic and tactical design of eco-driving guidance and in-vehicle interaction is advised.
... In contrast to studies examining short-term effect right after the training (Rutty et al., 2013), several studies compared the short-term behavioural change with long-term driving habits (Allison and Stanton, 2019;Barla et al., 2017;Beusen et al., 2009), demonstrating a fading effect of the training along the time span. For example, Barla et al. (2017) assessed the fuel-saving effect of the eco-driving training immediately after the training session and after ten months of the session. ...
... Moreover, the long-term effect of eco-driving guidance remains debatable: most studies presented a fading effect of either static or dynamic eco-driving guidance, leading to an inconsistent energy-saving improvement (for example, Allison and Stanton (2019); Barla et al. (2017), Beusen et al. (2009), Rolim et al. (2016 and Zavalko (2018)). However, the requirement of the time span and the sample size for long-term effect measurement is much higher than that of instantaneous or short-term effect, and this possibly is the reason for the research gap of the long-term. ...
Ecodriving guidance includes courses or suggestions for human drivers to improve driving behaviour, reducing energy use and emissions. This paper presents a systematic review of existing eco-driving guidance studies and identifies challenges to tackle in the future. A standard agreement on the guidance design has not been reached, leading to difficulties in designing and implementing eco-driving guidance for human drivers. Both static and dynamic guidance systems have a great variety of guidance results. In addition, the influencing factors, such as the suggestion content, the displaying methods, and drivers socio-demographic characteristics, have opposite effects on the guidance result across studies, while the reason has not been revealed. Drivers motivation to practice eco behaviour, especially long-term, is overlooked. Besides, the relationship between users acceptance and system effectiveness is still unclear. Adaptive driving suggestions based on drivers habits can improve the effectiveness, while this field is under investigation.
... Gilman et al. [27], among other parameters, used the percentage of time spent in different RPM intervals (less than 1500 RPM, between 1500 and 2000 RPM, between 2500 and 3500 RPM, and more than 3500 RPM) for developing a real prototype of a driving assistance system which helps drivers to drive more efficiently. As the most eco-efficient RPM interval for steady speeds, Beusen et al. [36], in their research, used interval between 1100 and 1700 RPM. ...
... That is mostly due to the linguistic nature of eco-driving rules. For example, accelerate/decelerate smoothly, maintain moderate and steady speed [50], accelerate moderately, avoid sharp braking and acceleration [19], drive with low engine RPM [36], press accelerator pedal gently [10]. These linguistic terms allow authors to subjectively determine what is for them gently, moderate, low, etc. Fuel consumption measurements under on-road conditions provide valuable data for the actual driver performance of eco-driving [7]. ...
Due to the great market competition, transport companies face the need to reduce their vehicle fleet costs. The vehicle fleet managers’ actions on the driver’s driving style to achieve fuel consumption savings are measures to increase the fleet’s energy efficiency. The authors developed a model for evaluating driving style using a type-2 fuzzy logic system. The model comprehensively considers three parameters: engine speed, accelerator pedal position, and acceleration/deceleration. These parameters can precisely describe the driving style and additionally have a strong influence on fuel consumption. The model output is the driver’s score, representing the influence of driving style to fuel consumption. The model is tested in the company whose drivers have attended the eco-driving training course. Each driver’s driving style was monitored for 15 days to obtain trustworthy assessments regarding driving style. The result was twofold: firstly, we point out the importance of simultaneous observation of all three defined parameters to get reliable driver’s score in terms of driving style, and secondly, it is established that drivers have significantly different driving styles regardless of whether they have attended the same eco-driving training. The established differences in driving styles have a direct impact on the obtained differences in fuel consumption among drivers. The proposed model can significantly reduce fuel consumption depending on the driving style and increase the vehicle fleet’s energy efficiency.
... In some papers, e.g. [1,3,10,11], it was proposed to use the On Board Diagnostics (OBD) system of the vehicle to obtain necessary driving data. ...
... By assumption, the investigation presented in this paper was limited to the standard parameters that can be obtained from the OBD system using widely available diagnostic devices. With a reference to previous research works [1,3,10,11,15] and taking into account the abovementioned limitations, the following parameters were selected as relevant to driving style: -vehicle velocity, -engine rotational speed, -relative accelerator pedal position, -relative throttle valve position, -relative engine torque, -intake manifold pressure. ...
The purpose of this study was to analyse the possibility of using information from the On Board Diagnostics (OBD) system of the ve-hicle to determine the characteristics of the drivers driving style. Available data from the OBD system were considered and the most useful ones were selected for further investigation. Selected zero-dimensional characteristics of vehicle velocity as well as characteristics of relative position of the accelerator pedal were proposed as criteria for the assessment of driving style. The tests were carried out in conditions of real road traffic using a passenger car with a spark-ignition engine. The car was equipped with a device for recording signals from the OBD system. The tests included two drivers traveling on routes in the urban and rural areas. The obtained results were used to analyse the driving style of both drivers separately in the considered traffic conditions. On this basis, conclusions on the suitabil-ity of information from the OBD system for the assessment of the drivers driving style were formulated.
... ICS-alike systems have been studied in the transport sector. These studies indicate that ICS could substantially reduce the risk of operators reverting to old inefficient working methods (af Wåhlberg 2007;Beusen et al. 2009;Huang et al. 2018;Pampel et al. 2018). However, the implementation of ICS requires a more detailed and reliable automatic detection of work elements and methods than what is possible with currently available on-board computing systems in forest machinery, especially for forwarders. ...
The productivity of cut-to-length machine operators exhibits a significant disparity, with the most productive individuals demonstrating twice the efficiency of their less productive counterparts. This discrepancy is largely attributed to variations in work methods. While supervised training has proven effective in streamlining work methods and enhancing productivity, the availability of forest-machine instructors for supervision is limited. Intelligent coaching systems (ICS) are periodically proposed to address this constraint. ICS are computer-based aids that offer machine operators real-time feedback on their work and guide them on how to rationalize their work. The successful implementation of ICS initially requires the development of systems for automatic work-element detection (AWED). Therefore, this article explores the history, current status, and technological possibilities of AWED. Additionally, key features of ICS are briefly reviewed. Lastly, a broader, interdisciplinary discussion is initiated on how to strategically allocate limited research resources. Questions surrounding the feasible ambition level for ICS and AWED are raised, prompting considerations for the next steps in research and development.
... Existing eco-driving strategies for urban traffic can be categorized into three kinds. The first kind of strategy focused on developing ecodriving training programs for drivers to master energy-efficient driving abilities [3][4][5][6][7][8]. The second kind of strategy focused on establishing eco-driving metrics to measure the energy efficiency and effectiveness of different eco-driving interventions [9][10][11][12][13]. ...
