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The main goal of road transport companies, whose activity is transport of goods or passengers, is to constantly increase the efficiency of the resources at their disposal in order to increase the transport market competitiveness. As fuel costs compose one of the largest costs in overall business, their reduction has a significant impact on the final product price. The paper presents the eco - driving significance, that can be used as a toll for increasing the transport fleet efficiency. The driver’s eco - driving training was carried out at the Public transport company in Belgrade by instructors of RICo training centre (Accredited training institute). Analysing the driving parameters before and after training, there were achieved the savings from 9,1% to 20,41% in total fuel consumption, average fuel consumption and average CO2 emissions after training.

International audience; The present paper deals with how to design and test an eco-driving training tool in the form of a digital educational game, including a specific guidance system interface to teach eco-driving rules. We tested whether learners could reproduce the eco-driving behaviour and implement the rules once they were autonomous. We also aimed to validate the method as a relevant eco-driving teaching tool that does not distract drivers or affect safety behaviour. We examined the contribution of the guidance system to teach procedural skills compared with traditional teaching methods such as video instruction. Results reveal that both methods lead to reduced CO2 emissions, but that the reduction is greater with the interactive guidance system. Further analysis and an eye-movement study revealed no increase in driving time or effect on safety.

As road transportation has been identified as a major contributor of environmental pollution, motivating individuals to adopt a more eco-friendly driving style could have a substantial ecological as well as financial benefit. With gamification being an effective tool towards guiding targeted behavioural changes, the development of realistic frameworks delivering a high end user experience, becomes a topic of active research. This paper presents a series of enhancements introduced to an eco-driving gamification platform by the integration of additional wearable and vehicle-oriented sensing data sources, leading to a much more realistic evaluation of the context of a driving session.

Motivated by the observation that modifying driver behavior can significantly reduce fuel usage and CO2 emissions, this paper documents the development of a dedicated in-vehicle interface to support eco-driving. This visual interface has been tested in simulator conditions, demonstrating an 8.5% reduction in fuel use, and will soon be deployed on-road. Transitioning from simulator testing to on-road testing presents significant challenges to ensure driver safety and system effectiveness in the presence of changing road conditions and imperfect information about the current driving scenario.

This paper shows an eco-driving pilot program that has been implemented in the public transport company “JGSP Belgrade”, Serbia, in order to assess the possibilities of using eco-driving for an entire car fleet of the assessed company in the future. Eco-driving training and education of thirteen drivers were conducted in real driving conditions on the route length of 14 km and consisted of three phases. The results of eco-driving training of thirteen bus drivers confirm the findings of the previous researches that eco-driving has got many benefits. After the training, all drivers saw fuel consumption reduced by 8.61% on average, and consequently the average CO2 emission reduced by 8.61%. The implementation of eco-driving training in the assessed company and the attained fuel economy, leads to significant annual savings. However, some driving parameters were not significantly improved after training indicating a driver’s slow adaptation and application of new driving techniques.

EBSF, 3iBS, ZeEUS, EBSF_2 and ELIPTIC are research projects funded by the European Commission, with the aim of developing a new generation of buses across Europe. For all, the common task is two-pronged (i) it is necessary to develop innovative solutions to increase the attractiveness of this mode and (ii) to operate more environmentally-friendly vehicles. Prototypes with more comfortable and accessible internal layouts, advanced ITS-based solutions, new engines designed to save fuel and eco-driving are key working areas. The environmental concern is behind the majority of the tested innovations. These projects are based on demonstrators, that is the innovative measures are tested in real urban environments. The assessment is a classical before-vs-during the implementation of measures comparison. A transferability study is aimed to assess the theoretical exportability of the tested measures across Europe. Results so far stressed contrasting aspects within a common vision for the development of a new generation of buses: although stakeholders are willing to innovate bus fleets by introducing more sustainable propulsion systems, conventional modes are still favoured and the environmental concern is not a driver to export the tested measures. The objectives of the paper are to describe such outcomes, identify reasons which prevent a wider exploitation of cleaner buses, provide possible explanations and suggest recommendations to develop “green” awareness among bus stakeholders.

Transit agencies are always seeking opportunities to reduce fuel consumption, which typically provides simultaneous emissions reductions. However, the effectiveness and efficiency of fuel conservation and emission reduction alternatives differ as a function of local transit operating characteristics. The fact that emissions reductions from multiple alternatives are not necessarily additive further complicates these analyses. In selecting a set of alternatives, transit agencies need to evaluate multiple options simultaneously, and under agency-specific operating parameters. This paper presents the analysis of emissions reduction alternatives of eco-driving and purchasing new fleet with compressed natural gas, utilizing real-world operating data collected from the Metropolitan Atlanta Rapid Transit Authority (local transit) and the Georgia Regional Transportation Authority (express bus) fleets. Each individual emissions reduction alternative was first investigated for effectiveness individually and then in combination to assess cumulative impacts. The results show that the comparative performance of emissions reductions alternatives is highly dependent on vehicle operating characteristics, and thus the preferred alternatives, differ between different fleets. The analysis conducted for the Atlanta, Georgia region in the United States provides insight to how such modeling techniques can assist planning for future fleets given each agency's unique local conditions.

