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Abstract: This paper presents a re-analysis of the data used in a previously published paper ‘Using on-board data logging devices to study the longer-term impact of an eco-driving course’ by Beusen et al. In this paper the effect of an eco-driving course on fuel consumption was studied. The fuel consumption of 10 drivers was monitored during a year. After half a year they received an eco-driving course. The main conclusion of this paper was that fuel consumption was reduced with 5.8% after the course and that the effect for the group as a whole was permanent up to 6months after the course. The data were analyzed again, including the effect of the ambient temperature on fuel consumption. A higher ambient temperature results in a lower fuel consumption. The main conclusion of our first paper still holds: an eco-driving course results in a significant decrease of the fuel consumption. However, this effect is gradually lost in the months after the course. In the first paper the effect seemed permanent because it was masked by the effect of increasing ambient temperature. [Copyright &y& Elsevier]

• European naturalistic driving data reveal behavioral differences across countries. • European drivers do not necessarily behave like U.S. drivers. • European drivers use their mobile phones less while driving than U.S. drivers. • The UDRIVE dataset provides more in depth understanding of driver behavior. • Introduction of the conceptual Framework for Naturalistic Driving Studies (FNDS). • FNDS gives insight in scope of study in the relation to sample and data collected. Within the UDRIVE project, a rich cross-European naturalistic driving database was created which includes everyday driving data on car and truck drivers and powered two-wheeler riders. The database provides extensive, reliable insights into driving behavior in real traffic as a foundation for improving the safety and sustainability of European road traffic. This paper discusses the characteristics of the data in the UDRIVE database—elucidating key methodological choices and presenting a selection of results to date. A priority of the study design was obtaining in-depth information on driving behavior, permitting the exploration of diverse research questions. A tailor-made data acquisition system collected very comprehensive data. A total of 287 drivers/riders participated. The sample size restricts the addressable research topics to common behaviors in everyday driving and limits the generalizability of results. However, the data are extensive and promising analyses have already been performed. The results show differences between European countries for distracting activities, seatbelt use, and looking behavior towards cyclists at urban intersections. Moreover, it shows that European drivers engage less in mobile phone use than U.S. drivers. It is likely that European drivers differ in other ways, also—highlighting the dataset's value for developing and implementing targeted safety measures, for the E.U. and its individual countries. Based on the comparison of the different studies, the paper introduces the general conceptual framework for naturalistic driving studies, providing insight in the relation between the scope of a naturalistic driving study and the key methodological choices on sample selection and data acquisition system. [ABSTRACT FROM AUTHOR]

The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco-driving strategy for autonomous electric vehicles to navigate continuous signalized intersections. This strategy incorporates the realistic powertrain's working efficiency and constraints into the vehicle motion optimization. In the domain of the global path, we formulate a co-optimization problem of vehicle motion and powertrain operation to tap the vehicle energy-saving potential and derive an optimal velocity profile, considering constraints related to queue release and limit speed in intersections. In the short rolling time domain, we propose a predictive control approach for vehicle speed tracking and real-time powertrain control. Simulation results under various scenarios show the advantages of the proposed method in terms of energy consumption compared to the baseline and existing methods. • An eco-driving strategy at continuous speed-limit signalized intersections. • The varying speed limits, traffic signal period, and queue effect are considered. • The powertrain efficiency and constraints are incorporated into motion planning. • A long-term co-optimization model of vehicle motion and powertrain operation. • A short-term optimal velocity tracking control for smooth powertrain operation. [ABSTRACT FROM AUTHOR]

• Eco-driving impacts are tested in a field trial with 1156 trips in two cities. • Fuel consumption is associated with both driving patterns and road conditions. • Eco-driving training influences directly drivers' driving habits. • Traffic congestion and steep road slope negatively influence fuel consumption. Eco-driving, as individual car-use behavior, is a cost-effective way of improving fuel efficiency, reducing CO 2 emissions and other air pollutants like NO x. This paper aims to expand the knowledge on the short-term impacts of eco-driving by developing an analytical model of the key factors that explain fuel consumption and eco-driving, and to examine their relations in greater depth. Additionally, this paper analyses the effects on drivers' stress levels after eco-driving. An eco-driving field trial is applied to collect real data of 1156 trips, using two vehicles and 24 drivers (42% Female; Age, M = 30.15; Years of driving experience, M = 10.30) in two Spanish cities with different road characteristics. A sequential method involving factor analysis, regression analysis and path analysis is used to analyze the sample. The results confirm that eco-driving is strongly affected by driving behavior like deceleration rate, RPM and speed, also showing that external factors as congestion and road slope have a direct influence on fuel consumption. The results also reveal that perceived stress levels vary among drivers, but there is not significant change when drivers eco driving. [ABSTRACT FROM AUTHOR]