... Eco-driving tips and tricks offered to the experiment participants. Note: Adapted from various sources[14][15][16][17][18][19]. ...
In the global fight against climate change, stimulating eco-driving could contribute to the reduction of CO2 emissions. Company car drivers are a main target in this challenge as they represent a significant market share and are typically not motivated financially to drive more fuel efficiently (and thus more eco-friendly). As this target group has received little previous research attention, we examine whether digitally administered feedback and coaching systems can trigger such company car owners to drive eco-friendly. We do so by using respondents (employees of a financial services company (N = 327)) that voluntarily have a digital device (‘dongle’) installed in their company car, which monitors and records driving behavior-related variables. In a longitudinal real-life field study, we communicate eco-driving recommendations (e.g., avoid harsh braking, accelerate gently, etc.) to the respondent drivers via a digital (computer) interface. Over a 21-week time frame (one block of seven weeks before the intervention, seven weeks of intervention, and seven weeks after the intervention), we test whether eco-driving recommendations in combination with personalized, graphical ‘eco-score index evolution’ feedback increase eco-driving behavior. We also experimentally evaluate the impact of adding social comparison elements to the feedback (e.g., providing feedback on a person’s eco-driving performance compared to that of the same car brand users). Structural Equation Modeling (in MPlus 8.4) is used to analyze data. Our results show that digitally administered personal performance feedback increases eco-driving behavior both during and after the feedback intervention. However, we do not observe increased effects when social comparison information is added to the feedback. As this latter element is surprising, we conclude with a reflection on possible explanations and suggest areas for future research. We contribute to the sustainable eco-driving literature by researching an understudied group: company car drivers. More specifically, we contribute by demonstrating the effectiveness of digitally administered personal performance feedback on eco-driving for this group and by observing and reflecting on the (in)effectiveness of feedback containing social comparison information.
... Existing eco-driving strategies for urban traffic can be categorized into three kinds. The first kind of strategy focused on developing ecodriving training programs for drivers to master energy-efficient driving abilities [3][4][5][6][7][8]. The second kind of strategy focused on establishing eco-driving metrics to measure the energy efficiency and effectiveness of different eco-driving interventions [9][10][11][12][13]. ...
Eco-driving is one of the most effective control strategies to enable energy management for urban traffic. However, the existing eco-driving strategies still have two shortcomings: (i) these strategies lack the consideration of lateral decision-making; (ii) their performance deteriorates when a controlled vehicle encounters traffic queues at a signalized intersection. To overcome these shortcomings, this paper proposes an innovative eco-driving strategy at intersection approach lanes consisting of the bus-priority lane (BPL) and general-purpose lanes (GPLs). The proposed strategy has the capability of lateral decision-making and allows ego connected and automated vehicles (CAV) to bypass the traffic queue. To enable this capability, the proposed strategy permits the ego CAV to change lanes and share the BPL. Both left-turning-movement CAVs and going-through-movement CAVs are allowed to share the BPL; i.e., the function of the BPL can be switched as per the phases of a traffic signal scheme. Through phase-switch-based bus lane sharing, the proposed eco-driving strategy aims to improve traffic efficiency and sustainability under the partially connected and automated traffic environment. To validate its effectiveness, the proposed strategy is evaluated against the non-control baseline and the state-of-the-art strategy. Sensitivity analysis is conducted under six different demand levels and five different CAV penetration rates. The results show that the proposed eco-driving strategy outperforms and has the benefits of fuel efficiency improvement, throughput improvement, and delay reduction.
... ACC assists the driver in achieving longitudinal control by adaptively adjusting the throttle or brake to maintain a certain cruise speed or to ensure an appropriate inter-vehicle distance, based on the state information of the preceding vehicle obtained by onboard sensors. Compared to human drivers who control longitudinal motions in an intuitive pattern, the longitudinal automation provided by ACC enables EVs to better execute eco-driving strategies [7], and as a result, the economy of EVs can be improved to a greater extent by designing an economy-oriented ACC system. ...
In this paper, an economy-oriented car-following control (EOCFC) strategy is proposed for electric vehicles in car-following scenarios. Specifically, a controller based on model predictive control (MPC) is developed to optimize the host vehicle’s speed for better energy economy while ensuring good car-following performance and ride comfort. The vehicle’s energy consumption is accurately quantified in the form of demand power, which is incorporated in the cost function for energy optimization. The proposed EOCFC strategy is evaluated using three standard test cycles, i.e., New European Driving Cycle (NEDC), Urban Dynamometer Driving Schedule (UDDS) and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), in comparison with a typical multi-objective adaptive cruise control strategy. The evaluation results demonstrate that the proposed EOCFC improves the energy economy of the host vehicle by 0.53%, 3.33% and 1.51%, under the NEDC, UDDS and WLTC test cycles respectively.
... Training and monitoring drivers on Eco-driving strategies has become a critical business consideration for many fleet companies. However, long term studies have shown that the effects from initial driver training tend to fade quickly and thus reduce the benefit to 5% just after 10 months [4]. To mitigate the diminishing effects of driver education, real-time feedback or continued training has been suggested [5]. ...
With the growing need to reduce energy consumption and greenhouse gas emissions, Eco-driving strategies provide a significant opportunity for additional fuel savings on top of other technological solutions being pursued in the transportation sector. In this paper, a model-free deep reinforcement learning (RL) control agent is proposed for active Eco-driving assistance that trades-off fuel consumption against other driver-accommodation objectives, and learns optimal traction torque and transmission shifting policies from experience. The training scheme for the proposed RL agent uses an off-policy actor-critic architecture that iteratively does policy evaluation with a multi-step return and policy improvement with the maximum posteriori policy optimization algorithm for hybrid action spaces. The proposed Eco-driving RL agent is implemented on a commercial vehicle in car following traffic. It shows superior performance in minimizing fuel consumption compared to a baseline controller that has full knowledge of fuel-efficiency tables.
... Each of the 2,670 road segments in the dataset is characterized by several variables related to road conditions, energy parameters, emissions, and driving patterns according to the literature in this field (Beusen et al., 2009;Ericsson, 2001;Smit et al., 2007). The instantaneous fuel consumption was calculated based on the VSP model, an accredited methodology that characterizes vehicles and driving profiles using real-world data (Section 2). ...
Worldwide, cities hold a preeminent position in the global economy and human activity, but their growth poses considerable challenges to sustainability. Parallel to the current trends of population growth and urbanization, the process of digitalization and the technological revolution promote a further increase in e-commerce transactions, which have emerged as a critical component in the debate over the future of cities. More people living in cities and more people transacting online translates into more demand for in-city delivery, with the consequent increase in land occupation, pollution, and reduction of liveability to name a few.