The new Automatic Train Operation (ATO) system over the standard European Rail Traffic Management System (ERTMS) will specify the requirements that an automatic train driving system must fulfil in order to be interoperable. The driving is defined by target times located along the journey that are received from the trackside system. Then, the on-board equipment drives the train with the objective of meeting all of the target times. The use of eco-driving methods to calculate the train driving is necessary, as one of the main goals of modern train driving systems is to increase the energy efficiency. This paper presents a simulation-based optimisation algorithm to solve the eco-driving problem constrained by multiple target times. This problem aims to minimize the energy consumption subject to a commercial running time, as the classical eco-driving problem, and also to meet intermediate target times during the journey between stations to enable automatic traffic regulation, especially at junctions. The algorithm proposed combines a Differential Evolution procedure to generate possible solutions with a detailed train simulation model to evaluate them. The use of this algorithm makes possible to find accurate speed profiles that meet the requirements of multiple time objectives. The proposed Differential Evolution algorithm is capable of finding the feasible speed profile with the minimum energy consumption, obtaining a 7.7% of energy variation in the case of a journey with one intermediate target time and 3.1% in the case of two intermediate targets.

International audience; In this paper, the calculation of eco-driving cycles for a Hybrid Electric Vehicle (HEV), using Dynamic Programming (DP), is investigated from the solving method complexity viewpoint. The study is based on a comparative analysis of four optimal control problems formulated using distinct levels of modeling. Starting with three state dynamics (vehicle position and speed, battery state-of-charge) and three control variables (engine and electric machine torque, gear-box ratio), the number of state variables is reduced to two in a first simplification. The other two simplifications are based on decou-pling the optimization of the control variables into two steps: an eco-driving cycle is calculated supposing that the vehicle is propelled only by the engine. Then, assuming that the vehicle follows the eco-driving cycle calculated in the first step, an off-line energy management strategy (torque split) for an HEV is calculated to split the requested power at the wheels between the electric source and the engine. As is shown, the decreased complexity and the * Corresponding author. decoupling optimization lead to a sub-optimality in fuel economy while the computation time is noticeably reduced. Quantitative results are provided to assess these observations.

Driving assistance systems (DAS) is a key technology to improve fuel economy for in-use vehicles. This also reduces the operational cost of running a fleet of these vehicles, such as city buses. In this paper, we develop a novel white-box evaluation model using machine learning for a manual transmission bus based on previous research about fuel consumption sensitivity to driving style. Using the proposed evaluation model, an algorithm for learning path planning (LPP) for a driving style is also proposed. The LPP method plans a step-by-step shortest learning path for different driving styles to achieve eco-driving, while increasing the driver’s acceptance and adaptation of DAS. Simulation results based on vehicle and engine physical models show that the proposed evaluation model, a pure data model, can be used as an alternative to physical model for the eco-driving prompt strategy. The results of the verification show that the proposed strategy can progressively guide the driver to improve the fuel consumption by 6.25% with minimal changes to driver’s driving task and driving style.

This paper discusses the results of applied research on the eco-driving domain based on a huge data set produced from a fleet of Lisbon's public transportation buses for a three-year period. This data set is based on events automatically extracted from the control area network bus and enriched with GPS coordinates, weather conditions, and road information. We apply online analytical processing (OLAP) and knowledge discovery (KD) techniques to deal with the high volume of this data set and to determine the major factors that influence the average fuel consumption, and then classify the drivers involved according to their driving efficiency. Consequently, we identify the most appropriate driving practices and styles. Our findings show that introducing simple practices, such as optimal clutch, engine rotation, and engine running in idle, can reduce fuel consumption on average from 3 to 5l/100 km, meaning a saving of 30 l per bus on one day. These findings have been strongly considered in the drivers' training sessions.

International audience; In this paper, the problem of eco-driving for a conventional vehicle equipped with an internal combustion engine is studied. The associated optimal control problem is formulated and solved using Dynamic Programming (DP). The impact of the mesh choice on the optimality of the DP solution is investigated in order to find a trade-off between the optimality of the DP solution and its computation time. The eco-driving speed trajectories obtained were tested on a high-frequency HIL (Hardware-In-the-Loop) engine test bench to quantify the real reductions in fuel consumption. Simulations and experiments are compared.