The energy-efficient train operation methodology is the focus of this paper, and a Q-Learning-based eco-driving approach is proposed. Firstly, the core idea of energy-distribution-based method (EDBM) that converts the eco-driving problem to the finite Markov decision process is presented. Secondly, Q-Learning approach is proposed to determine the optimal energy distribution policy. Specifically, two different state definitions, i.e., trip-time-relevant (TT) and energy-distribution-relevant (ED) state definitions, are introduced. Finally, the effectiveness of the proposed approach is verified in a deterministic and a stochastic environment. It is also illustrated that TT-state approach takes about 20 times more computation time compared with ED-state approach while the space complexity of TT-state approach is nearly constant. The hyperparameter sensitivity analysis demonstrates the robustness of the proposed approach. • The inverse problem of energy-efficient train control problem is formulated. • A data-driven method based on Q-Learning approach is proposed. • Two state definitions based on trip time and energy distribution are introduced. [ABSTRACT FROM AUTHOR]

Electric vehicles have been considered into effective solutions to address energy problems in urban traffic systems for their remarkable performance in energy costs and carbon emissions reduction. However, the energy-saving effect of electric vehicles in urban traffic systems is restricted due to the complexities arising from mixed traffic conditions. To improve energy efficiency, this paper proposes an eco-driving-based mixed vehicular platoon control model for successive signalized intersections with connected and autonomous vehicles (CAVs), connected vehicles (CVs) and manned vehicles (MVs). Firstly, the dynamics model and spacing policy are analyzed, then the optimal platoon size and speed are calculated according to different traffic scenarios. Secondly, the mixed vehicular platoon control algorithm is established by considering traffic timing, energy consumption and string stability. Thirdly, an actual eco-driving-based platoon control system for tested electric vehicles is designed. The field test results demonstrate that all platoons can pass through intersections during green phase with minimum energy consumption, meanwhile the string stability can be guaranteed. It is noteworthy that the proposed model can greatly improve urban traffic capacity and energy efficiency. • A novel dynamical model of mixed vehicular platoon is explored by integrating different characteristics of CAV, CV, and MV. • An optimal speed algorithm of the leading vehicle to pass through successive signalized intersections is proposed according to signal timing, vehicular speed, and entry time. • The eco-driving-based mixed vehicular platoon control model is established, and the multi-objective function considering traffic environmental constraints are formulated to minimize the travel time and energy consumption. [ABSTRACT FROM AUTHOR]

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. • A normalized energy consumption model is presented, combining two energy usage. • A novel eco-driving strategy is developed based on the proposed energy consumption. • The algorithm suits both free traffic and car-following scenarios with safety. • The characteristics of the four eco-driving strategies are compared. • The proposed strategy is verified by both simulation and vehicle-in-the-loop tests. [ABSTRACT FROM AUTHOR]

• Vehicle speed computation designed to give driver indications based on target speed. • Target speed calculation based on Predictive Cruise Control for energy reduction. • Speed computation algorithm valid to any vehicle type and powertrain architecture. • Simulated energy reduction achieved on real scenarios between 3% and 11%. • Algorithm suitable for on board implementation and real time use on vehicles. Energy reduction in automotive applications is addressed since many years by both academia and vehicle manufactures. Recently, the introduction of innovative technical solutions, based, for instance, on internet connectivity and Advanced Driver-Assistance Systems (ADAS), allowed the on-board implementation of complex Energy Management Strategies (EMS) based on advanced optimization algorithms. However, these solutions require, in most cases, high computational capabilities and are generally customized on vehicle type and addressed to hybrid powertrain configurations. In this regard, this paper presents a vehicle speed management algorithm that suggests in real time to the driver the speed that shall be followed to achieve fuel consumption reduction. The algorithm requires road information, such as speed limits and slope variation, but it can be applied to any vehicle (e.g., light-weight cars, busses, trucks, etc.) and powertrain configuration (conventional, hybrid, pure electric, etc.). The basic structure relies on Predictive Cruise Control (PCC) approach, without introducing any complex mathematical optimization algorithm. The algorithm performance is here investigated in simulated environment by analyzing different vehicle types and routes. Under all the addressed scenarios, the algorithm can allow fulfilling energy consumption reduction (within the range of 3%-11% for real scenarios) with respect to a given reference speed profile under the same travel time. [ABSTRACT FROM AUTHOR]