Last-mile delivery (LMD) is the last chapter of the logistic chain, in which the parcel is delivered to the end consumer. Thus, LMD operations vary according to the context, e.g, urban setting, road network, and product delivered. Furthermore, they involve the interaction between multiple stakeholders with different and frequently conflicting interests and objectives. In this context, the literature points out (i) a lack of real data which would help to define efficient policy measures for LMD in different urban settings, and (ii) the necessity to consider different stakeholder perspectives for efficient management and planning of LMD strategies. To that end, the thesis combines quantitative and qualitative techniques. First, real data of LMD operations carried out in different urban settings are analysed and the extent to which distribution operations vary according to delivery area is presented. Then, an energy consumption model is calibrated, and fuel consumption and pollutant emissions proper of LMD operations are estimated. Second, different strategies are
assessed for improving e-commerce LMD efficiency, and their strengths and
weaknesses are explored. To this scope, an online Multi-Actor Multi-Criteria evaluation is performed.
The results highlight the urgency of (i) replacing LDVs with non-motorised modes in city centres and suburban residential areas, and (ii) implementing intermodality and electric fleets in suburban areas. Considering both qualitative and quantitative analysis, it emerges that increasing parcel lockers in the cities would solve most of the inefficiencies identified and would receive the most consensus from stakeholders. This thesis provides a holistic perspective of the LMD problem in urban areas while promoting a bottom-up approach that considers different aspects of LMD operations. On the one hand, it gives empirical knowledge on the challenges that LMDs bring to the improvement of the liveability of cities. On the other hand, it seeks to ease the decisionmaking process for implementing strategies aimed at reducing LMD impacts in urban areas. The findings yield interesting conclusions and useful policy recommendations that could bring to more efficient and sustainable LMD operations.
... To enhance the energy-efficiency in both on-road and offroad vehicles, an area of research that has received significant attention in both the automotive industry and the academia over the years, is eco-driving [1]. Previous studies in this research area focussed primarily on coaching drivers on the eco-driving techniques to promote energy-efficient driving style behaviors [2]. To aid drivers by providing optimal speed suggestions in a continuous manner and avail the long-term benefits in terms of fuel or energy savings, the eco-driving assistance systems have shown great potential in the past years [3]. ...
This work proposes an eco-driving assistance system (EDAS) based on model predictive control (MPC) with a primary objective to improve the driver's driving style in an energy-efficient manner. To improve the efficiency of an EDAS, a learning-based approach to model the driver behavior from urban driving data collected using a dynamic driving simulator is presented. To cluster the driving data of thirty-four participants, unsupervised learning techniques such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used. Furthermore, to predict the driver speed error while tracking an advisory speed, both stochastic and deterministic models, namely Stochastic Volatility (SV) and Gated Recurrent Unit (GRU) respectively, are trained. Six new drivers evaluated the proposed concept, whose driving style is classified using a trained temporal convolution network (TCN). Using the predicted driver speed error, the eco-driving advisory speed is compensated and provided as a feedback to the driver via a human-machine interface (HMI). The results reveal that the deterministic model has been able to achieve higher prediction accuracy as compared to the stochastic model. Furthermore, the results also suggest that the drivers using EDAS with driver error compensation have been able to perform better advisory speed tracking and achieve improved energy savings.
... Pierre (2012), who studied the consequence of eco-driving training on French drivers and concluded that whilst information about eco-driving is pivotal and drivers' consistent performance was significantly enhanced when taking eco-driving courses. In a similar vein, Beusen et al. (2009) who investigated the impact of eco-driving on a larger scale and for a longer duration also empirically established that after the eco-driving courses attended by the drivers, they were able to reduce their fuel consumption on average 5.8 per cent, thereby, highlighting that eco-driving training not only proves beneficial inculcating eco-driving behaviour among drivers but also raises environmental awareness among drivers. Though, the eco-driving training must come with a clear objective in order to have anticipated results. ...
This study intends to examine the complexity of the factors that influence truck drivers’ intentions to adopt eco-driving behaviour. A total of 198 truck drivers from the third-party logistics service provides participated in the study survey. An asymmetrical analytical approach through fuzzy set qualitative comparative analysis (fsQCA) examined the casual recipes of the factors effecting driver’s intention to adopt eco-driving behaviour. This research also applied symmetrical analysis by using partial least squares structural equation modelling (PLS-SEM), to compare the analysis with the findings from fsQCA. Results revealed three models of factors that lead to an intention to adopt eco-driving behaviour. The findings have profound practical and theoretical implications for the growth of new theories in transportation and environmental sustainability.
Highlights
• Symmetrical analysis revealed that both perceived knowledge and subjective norm had insignificant associations with attitude and intentions.
• Whereas, asymmetrical analysis, compared to symmetrical analysis revealed that both subjective norm and attitude were found as the necessary component to achieve proposed outcome.
• fsQCA yielded 76% of variance whereas symmetrical analysis by using PLS-SEM yielded 61.7% of proposed model’s explanatory capacity, validating the use of asymmetrical methods.
... This is supported by multiple authors stating that ecological training, while successful on the short term, did not lead to lasting changes in their behavior [1]. While research by Beusen et al. [7] first showed that a majority of participants showed lower levels of fuel consumption even ten months after a training session, Degraeuwe and Beusen [17] showed that the longitudinal ecological benefits vanished when controlling for the season's influence on fuel consumption. ...
The highest CO2 quota per person is personal transport. An ecological driving style (eco-driving) could drastically reduce it's emissions. Current interventions focus mainly on training, which benefits are mostly short-term, and individual feedback, which needs commitment by setting (individual) goals. We present the concept of displaying not only the eco-friendly behavior of the driver but peers around them. As perceivable competition has been shown to lead to higher task performance and a more eco-friendly behavior, adding a competitive aspect and social enforcement to ecological driving shortcuts the goal-setting. In a virtual reality within-subjects study (N=19), we explored this possibility in manual and automated driving. We found that adding a comparative factor to ecological feedback did not lead to significantly more ecological driving in manual or automated driving.
... To enhance the energy-efficiency in both on-road and offroad vehicles, an area of research that has received significant attention in both the automotive industry and the academia over the years, is eco-driving [1]. Previous studies in this research area focussed primarily on coaching drivers on the eco-driving techniques to promote energy-efficient driving style behaviors [2]. To aid drivers by providing optimal speed suggestions in a continuous manner and avail the long-term benefits in terms of fuel or energy savings, the eco-driving assistance systems have shown great potential in the past years [3]. ...