Freight transport is responsible for about 45% of total CO 2 emissions caused by mobility. To reduce its contribution without limiting the quantity of goods distributed, the technological improvement of vehicles and alternative fuels is only a partial solution. Indeed, it should be flanked by other integrative push- and pull-measures implemented at the policy level. Among them, eco-driving represents an option that covers strategic, tactical and operational decisions and provides suggestions to drivers, as well as real-time monitoring of their performance. This paper brings some evidence to such assertion, first providing a summary of the literature assessing the impacts of eco-driving, and then highlighting the limited amount of studies regarding freight transport. The results of an eco-driving field trial conducted in the Chinese province of Jiangsu, whose data were gathered using a method based on real-time normalized indicators specifically developed by Deutsche Telekom AG, are then presented. 15 heavy-duty and 10 light commercial vehicles were monitored over a period of four months, and information was collected on more than 5200 trips, covering a total distance of 439,000 km. A comparison between the driving styles before and after the training revealed a reduction of unitary fuel consumption for heavy-duty vehicles (−5.5%), while no significant variations were visible for light commercial vehicles. The application of this research method also yielded useful information about braking, acceleration and standstill, which are normally not considered in these types of evaluations, but can be highly valuable to drivers who wish to modify their behaviour towards a more efficient style of driving.

International audience; One of the most important research interests in the automotive sector is energy consumption and pollutant emissions reduction. Usage developments are continuing in parallel to technological evolution to maximize the efficiency of the system. One of the recent methods in usage field is eco-driving. It refers to a fuel economic driving behaviour which is the result of a trajectory optimization. In this research a parametric study on the impact of some optimization constraints on the optimal operation of a gasoline powered vehicle is presented. A vehicle model is described and the optimization algorithm based on dynamic programming is introduced. The optimization results on a defined trajectory under different road and trip constraints are discussed in this paper. These constraints include trip time and road grade, as well as the impact of state discretization. With the results, impact of these constraints on fuel consumption and CO2 emissions is presented and the trade-off between fuel consumption and trip time is discussed. The results show that optimal vehicle operation vary depending on road and trip constraints.

Eco-driving is a promising strategy for reductions in fossil fuel con-sumption and carbon emissions. Eco-driving is most frequently promoted via in-vehicle feedback. Eco-driving feedback studies demonstrate fuel economy improvements up to 18 %, but results are widely variable—partly due to the wide variation in feedback design. This paper addresses the need for a greater under-standing of how variations in eco-driving feedback design are related to its effec-tiveness. We identified characteristics of feedback with implications for behavior change based on behavioral theory and evaluation of a large sample of in-vehicle eco-driving feedback interfaces. We developed a typology of in-vehicle eco-driving feedback interfaces based on these characteristics. We identified 15 distinct types of in-vehicle eco-driving feedback interfaces. We describe each feedback type and discuss implications for feedback design. Our typology provides a foundation for subsequent research to determine most effective feedback types for particular behaviors, drivers, and driving conditions.

New in-vehicle systems provide the driver with speed advises through the variable message signs and the use of an in-car display. In this paper, two dynamic speed guidance strategies (green wave speed guidance strategy (GWSGS) and eco-driving speed guidance strategy (EDSGS)) through in-car display were tested. A new multi-vehicle driving simulator platform taking into account drivers interactions was built, and the two strategies for highway system were programmed through the script language provided by Virtools software. To evaluate the effectiveness of the overall strategies, 15 typical drivers were recruited to take part in the speed guidance experiments. It was found that the number of stops is both significantly reduced for the vehicles with the GWSGS and EDSGS. Compared with the vehicles without speed guidance, the fuel consumption and carbon dioxide (CO₂) emissions can be reduced by 25% and 13% under the EDSGS and GWSGS respectively. The eco-driving vehicle's velocity trajectory is smoother than that of the green wave vehicle, and the average compliance rate of EDSGS is higher than GWSGS. The EDSGS showed more benefits than the GWSGS.

The Netherlands Organisation for Applied Scientific Research (TNO) is engaged in research, development and testing of a range of technologies relating to hybrid and electric vehicle energy management and performance. The impact of driver behaviour on vehicle energy consumption is a significant factor, and one which can often be reduced with eco-driving, typically 5-10% or higher in some cases. Eco-driving can be extended not only to take into account information sources, improved user acceptance, and integration with powertrain control. In this paper, TNO illustrates the possible applications of model-based control for (hybrid) electric vehicles and shows preliminary results of the developed system.

International audience; Serious games present a promising approach to training and learning. The player is engaged in a virtual environment for a purpose beyond pure entertainment, all while having fun. In this paper, we investigate the effects of the use of serious game in eco-driving training. An approach has been developed in order to improve players’ practical skills in terms of eco-driving. This approach is based on the development of a driving simulation based on a serious game, integrating a multisensorial guidance system with metaphors including visual messages (information on fuel consumption, ideal speed area, gearbox management, etc.) and sounds (spatialized sounds, voice messages, etc.). The results demonstrate that the serious game influences positively the behavior of inexperienced drivers in ecological driving, leading to a significant reduction (up to 10%) of their CO2 emission. This work brings also some guidelines for the design process. The experiences lead to a determination of the best eco-driving rules allowing a significant reduction of CO2 emission.