Road vehicles can obtain traffic information via Intelligent Transportation System (ITS), which yields more potential in improving driving performance. However, ITS is not available for off-road vehicles and only limited information can be obtained by onboard sensors. Meanwhile, the off-road terrain suffers from terrible road conditions, which brings great difficulties to ensuring driving safety. Therefore, how to coordinate fuel economy, vehicle mobility and driving safety with limited traffic information is a challenging problem for off-road vehicles. To tackle the problem, an online predictive eco-driving strategy is proposed in this paper, which consists of safety supervisory module, time reference generator and rolling optimization. Firstly, considering the off-road terrain characteristic, the safety supervisory module analysis the vehicle stability performance under different road conditions, and thus the driving safety on off-road terrain with low adhesion coefficient, high curvature and heavy grade can be guaranteed. Secondly, the time reference generator is designed to ensure vehicle mobility. With only a prior knowledge of distance to destination, the time reference generator can generate the reference time in prediction horizon fast and effectively. Finally, model predictive control is employed to construct the multi-objective eco-driving problem, with an ameliorated particle swarm optimization to minimize the fuel consumption while tracking the reference time and ensuring driving safety. Simulations are conducted to validate the effectiveness of the proposed strategy. The results exhibit that the fuel economy and vehicle mobility can be improved by 10.13% and 5.77% over practical strategy in the premise of ensuring driving safety under the 5 km off-road terrain scenario. Moreover, a hardware-in-loop test is implemented to verify the real-time ability of the proposed strategy. • An online predictive eco-driving control strategy is proposed. • The necessary conditions for vehicle stability are deduced to ensure driving safety. • The time reference generator is designed to control travel time precisely. • Coordinative optimization for vehicle velocity and power distribution. • The proposed strategy is validated in both simulation and hardware-in-loop test. [ABSTRACT FROM AUTHOR]

This paper presents a safety-enhanced eco-driving strategy for connected and autonomous vehicles (CAVs), which is implemented by a hierarchical and distributed framework. The driving risk field, shockwave theory, and motion planning and control method are integrated into this framework to optimize the trajectories of CAVs on a signalized arterial under mixed traffic flow, with the aim of reducing the driving risk and fuel consumption of CAVs simultaneously, while ensuring traffic efficiency. The optimization procedure is mainly composed of two parts: long-term trajectory planning based on optimal control and short-term trajectory control based on model predictive control, which makes the strategy more adaptable to the various traffic conditions. The results show that the proposed framework can effectively reduce the safety risk that vehicles are exposed to and their fuel consumption by 18%–24% and 20%–27%, respectively. Furthermore, it reveals that conventional eco-driving strategies may result in negative safety issues when only considering the impact of preceding vehicles on the eco-CAV. However, these negative impacts can be eliminated when the impacts of following vehicles on the eco-CAV are taken into account. In addition, the sensitivity analysis on the Market Penetration Rate (MPR) of CAVs and traffic demand is performed. The results show that the framework is robust and can work under various traffic conditions (including under-saturated and over-saturated ones) and different MPRs. • Propose a safety-enhanced eco-driving strategy for CAVs under mixed traffic flow. • Develop two algorithms to alleviate intersection queuing effects for eco-driving. • While ensuring traffic efficiency, optimize CAVs' fuel consumption and driving risk. • Perform numerical experiments to assess the benefits of the proposed framework. • Reveal conventional eco-driving strategy may result in safety risks. [ABSTRACT FROM AUTHOR]

This paper reports the findings of an eco-driving trial that was designed enable users to make pre-trip and on-route decisions when driving as to the optimal route to take. The basis of this paper will be to estimate how efficiently drivers are performing in relation to fuel consumption per kilometres (km). The analysis uses details on the vehicle specification, in terms of fuel efficiency, and relates this to the distance travelled to provide the user with information on the efficiency per km travelled. Eco-driving involves the training of individuals to change their driving patterns and to adapt to driving conditions. The results of the study show that eco-driving feedback is a powerful tool and how it can be used to reduce emissions. [ABSTRACT FROM AUTHOR]