This work proposes an eco-driving assistance system (EDAS) based on model predictive control (MPC) with a primary objective to improve the driver's driving style in an energy-efficient manner. To improve the efficiency of an EDAS, a learning-based approach to model the driver behavior from urban driving data collected using a dynamic driving simulator is presented. To cluster the driving data of thirty-four participants, unsupervised learning techniques such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used. Furthermore, to predict the driver speed error while tracking an advisory speed, both stochastic and deterministic models, namely Stochastic Volatility (SV) and Gated Recurrent Unit (GRU) respectively, are trained. Six new drivers evaluated the proposed concept, whose driving style is classified using a trained temporal convolution network (TCN). Using the predicted driver speed error, the eco-driving advisory speed is compensated and provided as a feedback to the driver via a human-machine interface (HMI). The results reveal that the deterministic model has been able to achieve higher prediction accuracy as compared to the stochastic model. Furthermore, the results also suggest that the drivers using EDAS with driver error compensation have been able to perform better advisory speed tracking and achieve improved energy savings.
... Additionally, there have been experimental field studies that focus on eco-driving. Many of them study the benefit of eco-driving that arises from smart driving after a fuelefficient driving course was provided to a selected set of human drivers [19], [20]. Further studies have also been carried out to field test and quantify the fuel saving benefits of eco-driving using CAVs [21], [22]. ...
Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2 emission, and travel time and compare with naturalistic driving and model-based baselines. We further demonstrate the generalizability of the learned policies under mixed traffic scenarios. Simulation results indicate that scenarios with 100% penetration of connected autonomous vehicles (CAV) may yield as high as 18% reduction in fuel consumption and 25% reduction in CO2 emission levels while even improving travel speed by 20%. Furthermore, results indicate that even 25% CAV penetration can bring at least 50% of the total fuel and emission reduction benefits.
... This involves two approaches. The first approach involves educating the drivers on how to drive the cars economically [14], [18]. The other involves introducing an automatic powertrain operating system to improve the fuel economy. ...
Advanced fuel economy strategies are expected to reduce the fuel consumption of vehicles. An internal combustion engine (ICE) driving vehicle equipped with free-wheeling turns off the fuel injection and decouples the engine from the drivetrain when the driving force is not required. This paper proposes a method to reduce the fuel consumption of a vehicle equipped with free-wheeling. First, an optimization problem is formulated to minimize the fuel consumption of a vehicle with free-wheeling when the traveling distance, the initial and final speed are specified and the vehicle needs to glide before arriving at the end point for fuel economy. The speed profile of the vehicle, engine operating point, and engine on/off timing are obtained as the results of the optimization. The analytical and numerical analyses results demonstrate the effectiveness and the fuel-saving mechanism of the obtained speed profile. The main finding of the analyses is that rather than starting a gliding stage immediately after an acceleration or a constant speed stage, adding a pre-acceleration stage before the gliding stage is more fuel-economic under some conditions independent of the complexity of the vehicle model. The obtained speed profile including a pre-acceleration stage is applied to a driving scenario including traffic congestions. The results demonstrate the effectiveness of the pre-acceleration stage in reducing fuel consumption for a vehicle equipped with free-wheeling.
... Recently, performing empirical examinations regarding enhanced motivation and long term continuation [4] and supporting through the provision and sharing of information [5] have been pointed out as essential for the long-term continuation of eco-driving. Wu, Zhao, and Ou found 12-31% gas savings for a Fuel-Economy Optimization System (FEOS) which displayed the optimal acceleration/deceleration [6]. ...
This study looked at the effectiveness of acceleration/deceleration information regarding preceding and pre-preceding vehicles on the driving behavior and fuel economy of the following vehicle. As a result, it was suggested that information provision may improve the fuel economy of the following vehicle. It was also found that the subjects that increased fuel economy tended to value information on the acceleration/deceleration of the pre-preceding vehicle compared to those that decreased fuel economy did. From the above, it was indicated that the provision of information on the acceleration/deceleration of a preceding vehicle group to a following vehicle was effective.
... Such a metric as AH that is easy to calculate and straightforward to interpret could be employed as a tool to quantify drivers' awareness. This can be useful, especially when driving style alone has been shown to account for at least 6% of fuel consumption [38,39], reduced driver vigilance contributes to 35% of motorway crashes [40], and inattentiveness after speeding is the biggest human error in road crashes [27]. ...
Understanding the operation of complex assets such heavy-duty vehicles is essential for improving the efficiency, sustainability, and safety of future industry. Specifically, reducing energy consumption of transportation is crucially important for fleet operators, due to the impact it has on decreasing energy costs and lowering greenhouse gas emissions. Drivers have a high influence on fuel usage. However, reliably estimating driver performance is challenging. This is a key component of many eco-driving tools used to train drivers. Some key aspects of good, or efficient, drivers include being more aware of the surroundings, adapting to the road situations, and anticipating likely developments of the traffic conditions. With the development of IoT technologies and possibility of collecting high-precision and high-frequency data, even such vague concepts can be qualitatively measured, or at least approximated. In this paper, we demonstrate how the driver’s degree of attention to the road can be automatically extracted from onboard sensor data. More specifically, our main contribution is introduction of a new metric, called attention horizon (AH); it can, fully automatically and based on readily-available IoT data, capture, differentiate, and evaluate a driver’s behavior as the vehicle approaches a red traffic light. We suggest that our measure encapsulates complex concepts such as driver’s “awareness” and “carefulness” in itself. This metric is extracted from the pedal positions in a 150 m trajectory just before stopping. We demonstrate that this metric is correlated with normalized fuel consumption rate (FCR) in the long term, making it a suitable tool for ranking and evaluating drivers. For example, over weekly periods we found a negative median correlation between AH and FCR with the absolute value of 0.156; while using monthly data, the value was 0.402.
... Despite proposed benefits, drivers are required to invest a considerable amount of time and effort into eco-driving to see a relatively small fuel saving benefit. Beusen et al. (2009) suggests that a typical driver would achieve an estimated weekly saving of fuel valued at less than £5 (~$7 USD). Several researchers have suggested that the required effort and inherent difficulty of eco-driving is simply not worth this minimal saving (Delhomme, Cristea & Paran, 2013;Dogan et al., 2014). ...
Despite both the environmental and financial benefits of eco-driving being well known, the psychological impact of engaging in eco-driving behaviours has received less attention within the literature. It was anticipated that being asked to engage in eco-driving behaviours not only has an impact on vehicle fuel usage, but also on the driver, both in terms of their overall mood and willingness to re-engage with the task at a later time. Results from a simulated driving study suggest that although eco-driving was beneficial in reducing fuel consumption, being asked to eco-drive had a negative effect on overall journey time and mood. Engaging in eco-driving did however have a positive effect on self-esteem, suggesting potential longer term psychological benefits of adopting this behaviour.