International audience; In this paper, two simplified methods based on Dynamic Programming (DP) to solve an Eco-driving problem for a conventional vehicle equipped with an internal combustion engine are studied. The first method is based on the transformation of a time-based Optimal Control Problem (OCP) into a distance-based OCP while the second is based on solving the time-based OCP directly. The Pontryagin Minimum Principle (PMP) is used to decrease the complexity of the OCP formulation. Based on simulations, the two methods are compared in terms of optimality (fuel consumption) and the time needed to run the DP. The impact of the mesh choice on the optimality of the solution is also investigated.

Eco-driving is an efficient and economical way to reduce vehicles' energy consumption. Eco-driving techniques have been practised for decades. However, only conventional vehicles have been deeply studied particularly by using optimisation and modelling to identify the energetically best speed profiles. In this paper, eco-driving of hybrid vehicles is addressed. The complexity of speed profile optimisation problem is increased as we have to consider the two energies consumed on-board, i.e., fuel and electricity. Considering an existing hybrid electric vehicle (HEV) with its own energy management strategy, we propose here an original approach based on dynamic programming, vehicle modelling and an equivalence factor between the consumed fuel and electricity. Results show that the defined factor should be carefully chosen in order to achieve the best fuel economy.

The operation of groups of heavy-duty vehicles (HDVs) at a small inter-vehicular distance (known as platoon) allows to lower the overall aerodynamic drag and, therefore, to reduce fuel consumption and greenhouse gas emissions. However, due to the large mass and limited engine power of HDVs, slopes have a significant impact on the feasible and optimal speed profiles that each vehicle can and should follow. Therefore maintaining a short inter-vehicular distance as required by platooning without coordination between vehicles can often result in inefficient or even unfeasible trajectories. In this paper we propose a two-layer control architecture for HDV platooning aimed to safely and fuel-efficiently coordinate the vehicles in the platoon. Here, the layers are responsible for the inclusion of preview information on road topography and the real-time control of the vehicles, respectively. Within this architecture, dynamic programming is used to compute the fuel-optimal speed profile for the entire platoon and a distributed model predictive control framework is developed for the real-time control of the vehicles. The effectiveness of the proposed controller is analyzed by means of simulations of several realistic scenarios that suggest a possible fuel saving of up to 12% for the follower vehicles compared to the use of standard platoon controllers., Comment: 16 pages, 16 figures, submitted to journal

V posledních dekádách vznikly v rámci evropské vědecko-technické spolupráce účinné přístupy k ekologizaci dopravy, zvýšení její efektivnosti a zejména bezpečnosti. Po ověřovacích testech a pilotních studiích, které ukázaly účinnost nových přístupů, je nutné zajistit jejich implementaci, aby účinně sloužily, jak bylo zamýšleno. Tento příspěvek nastiňuje možnosti implementace do výukových a profesních programů a tím i do společenské praxe příslušných oborů. Je orientován na znalce v oblasti dopravy, zejména silniční dopravy a má informovat o novějších trendech v silniční dopravě v souvislosti s EU proklamovanou ekologizací zejména městské dopravy (ecological transport, greening of urban transport). Tyto trendy budou mít zřejmě dopad i na oceňování vozidel, ekologické a bezpečnostní inženýrství. In last decades there have been initialised in the frame of common European co-operation effective programmes of transport greening, enhancement of urban transport efficiency and traffic safety. After the stage of pilot studies and testing, that showed its effectivity, it is necessary next step to broad implementation that secures intended impact. This paper shows possibilities of implementation in learning and professional programmes and into common praxis of respective branches. That is why this call is oriented towards experts in transport branch, especially in road transport and has to inform about the newest trends of urban transport (ecological transport, greening of urban transport). These trends will have impact on vehicle evaluation and ecological engineering.

Eco-driving style is widely known to induce up to 20% fuel consumption reduction, but little is known on the effects of different learning methods. In order to evaluate the potential impacts of future ecological driving assistance system (EDAS), two kinds of experiments are analyzed in this paper: In the first one, simple advices are given to the participants, while in the second one, full courses with eco-driving experts were used. Different kind of statistical models are discussed, among which we choose to apply the ordinary logistic regression to assess the effects of each driving advice separately.