• Onboard eco-driving feedback improves fuel economy by 6.6% on average. • The impact of most onboard feedback tested to-date deteriorates over time. • Eco-driving feedback should include both fine- and course-grained information. • Eco-driving feedback should include performance standards. • Multimodal eco-driving feedback may be more effective than visual only. • Gamification may enhance eco-driving feedback effectiveness. • Interventions should combine eco-driving feedback with education and rewards. Eco-driving refers to suites of behavior a driver can engage in to improve fuel economy. The most common strategy used to promote eco-driving is onboard feedback that conveys information about fuel efficiency to the driver. This paper presents a statistical meta-analysis of eco-driving feedback studies in order to determine a weighted estimate of the average impact of feedback on fuel economy and explore potential moderators of its effectiveness, particularly regarding features of the feedback interface design. The main effect of onboard feedback on fuel economy across the final sample of 17 studies and 23 effect sizes was 6.6% improvement. Feedback that included information about both instantaneous and accumulated performance predicted larger effects. Though not statistically significant, trends in relationships between other feedback design features and fuel economy outcomes aligned with study hypotheses. Length of feedback intervention negatively related to effects, and pairing feedback with instructions or rewards predicted larger effects. [ABSTRACT FROM AUTHOR]

In this paper, we consider a special scenario where connected (V2V and V2I) vehicular technologies are used to alert motorists when they approach a hazardous zone, such as a low visibility area, and recommend proper speeds. We present the principles of the proposed safety driving system and compare the performance of V2V and V2I communications in terms of road safety effectiveness and network communication efficiency. This performance analysis is based on extensive computer simulation experiments by adapting the iTetris platform under various scenarios. We also explore, via simulations, whether CHAA systems, based on V2V and V2I communications can potentially contribute towards eco-driving by reducing Carbon Dioxide (CO 2) emissions. Our simulation results showed that our alerting system, based on V2I communication, yields better message reception rate and better safety efficiency compared to a V2V alert system. The results also show that the proposed CHAA system can contribute as well towards reduced CO 2 emissions by promoting speed harmonization. [ABSTRACT FROM AUTHOR]

The emergence of the intelligent transportation system and cloud computing technology has brought the available traffic information and increasing computing power, which lead to a significant improvement in driving performance. In order to enhance energy economy and mobility simultaneously, a hierarchical eco-driving strategy is proposed in this paper, which is comprised of the cloud-level controller and the vehicle-level controller. The dynamic programming-based cloud-level controller optimizes the velocity and battery state-of-charge utilizing the global traffic information obtained from the intelligent transportation system. However, the global traffic information suffers from uncertainties, which deteriorates the effectiveness of the cloud-level controller. The vehicle-level controller is constructed on the model predictive control framework, aiming to cope with the uncertainties, improve fuel economy and reduce travel time. Besides, a transfer learning-based particle swarm optimization algorithm is presented for solving the optimization problem in model predictive control, which can achieve great control performance utilizing the knowledge from the cloud-level controller. To validate the effectiveness of the proposed strategy, simulation tests are conducted. The results demonstrate that the proposed strategy can achieve near-global-optimal performance in fuel economy and mobility. Moreover, the real-time performance of the proposed strategy is validated through the hardware-in-loop test. • A hierarchical eco-driving strategy is designed using vehicle-to-cloud connectivity. • The uncertainties in traffic information are considered and tackled. • A novel transfer learning-based partial swarm optimization algorithm is proposed. • Coordinative optimization for vehicle velocity and power distribution. • The proposed strategy is validated in both simulation and hardware-in-loop test. [ABSTRACT FROM AUTHOR]

• A large naturalistic driving database from Athens is exploited to compare driving behavior before, during and after the COVID-19 pandemic. • Significant changes in number and duration of trips and traffic conditions are observed before and after the two lockdown periods. • An unsupervised learning approach is followed to discover driving profiles and their emergence during the above periods. • Results indicate that, during and after the pandemic, drivers are driving in a smoother and less aggressive way than before the pandemic. The outburst of the COVID-19 pandemic has significantly disrupted the operation of the existing transportation systems, decreasing the total number of trips, as well as the habits of the majority of the travelers. In this paper, the impact of the pandemic on driving behavior and, specifically, on ecological driving is investigated. First, a clustering approach is adopted to identify different driving profiles in a dataset of naturalistic driving data. It was found that there are three discrete profiles, each with its respective characteristics: aggressive, eco and typical. Furthermore, statistical analysis and comparison of the distribution of the trips in the three profiles were conducted in four different time periods: before the lockdowns, during the first lockdown, between the two lockdowns and during the second lockdown. Interestingly, the results indicate that during the above period, the drivers gradually moved to smoother and, thus, safer and more ecological driving behavior. [ABSTRACT FROM AUTHOR]