... In addition, motivational determinants of eco-driving should be investigated due to contradictory results and decaying effects of eco-driving interventions found by previous studies for which explanation and understanding is still lacking. Various researchers reported that although training interventions show direct effects on behavior, the effects diminish over time and people return to their original driving habits (af Wåhlberg, 2007;Beusen et al., 2009;Degraeuwe & Beusen, 2013;Huang et al., 2018;Pampel et al., 2017;Schall et al., 2016). Therefore, knowledge seems no sufficient factor to change strong driving habits. ...
Although most people are aware of the harmful CO2 emissions produced by the transport sector threatening life on earth now and in the future, they do not eco-drive. Eco-driving improves the vehicle’s fuel or energy economy and reduces greenhouse gas emissions. We investigated the motivational predictors of eco-driving based on the theory of self-concordance (i.e., the consistency between a behavior/goal with the person’s pre-existing values and interests). Data from a cross-sectional online survey with 536 German drivers revealed that self-reported eco-driving was significantly predicted by sustained effort towards eco-driving, which in turn was predicted by self-concordance variables. Therefore, individuals pursuing eco-driving out of strong interest or deep personal beliefs (i.e., autonomous motivation) as opposed to external forces or internal pressures (i.e., controlled motivation) reported greater effort towards this behavior. Furthermore, biospheric striving coherence, i.e., the coherence between personal valuable biopsheric values (i.e., values addressing the well-being of the environment/biosphere) and eco-driving, significantly predicted effort towards eco-driving. In sum, our results suggest that autonomous rather than controlled motives and coherence between behavior and intrinsic rather than extrinsic values are relevant predictors for eco-driving. We discuss implications for future strategies and interventions fostering eco-driving in the long term.
... As mentioned above, nowadays, the data of the car GPS-tracking device is basically used for navigation and position purpose by managers, or for investigation of traffic accidents by police officers. There are few studies that utilized the data of car-GPS tracking devices to analyzed driver behaviors [6] and fuel consumption simultaneously [7,8]. ...
According to the current issue of resource shortage, the reduction of fuel consumption or using alternative energies which has an environmentally friendly effect is the priority of governments in many countries. Besides, many studies mentioned that driver behaviors play an essential role to reduce fuel use and decrease vehicle exhaust emissions. On the other hand, the carGPS tracking data in itinerary monitoring equipment is typically using in navigation, positioning, and vehicle management. Thus, this study aims to analyze the correlation between driver behaviors and fuel consumption based on the velocity, acceleration, fuel sensors data collected from car-GPS trackers device in association with Geographic Information System (GIS) to find out the solution to evaluate the effectiveness of ecodriving courses as well as assess the usability of using car-GPS tracking data from the GPS tracking device to analyze and adjust driver behavior in urban areas to save energy and protect the urban environment
... These studies generally offer empirically-based advice, such as driving at an economical cruise speed, and accelerating or decelerating less aggressively. Although this may lead to significant fuel efficiency improvements in the short term [9], degradation in the long term due to human behavior variations has remained an issue [10]. Thus, recently researchers have been relying on increasing levels of longitudinal automation, such as cruise control and adaptive cruise control. ...
The booming of e-commerce is placing an increasing burden on freight transport system by demanding faster and larger amount of delivery. Despite the variety in freight transport means, the dominant freight transport method is still ground transport, or specifically, transport by heavy-duty vehicles. Roughly one-third of the annual ground freight transport expense goes to fuel expenses. If fuel costs could be reduced, the finance of freight transport would be improved and may increase the transport volume without additional charge to average consumers. A further benefit of reducing fuel consumption would be the related environmental impact. The fuel consumption of the heavy-duty vehicles, despite being the minority of road vehicles, has a major influence on the whole transportation sector, which is a major contributor to greenhouse gas emissions. Thus, saving fuel for heavy-duty trucks would also reduce greenhouse gas emission, leading to environmental benefits. For decades, researchers and engineers have been seeking to improve the fuel economy of heavy-duty vehicles by focusing on vehicles themselves, working on advancing the vehicle design in many aspects. More recently, attention has turned to improve fuel efficiency while driving in the dynamic traffic environment. Fuel savings effort may be realized due to advancements in connected and automated vehicle technologies, which provide more information for vehicle design and control. This dissertation presents state-of-the-art techniques that utilize connectivity and automation to improve the fuel economy of heavy-duty vehicles, while allowing them to stay safe in real-world traffic environments. These techniques focus on three different levels of vehicle control, and can result in significant fuel improvements at each level. Starting at the powertrain level, a gear shift schedule design approach is proposed based on hybrid system theory. The resulting design improves fuel economy without comprising driveability. This new approach also unifies the gear shift logic design of human-driven and automated vehicles, and shows a large potential in fuel saving when enhanced with higher level connectivity and automation. With this potential in mind, at the vehicle level, a fuel-efficient predictive cruise control algorithm is presented. This mechanism takes into account road elevation, wind, and aggregated traffic information acquired via connectivity. Moreover, a systematic tool to tune the optimization parameters to prioritize different objectives is developed. While the algorithm and the tool are shown to be beneficial for heavy-duty vehicles when they are in mild traffic, such benefits may not be attainable when the traffic is dense. Thus, at the traffic level, when a heavy-duty vehicle needs to interact with surrounding vehicles in dense traffic, a connected cruise control algorithm is proposed. This algorithm utilizes beyond-line-of-sight information, acquired through vehicle-to-vehicle communication, to gain a better understanding of the surrounding traffic so that the vehicle can response to traffic in a fuel efficient way. These techniques can bring substantial fuel economy improvements when applied individually. In practice, it is important to integrate these three techniques at different levels in a safe manner, so as to acquire the overall benefits. To achieve this, a safety verification method is developed for the connected cruise control, to coordinate the algorithms at the vehicle level and the traffic level, maximizing the fuel benefits while staying safe.
https://deepblue.lib.umich.edu/handle/2027.42/147523
... For combustion engine driven vehicles this lowers CO 2 emissions, while for plug-in electric vehicles it reduces charging costs and increases range. Eco-driving, the modification of driving behaviour to save fuel and energy, is an efficacious method to reduce vehicle energy usage [1] that nonetheless can have limited effectiveness in practice due to difficulty in getting drivers to maintain energy-efficient behaviour longterm [2]. One suggestion to improve the uptake and retainment of eco-driving behaviour is to provide real-time feedback on a driver's energy efficiency through a visual [3] or auditory or haptic [4] humanmachine interface (HMI). ...