In order to reduce CO2 emission of automobiles, promotion of eco-driving is considered being effective. As a tool of promoting the eco-driving, some driver assistance systems have been developed for improving the drivers’ techniques by providing information after having evaluated the drivers’ behaviour when driving. What can make the drivers improve their driving to be more economically and ecologically attracts us to conduct this study. In this paper, factors affecting drivers’ improvement of eco-driving are reported on the basis of our analysis of the data collected from a social experiment undertaken during October 2009 and January 2010 in Toyota City.

Naturalistic driving (ND) is a research method that provides insight in everyday driver behaviour. Typically, in an ND study vehicles are equipped with several small cameras and sensors, which continuously and inconspicuously register vehicle manoeuvres, driver behaviour, and external conditions. This allows observation and analysis of the interrelationships between driver, vehicle, road, and other traffic in normal situations, conflict situations, and crashes. This paper introduces the concept of ND studies, among others in relation to more traditional research methods, gives a brief overview of previous and current ND studies and the closely related Field Operational Tests, and subsequently presents the main findings of the recently finished EU project PROLOGUE. This project investigated the feasibility and usefulness of carrying out a large-scale European ND study. Issues discussed include research questions, potential users and their interest, and the technical and methodological requirements with a special focus on the issue of safety critical events and crash risk estimates. Conclusions are presented in terms of 11 recommendations for a large-scale ND study in Europe.

To realize the sustainable transportation system in Japan, the issue to reduce CO2 emission made by the automobiles has become very serious. Among the countermeasures discussed and proposed, the eco-driving has been treated as very important and believed to be very effective. The eco-driving means changing the driving styles to be much more economically and ecologically. This does not only apply to the cars doing the eco-driving but also affect the cars following the eco-driving cars. Furthermore, by considering most of the studies up to now had been based on the computer simulations and there had been only a few studies that were based on the real situation, we conducted a test run by using vehicles on the public roads under the normal traffic conditions. This paper is to report a part of the results of our test run. The driving data were recorded by the on board unit FCM-GPS MMC produced by TECHTOM being used together with EC15WWF-40. The driving behaviors of the followed cars were recorded by the video camera taken from the back window of the last car of the eco-driving vehicle queue. The analysis mainly on that how many cars kept in following and how many cars gave up the following and then overtook the eco-driving car. As a result, we found that whether the following cars change their behaviors also depends on how perfect the followed car does the eco-driving. Here, the eco-driving is described by three indicators: tenderly starting, less change of speed and early accelerator off. Moreover, whether the eco-driving car has a sticker or not affected the behavior of the following cars, too.

Eco-driving is becoming more widespread as individual car-use behaviour is a cost-effective way of improving vehicle fuel economy and reducing CO2 emissions. The literature shows a wide range of efficiencies as a result of eco-driving, depending on route selection, traffic characteristic, road slope, and the specific impact evaluation method. This paper follows this line of research and assesses the impact of an eco-driving training programme on fuel savings and reduction of CO2 emissions in a well-designed field trial, focusing on the specific impacts according to road type. The methodology includes a comprehensive trial on different types of road sections under various traffic conditions, a processed dataset using R codes to integrate, clean, and process all the information collected, and a systematic method to evaluate the overall and specific impacts of eco-driving. The final results show a general fuel saving after eco-driving training of up to an average of 6.3% regardless of fuel and road type. Driving performance, as represented by selected parameters (average and maximum RPM, average and maximum speed, aggressive acceleration/deceleration), changed significantly after the training. The highest fuel savings are achieved on major arterial road sections with a certain number of roundabouts and pedestrian crossings. This work contributes to an understanding of the key factors for eco-driving efficiency according to road type under real traffic conditions. It offers greater insights for policymakers in road transport planning and for drivers when applying eco-driving techniques.

IEEE VPPC 2015 - Vehicular Power and Propulsion Conference, MONTREAL, CANADA, 19-/10/2015 - 22/10/2015; Electric vehicles are facing a real challenge as they are expected to reduce drastically energy consumption and air pollution of transport. However their performances remain under users' expectation mainly concerning the range and its fluctuation. 'Eco-behavior' use like eco-driving could help to reduce range weakness. This paper proposes an approach based on speed profile optimization that allows the extraction of the eco-driving rules. The optimization uses an experimentally validated model associated to a dynamic programming method. The study of the more impacting parameters showed high influence of the cruising speed value whereas the acceleration rate could be considered as a second order parameter excepting for high level time constraints.