Modern powertrain control design practices rely on model-based approaches accompanied by costly calibrations in order to meet ever stringent energy use and emissions targets specified for standardized drive cycles. These practices struggle to capture the complexity and uncertainty of real-world driving. However, the deluge of operational data now available with connected vehicle technology presents opportunities to foster data-driven control design methods that adapt the powertrain control systems to their field use conditions. While the most attractive of these methods is reinforcement learning (RL), it is rarely directly applicable in physical applications due to its challenges of learning efficiency (sample complexity) and guaranteeing safety. In this paper, we propose and evaluate an adaptive policy learning (APL) framework that leverages existing source policies shipped with vehicles to accelerate initial learning while recovering the asymptotic performance of a reinforcement learning-based powertrain control agent trained from scratch. We present a critique of related residual policy learning approaches and detail our algorithmic implementations for two versions of the proposed framework. We find that the APL powertrain control agents offer in the order of 10% fuel economy improvement over a default powertrain controller of a commercial vehicle without compromising driver accommodation metrics. We demonstrate that the APL frameworks offer a viable approach towards potentially applying RL for real-world scenarios by addressing its learning efficiency issues. • Adaptive Reinforcement Learning (RL) of vehicle powertrain control for eco-driving • Accelerated policy learning by leveraging existing source control policies • Evaluation of proposed algorithms with conventional RL via Monte Carlo simulations [ABSTRACT FROM AUTHOR]

In this paper, we consider a special scenario where connected (V2V and V2I) vehicular technologies are used to alert motorists when they approach a hazardous zone, such as a low visibility area, and recommend proper speeds. We present the principles of the proposed safety driving system and compare the performance of V2V and V2I communications in terms of road safety effectiveness and network communication efficiency. This performance analysis is based on extensive computer simulation experiments by adapting the iTetris platform under various scenarios. We also explore, via simulations, whether CHAA systems, based on V2V and V2I communications can potentially contribute towards eco-driving by reducing Carbon Dioxide (CO 2) emissions. Our simulation results showed that our alerting system, based on V2I communication, yields better message reception rate and better safety efficiency compared to a V2V alert system. The results also show that the proposed CHAA system can contribute as well towards reduced CO 2 emissions by promoting speed harmonization. [ABSTRACT FROM AUTHOR]

Highlights • The automated Eco-CACC system could achieve fuel savings of 31% on average. • The manual Eco-CACC system could achieve fuel savings of 13% on average. • The Eco-CACC system also reduced the travel time by 8.5% approximately. • The Eco-CACC system reduced the percentage of time idling at intersection to zero. Abstract This paper field implements and conducts a unique controlled field experiment designed to evaluate an Eco-Cooperative Adaptive Cruise Control (Eco-CACC) system that computes fuel-efficient trajectories that are either recommended to drivers or implemented within a traditional ACC system. The controlled field experiment included three different scenarios: normal driving, driving with a speed advisory (manual Eco-CACC system), and automated Eco-CACC. The controlled field experiment was conducted for four red indication offset values (with two repetitions for each offset) randomly delivered to drivers traveling along an uphill and downhill approach on the Smart Road test facility. In total, 1536 trips were conducted by 32 different participants between the ages of 18 and 30 with an equal number of males and females. The collected data were compared with regard to fuel economy and travel time over a fixed distance starting upstream and ending downstream of the intersection (from 820 feet [250 m] upstream of the intersection to 590 feet [180 m] downstream for a total length of 1410 feet [430 m]). The results demonstrate that the proposed Eco-CACC system reduces fuel consumption levels significantly, especially when driving downhill. Specifically, the results indicate that the automated scenario could achieve fuel and travel time savings of 31% and 9% on average, respectively. These results also demonstrate that automatic longitudinal control produces significant benefits over human control (an approximately19% reduction in fuel consumption). [ABSTRACT FROM AUTHOR]