Eco-driving assistance systems that encourage drivers to engage in fuel-saving behavior are effective at improving energy-efficiency, with recent research directed towards incorporating predictive models of energy losses in these systems to optimize recommendations. In this article, we evaluate a predictive eco-driving assistance system on three powertrains: a combustion engine-driven vehicle, a parallel hybrid electric vehicle, and a battery electric vehicle. In each case, energy consumption is found by applying a quasi-static model to driving simulator data for a simulated route including urban, rural, and highway sections. We find that both assisted and unassisted eco-driving has a beneficial effect in all cases, with the assistance system leading to reductions in energy usage of 6.1%, 15.9%, and 16.6% for the combustion engine, hybrid electric, and battery electric powertrains, respectively.
... Research suggests that an acceleration-prone driving style including high speeds uses up to 30% more energy as compared to a steady driving style at lower speeds (Alessandrini, Orecchini, Ortenzi, & Villatico Campbell, 2009;Bingham, Walsh, & Carroll, 2012;Knowles, Scott, & Baglee, 2012). Yet, while interventions that target drivers, such as "eco driving" reminders and trainings, prove effective initially, effects tend to be transient (af Wåhlberg, 2007;Beusen et al., 2009;Lauper, Moser, Fischer, Matthies, & Kaufmann-Hayoz, 2015;Rolim, Baptista, Duarte, & Farias, 2014). It thus seems tempting to use technologies that influence driving style more directly by changing car parameters. ...
Individual, car-based mobility contributes significantly to worldwide greenhouse gas emissions. Driving style accounts for up to 30% of fuel consumption and manufacturers have implemented technologies such as energy-efficient “eco” driving modes to reduce emissions. Here we report evidence from a field experiment with battery-electric vehicles. Two behavioral interventions, changing the mode’s default to on and informing drivers about the frequency of other people’s usage of the mode, i.e. providing a descriptive social norm, successfully increased eco mode usage. However, the cars’ acceleration and energy consumption remained unaffected due to a behavioral rebound, and were instead predicted by a situational factor, trip distance. While behavioral interventions proved effective, the results suggest that technological interventions aiming to reduce the environmental impacts might focus more strongly on alterations of situational rather than dispositional factors of people or cars.
... Each of the 2,670 road segments in the dataset is characterized by several variables related to road conditions, energy parameters, emissions, and driving patterns according to the literature in this field (Beusen et al., 2009;Ericsson, 2001;Smit et al., 2007). The instantaneous fuel consumption was calculated based on the VSP model, an accredited methodology that characterizes vehicles and driving profiles using real-world data (Section 2). ...
The increasingly widespread use of vehicles has intensified fuel consumption and hence the emission of air pollutants, causing a negative environmental impact on both human health and climate change. It is well known that vehicle fuel consumption depends on several factors such as engine and vehicle technology, road characteristics, traffic conditions, and driver ability. Although the relationship between these variables has been subject of several researches, the combined influence of traffic flow with road type on vehicle fuel consumption has not yet been studied in depth. This paper aims to fill this gap by processing a large dataset of real-world driving data from an experiment carried out in Madrid, Spain; and to develop and validate a new approach using cluster analysis to define real traffic conditions. The results indicate that poor traffic conditions can significantly reduce vehicle’s energy efficiency and influence driving behavior, rather drastically depending on the road typology. While on high-capacity roads the speed covariance increases up to 73% in congestion, on low-capacity roads it increases by 31%, since road geometry also covers an important role; indeed, due to their complex and segmented geometry, local streets show 37% less vehicle’s energy efficiency compared with highways. The outcomes of this study suggest that energy efficiency depends on avoiding congestion on high-capacity roads, selecting green itineraries using the right road sections and having a more homogeneous driving behavior on low-capacity roads, through eco-driving whenever possible.
... The paradigm of eco-driving driver training has been accepted by many organisations to standardise efficient driving behaviours especially at high traffic volumes [16]. The effect of eco-driving training, however, seems to diminish over time as human drivers fall back into their original driving behaviours [17]. While six months of extensive training using a simulator resulted in a maximum average fuel economy improvement of 16.9% in [12], the long-term analysis in [15] revealed that the effect of the training in fuel saving was reduced to 4% just three months after the end of the training, which suggests the need for continuous training and support to reinforce initial changes in the driver's behaviour. ...
This study presents a radar-based predictive kinetic energy management (PKEM) framework that is applicable as an add-on driver assistance module for a heavy vehicle with an internal combustion engine powertrain. The proposed framework attempts to minimise fuel consumption by estimating the motion of the leading vehicle from radar information and optimising the inputs to the ego vehicle in a predictive manner. The PKEM framework consists of a driver-pedal pre-filter, an interacting multiple model radar-based filter and predictor of traffic object states, and a non-linear model predictive controller. The framework is integrated with established human-driver car-following models representing various driving styles and evaluated over a set of standardised driving cycles. The authors found that the energy-saving benefits can be as much as 23% over the baseline driver-only case with minimal compromises on travel time in urban environments, while the benefits are nearly negligible on the highway cycle. The results included also show the potential trade-offs in accommodating driver-desired inputs.
... Research made on different types of road normally indicates that eco-driving reduces speed, rpm, and accelerations [53]. However, there are other studies which show that on congested roads, eco-driving does not reduce these parameters [54]. ...
In recent years, eco-driving has proven to be an effective tool for reducing fuel consumption and greenhouse gas (GHG) emissions. Until now, most research carried out has focused on ordinary drivers applying eco-driving techniques on their usual routes. However, there is little research on professional driver couriers. This research is aimed at analyzing the effects that eco-driving has on fuel consumption and GHG emissions on courier deliveries in small cities such as Caceres (Spain). For this purpose, a real-life experiment was performed with professional drivers with Spanish post vans from the public sector company Correos. In the first period, driving was under normal conditions (non-eco), and after a theoretical training eco-driving course, there was a second driving period (eco). Driving parameters (speeds, accelerations, rpm, and consumptions) were recorded on all trips to analyze how effective the eco-driving was. The research concluded that eco-driving training does not correlate with more sustainable driving for professional drivers under pressure with the need to deliver packages on time. However, there is a trend in fuel savings when using higher capacity routes.
... The results were generally positive with the adaptive system being more acceptable (subjectively) and effective (objectively) than one which was more rigid in its design. One of the studies personalized the system design such that the auditory alert in a forward collision warning system was tailored to a driver's brake reaction time and this adaptiveness was particularly appreciated by drivers who demonstrated a more aggressive driving style [16]. ...