Despite the recent technological improvements in vehicles and engines, and the introduction of better fuels, road transportation is still responsible for air pollution in urban areas due to the increasing number of circulating vehicles, and their relative travelled distances. We develop a methodology to calculate, in real-time, the consumption and environmental impact of spark ignition and diesel vehicles from a set of variables such as Engine Fuel Rate, Speed, Mass Air Flow, Absolute Load, and Manifold Absolute Pressure, all of them obtained from the vehicle’s Electronic Control Unit (ECU). Our platform is able to assist drivers in correcting their bad driving habits, while offering helpful recommendations to improve fuel economy. In this paper we will demonstrate through data mining, to what extent does the driving style really affect (negatively or positively) the fuel consumption, as well as the increase or reduction of greenhouse gas emissions generated by vehicles., This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01

[EN] This paper proposes an algorithm for obtaining the optimal average speed to save fuel and improve safety. The proposed algorithm is based on genetic algorithms. The algorithm uses information about the environment, the road and the vehicle for obtaining the optimal average speed which it minimizes fuel consumption without dramatically increasing the travel time. Moreover, the proposed algorithm improves safety adapting vehicle speed to road conditions. The environment information is obtained from web services and vehicle information is obtained through the OBD2 port. The algorithm is validated in situations with and without incidents. In addition, we analyze the impact of the average speed and acceleration incidents and their impact on fuel consumption., [ES] En este trabajo se propone un algoritmo para obtener la velocidad media óptima para ahorrar combustible y mejorar la seguridad. El algoritmo propuesto se basa en los algoritmos genéticos. El algoritmo emplea información sobre el entorno, la carretera y el vehículo para obtener la velocidad media que minimice el consumo de combustible sin incrementar drásticamente la duración del trayecto. Además, el algoritmo propuesto mejora la seguridad ya que adecua la velocidad a las condiciones de la vía. La información sobre el entorno se obtiene de servicios web y la información sobre el vehículo se obtiene a través del puerto OBD2. El algoritmo es validado en situaciones reales con incidentes de tráfico y sin ellos. Por otra parte, se analiza el impacto de la velocidad media y los incidentes de tráfico en las aceleraciones y su influencia en el consumo de combustible., The research leading to these results has received funding from the ARTEMISA project TIN2009-14378-C02-02 within the Spanish "Plan Nacional de I+D+I" and from the Spanish Ministerio de Economía y Competitividad funded projects IRENE (PT-2012-1036-370000), COMINN (IPT-2012-0883-430000) and REMEDISS (IPT-2012-0882-430000) within the INNPACTO program.

The European Commission (EC) has defined the objective to reduce carbon emission in transport by at least 60% compared to the year 1990. Until 2020 the increase of CO2 emissions shall be limited to 8% [1]. Therefore the EC is funding research projects which are dealing with eco-assistant/information systems such as the within the EU co-funded 7th Framework eCoMove project (Cooperative Mobility Systems and Services for Energy Efficiency) eCoMove. Based on in-vehicle but also cooperative systems such as dynamic eco-navigation, dynamic eco-guidance or other systems that support eco driving, which addressing route choice, velocity profile and traffic control information enable the driver to a more eco-friendly driving style which shall result in reduced fuel consumption. . In order to reach the envisaged goal it is very important that the systems are accepted by the driver and that driver comply with the system’s recommendations. Different methods, such as field trials, driving simulator studies and microscopic network simulations have been applied to verify and validate different applications with the main goal to assess the impacts of eco driving support systems on potential reduction of fuel consumption and carbon emission. The current paper provides an overview of the methodology used to validate the implemented tools and the findings of the conducted studies.

International audience; In this work the trade-off between economic, therefore fuel saving, and ecologic, pollutant emission reducing, driving is discussed. The term eco-driving is often used to refer to a vehicle operation that minimizes energy consumption. However, for eco-driving to be environmentally friendly not only fuel consumption but also pollutant emissions should be considered. In contrast to previous studies, this paper will discuss the advantages of eco-driving with respect to improvements in fuel consumption as well as pollutant gas emissions. Simulating a conventional passenger vehicle and applying numerical trajectory optimization methods best vehicle operation for a given trip is identified. With hardware-in-the-loop testing on an engine test bench the fuel and emissions are measured. An approach to integrate pollutant emission and dynamically choose the ecologically optimal gear is proposed.

International audience; Nowadays, the eco-driving technique represent one possible mean to reduce the energy consumption and the pollutants emission of vehicles. Besides the very low investment needed for such an option, its suitability to be applied even to old vehicles makes its impact important and immediate. The work presented here aims to quantify in a first approach the potential gain allowable by an eco-driving behavior for a given vehicle using numerical optimization methods (only the case of conventional ICE based vehicle is detailed in this paper). Then, an Advanced Driving Assist System (ADAS) based on the proposed optimization has been designed and tested. The design approach takes into account the different constraints that could appear like the traffic limitation, the safety, the pollution but also those related to the driver receptivity and his ability to understand and apply the information given.