Given the rapid progress being made in the design and development of autonomous vehicles, society is reaching the situation whereby customers will be able to access a range of semi-autonomous vehicles. These vehicles have the capability to drive autonomously in certain circumstances, with minimal input from the driver, except situations when a Request to Intervene is issued. While user requirements differ across and between types of users, there is no unified set of user requirements which will be acceptable to all drivers. Motivated by the recent explosion of interest around autonomous mobility, the authors made an attempt to extract, rank and compare the requirements that should be met according to different types of users - experts and non-experts. An initial set of user requirements was obtained, recognizing that drivers will have different priorities and preferences in this most critical of handover scenarios.
... To achieve this goal, it was crucial that the drivers didn't have indications about their monitoring as it could have an impact changing their behavior and consequently it could have distorted the initial indicators. (Ayyildiz et al., 2017;Beusen et al., 2009;Boriboonsomsin et al., 2010). ...
While diesel use represents one of the most important costs of the waste-collection process, the impact of eco-driving practices in this context has been surprisingly little addressed so far. Here, we present the results obtained by implementing eco-driving through the installation of in-board driving-assistance devices in a Spanish waste-collection fleet. Driving parameters and diesel use were monitored for over a year on 67 vehicles. An average fuel consumption decrease of 7.45% was observed, ranging from 1.86% to 11.50% according to the type of vehicle and to its waste-collection mechanism. Waste-transfer trucks that were not performing stop-and-go cycles displayed the highest values of fuel savings. In addition, eco-driving benefits obtained through real-time feedback did not tend to get lost over time, as fuel consumption remained remarkably steady. An average difference of only −0.45% between the first and the last month of monitoring was observed. After 14 months, an economic and environmental assessment of eco-driving implementation in the fleet was carried out. Nearly 120,000 L of diesel were economized, leading to substantial financial savings and to a significant exhaust emission decrease that was theoretically quantified in terms of CO2, CO, HC, NOx and PM. Overall, our results tend to show a highly positive environmental and economic impact of fuel-efficient driving in the waste-collection context.
This study investigates the relationship between drivers’ electrodermal activity (EDA) and their eco-driving behaviours through real-time monitoring. Electrodermal activity, a physiological marker of sympathetic nervous system arousal, reflects emotional and cognitive states, providing a valuable window into drivers’ internal experiences. EDA and driving data were collected for 48 trips from 10 different drivers. Cluster analysis and the Pearson correlation coefficient was used to uncover potential patterns between driver EDA and their driving behaviour as measured using a driving score. The results follow the Yerkes-Dodson Law. Driving performance increase with EDA arousal, but only to a point. The investigation has implications for enhancing road safety, as it contributes to our understanding of how drivers’ emotional states influence their on-road performance. Additionally, it holds promise for developing innovative in-car systems that can adapt to drivers’ changing emotional states, promoting safer and more comfortable driving experiences. Ultimately, this study bridges the gap between psychophysiology and transportation, shedding light on the often-overlooked emotional aspects of driving behaviour.
Recent technological advancements allow monitoring of drivers' behaviour and offer the opportunity for providing feedback. While this approach has been shown to have a positive effect on driver behaviour, whether it is accepted by drivers has not yet been extensively investigated. This questionnaire study examined the opinions of a sample of 628 Dutch drivers on the potential use of a monitoring and feedback system. The focus was on (1) whether drivers would be interested in being assessed, (2) whether data collection (i.e., monitoring) could be used for this purpose, and (3) which features the potential system must have in order to get accepted. The results showed that participants were moderately enthusiastic about the prospect of receiving monitoring feedback: on average, their opinion was between neutral and positive. Professional drivers expressed slightly more positive opinions, but no demographic variable was strongly associated with acceptability. Many drivers rated themselves as good drivers already and had low sensitivity to data collection, i.e., participants indicated being used to data collection online. If they were to use a monitoring and feedback device, participants indicated a preference for data on speed and forward-facing video footage and a preference for personalisation. The use of a monitoring and feedback system can be considered as a trade-off between sharing personal data and receiving support to improve driving skills. Based on the participants' reported online behaviour, it appears that the perceived costs associated with sharing data are small. The potential benefits of driver monitoring and feedback, however, are not salient to the participants, which may limit the use of such a system on the roads.
Resource conservation has become a hot topic, and driving habits also have a significant impact on a car’s fuel consumption. In view of the different needs of drivers with different driving habits for automatic transmission and shift characteristics, a vehicle automatic transmission, and shift correction control strategy based on driving habit recognition is proposed. Based on the analysis of drivers’ driving behavior, the phase space reconstruction method is first used to reconstruct the time series of driving control signals, and the driving habit identification and gear shift correction control are conducted based on the correlation dimension and Kolmogorov entropy evaluation index. Simulation and real car test show that the identification method based on phase space reconstruction method and driving habits evaluation index can accurately identify drivers’ driving habits. The gear shift correction control strategy based on driving habit recognition fully meets the different requirements of different drivers for the gearshift performance of vehicles and improves the intelligence degree of automatic transmission of vehicles.
We consider a signalized intersection under a partially connected automated vehicle (CAV) environment. There is a control center for the control zone that needs to predict the trajectories of human driving vehicles (HDVs) and control the trajectories of CAVs. By adopting model predictive control, we propose a real-time eco-driving strategy for the control center. In the proposed strategy, the Gipps’ car-following model is selected to update the acceleration of HDV and an optimal control problem (OCP) is proposed to optimize the trajectory of each CAV based on real-time travel information. The control objective is to minimize the total fuel consumption of each CAV during the current control period. Pontryagin’s minimum principle is employed to derive necessary optimality conditions under different scenarios. With the necessary optimality conditions, a numerical method is developed to solve the proposed OCP. Finally, numerical examples are provided to illustrate the performance of the proposed eco-driving strategy.
Green driving describes techniques that drivers can use to optimize their automobile fuel
economy. The energy in fuel consumed in driving is lost in many ways, including engine
inefficiency, aerodynamic drag, rolling friction, and kinetic energy lost to braking (and to a
lesser extent regenerative braking). Driver behavior can influence all of these. Since
climate change and humanity responsibility has been widely accepted, many drivers have a
new goal in mind: fuel efficiency. Eco-driving style is therefore often referred as smart
driving because of the necessary complex tradeoff between the multiple goals the driver has
to manage with.
Studies usually simplifies the green way to drive using simple advices easily understood by
drivers, but sometimes leading to a misunderstanding of the fuel efficient driving
strategy. Other studies used trial experiments before and after a training program to assess
the eco-driving impact.
Effects of eco-driving on fuel consumption are well described in the literature, but results
are often optimistic: CO2 emissions reduction can be up to 30% according to many studies.