Proceedings of 2014 International Conference on Connected Vehicles and Expo (ICCVE,IEEE), took place 2014, November, 03-07, in Viena (Austria). In this paper, we propose a learning method for eco-driving based on imitation. The system uses Data Envelopment Analysis (DEA) in order to calculate the driving efficiency from the point of view of the fuel consumption. The input and output parameters have been selected taking into account the Longitudinal Vehicle Dynamics Model. This technique allows us to notify the user about who is the most efficient driver close to him or her and to suggest the imitation of the behavior of such driver. The proposed method promotes learning by observation and imitation of efficient drivers in a practical rather than theoretical way such as attending eco-driving lessons. The DEA algorithm does not depend on the definition of a preconceived form of the data in order to calculate the efficiency. The DEA algorithm estimates the inefficiency of a particular DMU by comparing it to similar DMUs considered as efficient. This is very important due to the dynamic nature of the traffic. A validation experiment has been conducted with 10 participants who made 500 driving tests in Spain. The results show that combining eco-driving lessons with the proposed learning system, drivers achieve a very significant improvement on fuel saving (15.82%) The research leading to these results has received funding from the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R within the Spanish "Plan Nacional de I+D+I" under the Spanish Ministerio de Economía y Competitividad and from the Spanish Ministerio de Economía y Competitividad funded projects (co-financed by the Fondo Europeo de Desarrollo Regional (FEDER)) IRENE (PT-2012-1036-370000), COMINN (IPT-2012-0883-430000) and REMEDISS (IPT-2012-0882-430000) within the INNPACTO program. Publicado

In this paper, we present our work towards an auditory display that is capable of supporting a fuel-efficient operation of vehicles. We introduce five design approaches for employing the auditory modality for a fuel economy display. Furthermore, we have implemented a novel auditory display based on one of these approaches, foussing on giving feedback on the engine's optimal rpm range, which is a major factor for eco-driving. Finally, we report on the development of a simple but physically realistic car simulator, which allows for a reproducible evaluation of prototype auditory displays as well as a comparison to state-of-the-art visual fuel efficiency indicators.

Vehicles in the US use approximately 1.4 billion liters of fuel a day. This number can be reduced by driving more fuel efficiently. Several sources provide fuel saving eco-driving advice, but the advices are often quite abstract. This paper uses a large set of high-frequent GPS and Controller Area Network Bus (CANBus) data from four similar vehicles to evaluate the eco-driving advice. The CANBus data provides information such as the fuel consumption per second and rounds per minute of the engine. The vehicles are compared and evaluated on the drivers' ability to follow the eco-driving advice. This comparison shows that there is a 23% difference in the fuel efficiency. This difference is due to quite different speed and acceleration profiles of the drivers but also that stopping at a traffic light consumes double the fuel compared to not stopping. Overall it is shown that there is good potential for saving fuel by following the eco-driving advice. However, no single advice dominates, and multiple advices are found to have a positive effect on the fuel efficiency.

During the past decade, train Driver Advisory Systems (DAS) have been an emerging topic. Many projects have been started with the goal to increase punctuality and decrease energy consumption. We found that many systems provide too little information, e.g. only a speed advice, and thus do neither improve drivers’ skills nor their Situation Awareness (SA) of the traffic situation. In collaboration with train drivers, we developed key concepts for future DAS. This paper discusses today’s problems and principles to solve them. We explain how SA can be supported and how this can improve overall quality of train traffic. Future Train Traffic Control

Sustainable mobility is the basic and long-term goal of the traffic policy. Eco-driving represents one of 40 measures that should by 2050 contribute to 60% of traffic-generated emission reduction. The paper presents the significance of educating the drivers about eco-driving as well as eco-drive training with the aim of reducing fuel consumption and CO2 emission. During research the drivers were tested in three cycles, prior to education, immediately following the education and eco-training and three months after the eco-training. The analysis of the results shows that driving according to eco rules allows fast and efficient reduction of fuel consumption and CO2 emissions, which means at the same time also the need to systemically educate the drivers about the eco-driving at specialized educational centres which is for the moment absent in the Republic of Croatia.

The Netherlands Organisation for Applied Scientific Research (TNO) is engaged in research, development and testing of a range of technologies relating to hybrid and electric vehicle energy management and performance. The impact of driver behaviour on vehicle energy consumption is a significant factor, and one which can often be reduced with eco-driving, typically 5-10% or higher in some cases. Eco-driving can be extended not only to take into account information sources, improved user acceptance, and integration with powertrain control. In this paper, TNO illustrates the possible applications of model-based control for (hybrid) electric vehicles and shows preliminary results of the developed system.

Eco-driving is a set of driving techniques which lead to a reduction in fuel consumption and CO2 emissions. According to different authors, the use of eco-driving can reduce fuel consumption of 5% to 30%. Modern information and communication technologies can provide support to the driver by giving an insight into the parameters of driving (average fuel consumption, CO2 emissions, maximum speed, maximum revolutions per minute, etc.). This paper will present a set of information and communication technologies that enable the collection and analysis of parameters for determining the effectiveness of eco-driving. Collection of data on vehicle and driving, real-time display and post-processing of that data is one of the ways to support the driver. Further analysis and comparison of data on vehicle and driving determines efficiency of eco driving techniques.

The Netherlands Organisation for Applied Scientific Research (TNO) is engaged in research, development and testing of a range of technologies relating to hybrid and electric vehicle energy management and performance. The impact of driver behaviour on vehicle energy consumption is a significant factor, and one which can often be reduced with eco-driving, typically 5-10% or higher in some cases. Eco-driving can be extended not only to take into account information sources, improved user acceptance, and integration with powertrain control. In this paper, TNO illustrates the possible applications of model-based control for (hybrid) electric vehicles and shows preliminary results of the developed system.

Eco-driving allows the driver to optimize his/her behaviour in order to achieve several types of benefits: reducing pollution emissions, increasing road safety, and fuel saving. One of the main rules for adopting eco-driving is to anticipate the traffic events by avoiding strong acceleration or braking and maintaining a steady speed when possible. Therefore, drivers have to comply with speed limits and time headway. The present study explored the role of three types of motivation and social norms in predicting French drivers- intentions to comply with speed limits and time headway as eco-driving practices as well as examine the variations according to gender and age. 1234 drivers with ages between 18 and 75 years old filled in a questionnaire which was presented as part of an online survey aiming to better understand the drivers- road habits. It included items assessing: a) behavioural intentions to comply with speed limits and time headway according to three types of motivation: reducing pollution emissions, increasing road safety, and fuel saving, b) subjective and descriptive social norms regarding the intention to comply with speed limits and time headway, and c) sociodemographical variables. Drivers expressed their intention to frequently comply with speed limits and time headway in the following 6 months; however, they showed more intention to comply with speed limits as compared to time headway regardless of the type of motivation. The subjective injunctive norms were significantly more important in predicting drivers- intentions to comply with speed limits and time headway as compared to the descriptive norms. 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Proceedings of the 1st IEEE International Conference on Consumer Electronics - Berlin (IEEE ICCE-Berlin 2011), September 6 - 8, 2011, Berlin, Germany This paper proposes an eco-driving assistant that facilitates the user to learn the techniques of efficient driving. The Artemisa´s assistant evaluates the driver´s driving style taking into account some environmental as well as some vehicles´s variables such as speed, gear, R.P.M, etc. Besides, tips are inferred to teach efficient driving habits. Compared to other similar systems, the Artemisa’s assistant runs on a mobile device with Android O.S. and it does not need to install additional hardware Proyecto CCG10-UC3M/TIC-4992 de la Comunidad Autónoma de Madrid y la Universidad Carlos III de Madrid

The eco-driving concept consists on applying a set of rules while driving to save fuel. Eco-driving has acquired great importance in recent years because it is a way to reduce energy consumption that can be applied to any type of vehicle. However, for these rules to be applied requires a process of continuous learning and motivation. For this reason, many eco-driving assistants have emerged. The problem of these assistants is that they are dependent on the model of vehicle, expensive and imprecise. In this context, this paper presents a novel efficient driving assistant that uses the features of the Smartphone to accurately model the driver's driving style from the point of view of energy consumption and generate eco-driving tips to correct the bad driver's driving habits Proyecto CCG10-UC3M/TIC-4992 de la Comunidad Autónoma de Madrid y la Universidad Carlos III de Madrid

This paper presents the main elements of a project entitled ICT-Emissions that aims at developing a novel methodology to evaluate the impact of information and communication technology (ICT)-related measures on mobility, vehicle energy consumption and carbon dioxide (CO2) emissions of vehicle fleets at the local scale, in order to promote the wider application of the most appropriate ICT measures. The proposed methodology combines traffic and emission modelling at micro and macro scales. These will be linked with interfaces and submodules which will be specifically designed and developed. A number of sources are available to the consortium to obtain the necessary input data. Also, experimental campaigns are offered to fill in gaps of information in traffic and emission patterns. The application of the methodology will be demonstrated using commercially available software. However, the methodology is developed in such a way as to enable its implementation by a variety of emission and traffic models. Particular emphasis is given to (a) the correct estimation of driver behaviour, as a result of traffic-related ICT measures, (b) the coverage of a large number of current vehicle technologies, including ICT systems, and (c) near future technologies such as hybrid, plug-in hybrids, and electric vehicles. The innovative combination of traffic, driver, and emission models produces a versatile toolbox that can simulate the impact on energy and CO2 of infrastructure measures (traffic management, dynamic traffic signs, etc.), driver assistance systems and ecosolutions (speed/cruise control, start/stop systems, etc.) or a combination of measures (cooperative systems).The methodology is validated by application in the Turin area and its capacity is further demonstrated by application in real world conditions in Madrid and Rome.