The key question for policy makers is “how big” of an emission reduction we can get by
encouraging an eco-driving style, taking into account the diversity in the way to learn eco�driving: just reading a few driving tips, taking a course with a professional, or doing
practical exercises with equipped vehicles? Moreover, there is a need to understand the best
way to teach and learn eco-driving style, especially for young drivers.
MOTOR VEHICLES & MOTORS 2012
Road accidents and vehicular emissions are two significant issues related to road transportation, affecting both human life and the environment. Prior research suggests that driver behavior is a crucial factor in the majority of road crashes and is a significant factor influencing fuel consumption and vehicle emission. Significant improvement in driving behavior can be achieved by providing feedback to drivers about their driving behavior. An increasing interest among researchers to identify risky and non-economical driving maneuvers has led to the development of driver behavior profiling, i.e., rating/categorizing drivers into different categories based on how they drive. To get an insight into different parameters and methodology adopted by researchers for categorizing drivers into different categories, this paper presents a systematic review of studies on driver behavior profiling. In the present paper, PRISMA approach was adopted to shortlist the most relevant studies for systematic review out of 1231 initial studies, which were extracted using the relevant keywords. The findings from our study suggest that the selection of parameters for profiling the driver will depend on the application of the profiling scheme, type of device used for extracting data, and importance of parameter in rating criteria. Further, the findings suggest that significant improvement in driving behavior can be achieved by providing feedback to the drivers about their driving behavior and by implementing usage-based insurance schemes. It is also suggested that future studies shall focus on using smartphone devices for the collection of driver data as smartphones are nowadays easily accessible to everyone.
Modifying driving styles can help to reduce the energy use and emissions of driving without requiring changes to infrastructure or vehicle technology. Here, we evaluate the energy consumption and duration of trips before and after driving style changes. These modifications are made using emissions-friendly driving style heuristics that are easily implementable by drivers and do not require real-time feedback or on-board diagnostics. We use a data-driven approach to apply these heuristics to a representative baseline of U.S. drive cycles. The simulated driving-style improvements provide an average fuel savings per trip of 6%, alongside a 1.5% increase in trip duration. Decelerating early and reducing highway speeds can each contribute substantially to fuel savings. Accelerating more gradually contributes less. The percentage fuel savings are relatively consistent across locations and vehicle classes. These findings can inform several decision-makers, including drivers aiming to reduce fuel consumption, car manufacturers or software developers designing driving style feedback, and policy makers examining emissions savings opportunities.
To save more for taxi drivers, in this chapter, we present a two-phase fuel-efficient path-planning framework called GreenPlanner. In the first phase, we build a personalized fuel consumption model (PFCM) for each driver, based on the individual driving behaviors embedded in the GPS trajectory data and the physical features (e.g., traffic lights, stop signs, road network topology) along the routes provided by road network data. Furthermore, we build a general PFCM which only needs some basic information about the driver, including the category of overall fuel consumption performance in history and the car mode. In the second phase, with the real-time traffic information collected via the mobile crowdsensing manner, we are able to estimate and compare the fuel cost among different routes for a given driver, and recommend him/her with the most fuel-efficient one. We evaluate the two-phase framework using the real-world datasets, and results demonstrate that, compared to the baseline models, the proposed model achieves the best accuracy. Moreover, users could save about 20% fuel consumption on average if driving along the suggested routes in our case studies.
In October 2002 the first ISA-trial in Belgium was started in Ghent. Thirty-four cars and three buses were equipped with the "active accelerator pedal". In this system a resistance in the accelerator is activated when the driver attempts to exceed the speed limit. If necessary, the driver can overrule the system. The main research goals of the trial in Ghent were to evaluate the effects of ISA on speed-change, traffic safety, drivers' attitude, behaviour and drivers' acceptance. To study these effects of the ISA-system both surveys and logged speed data were analyzed. In the surveys drivers noticed that the pedal assisted them well in upholding the speed limits and that the system increased driving comfort. Most important drawbacks were technical issues. Data analysis shows a reduction in the amount of speeding due to the ISA-system. There is however still a large remaining percentage of distance speeding, especially in low speed zones. Differences between drivers are large. For some drivers speeding even increases despite activation of the system. For less frequent speeders average driving speed almost always increases and for more frequent speeders average speed tends to decrease. With the system, less frequent speeders tend to accelerate faster towards the speed limit and drive exactly at the speed limit instead of safely below, which causes average speeds to go up.
This book provides a new, quantitative assessment of the potential oil savings and costs of rapid oil demand restraint measures for transport. Some measures may make sense under any circumstances; others are primarily useful in emergency situations. All can be implemented on short notice – if governments are prepared.
The report examines potential approaches for rapid uptake of telecommuting, "ecodriving", and car-pooling, among other measures. It also provides methodologies and data that policy makers can use to decide which measures would be best adapted to their national circumstances. This "tool box" may help countries to complement other measures for coping with supply disruptions, such as use of strategic oil stocks.
The effects of training in fuel-efficient driving for bus drivers in a city environment were evaluated. Three dependent variables, hypothetically associated with such training, were used; fuel and accident data from the bus company, and driver acceleration behavior from five buses, over time periods of several years. Effects of temperature and number of passengers on fuel consumption were held constant. Fuelling and acceleration data yielded fairly similar results. It was found that, although the effects on these variables during training were very strong (as found in a previous study), these did not transfer well into the drivers’ working situation. Overall, the effect was about two percent fuel consumption reduction as a mean over 12 months after training. No effect was found for accidents, although a two percent reduction would not have been detectable. In a second phase of the study, 28 buses were equipped with Econen feedback equipment, which give an indication on how much fuel is used concurrently, resulting in a further reduction of consumption of about two percent.
Economical, ecological and safe driving (eco-driving) is aimed at reducing fuel consumption, greenhouse gas emissions and accidents. Eco-driving is concerned about driving in a way compatible with modern engine technology: smart, smooth and safe techniques that lead to potential fuel savings of 10–15%. The Centre for Renewable Energy Sources of Greece conducted an eco-driving pilot study in collaboration with the Organization of Urban Transportation of Athens, and the Thermo-Bus Company to assess the effects of changing urban bus drivers' driving style.
Linear Mixed-Effects * Theory and Computational Methods for LME Models * Structure of Grouped Data * Fitting LME Models * Extending the Basic LME Model * Nonlinear Mixed-Effects * Theory and Computational Methods for NLME Models * Fitting NLME Models
Making cars more fuel efficient: technology and policies for real improvements on the road
- Ecmt Iea
ECMT/IEA, 2005. Making cars more fuel efficient: technology and policies for real
improvements on the road. OECD/ECMT and IEA.