Highlights • There is a need for safety to be included in eco-driving algorithms. • Eco-driving algorithms' efficiency reduces with unexpected traffic change. • We proposed a novel model predictive controller with hard constraint on safety. • The proposed method adapts in real-time to traffic. • Our model improves both safety and fuel efficiency. Abstract Road transport plays an important role in the economy but generates huge costs in terms of road trauma and greenhouse gas emissions. The vast majority of previous research efforts have focused on driving behaviours that either minimise fuel consumption, or advise on safe driving behaviours. However, a fuel-optimal behaviour is often difficult to implement due to traffic conditions and may lead to unsafe behaviour without proper constraints. While a range of optimisation techniques are currently available, most lack the ability to both consider time and distance dependent constraints necessary for computationally efficient determination of optimal behaviour. In this paper, a methodology to find an ecological and safe (EcoSafe) driving behaviour by formulating and solving an optimal control problem for the minimum fuel driving behaviour while respecting key safety constraints was developed. We first propose a modified distance-based dynamic programming (MDDP) technique that can treat distance and time inequality constraints while optimising. Then we propose a model predictive control (MPC) structure based on MDDP and Pontryagin's maximum principle by considering inter vehicle time and time-to-collision as hard safety constraints. In contrast to existing approaches, the proposed MPC method does not require ad hoc tuning of constraint penalty weights in the cost function to ensure safe following distances. Simulation results of the proposed method show that the proposed algorithm produces lower vehicle fuel consumption compared to existing approaches when safety constraints are limiting. Thus, the results demonstrate the importance of considering safety constraints within an eco-driving system. [ABSTRACT FROM AUTHOR]

Highlights • Use of immersive simulation and a guidance system to teach eco-driving with practical experience. • Design of an interactive guidance system based on multimedia learning educational theories. • Drivers learn either with instructional videos, or videos combined with the guidance system. • Guidance system significantly contributes to higher eco-performances. • No impact in safety measures or driving time. Abstract 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. [ABSTRACT FROM AUTHOR]

Abstract Mitigating emissions of carbon dioxide and other greenhouse gases is critical if we are to meet the increasing threats posed by global warming. Previous studies have shown conclusively that a substantial part of all carbon dioxide emissions comes from transportation, and that Eco-Driving principles based upon strategic, tactical, and operational decisions have the potential to reduce these emissions. However, these well-established principles have been neglected within the construction industry despite the large number of transport-related activities that attend most construction projects. This paper therefore aims to increase awareness and understanding within the industry of the potential reductions of both carbon dioxide emissions and the costs of earthmoving activities that could be achieved through the use of Eco-Driving principles. A new concept labeled 'Eco-Hauling', which extends the Eco-Driving concept to earthmoving, is proposed. A case study of a road project has been conducted and used to demonstrate the new concept. Discrete-event simulation is used to support the data analysis as it enables modeling of the dynamic interactions between equipment and activities of multiple different construction scenarios. The presented findings show that a combination of decisions taken from the proposed Eco-Hauling concept can enable earthmoving contractors to substantially reduce carbon dioxide emissions and costs while maintaining productivity. This study has implications for the general advancement of Eco-Driving theory, as well as for project management as it sets out a viable approach for reducing greenhouse gas emissions in construction projects. Highlights • The Eco-Hauling concept is proposed by extending Eco-Driving to earthmoving. • The concept was demonstrated in a case study using discrete-event simulation. • A multi objective trade-off determines the optimal Eco-Hauling scenarios. • The reduction potential was shown at 6.94% in CO 2 emissions and 15.63% in costs. [ABSTRACT FROM AUTHOR]

Road transport consumes significant quantities of fossil fuel and accounts for a significant proportion of CO 2 and pollutant emissions worldwide. The driver is a major and often overlooked factor that determines vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions significantly. This paper reviews the major factors, research methods and implementation of eco-driving technology. The major factors of eco-driving are acceleration/deceleration, driving speed, route choice and idling. Eco-driving training programs and in-vehicle feedback devices are commonly used to implement eco-driving skills. After training or using in-vehicle devices, immediate and significant reductions in fuel consumption and CO 2 emissions have been observed with slightly increased travel time. However, the impacts of both methods attenuate over time due to the ingrained driving habits developed over the years. These findings imply the necessity of developing quantitative eco-driving patterns that could be integrated into vehicle hardware so as to generate more constant and uniform improvements, as well as developing more effective and lasting training programs and in-vehicle devices. Current eco-driving studies mainly focus on the fuel savings and CO 2 reduction of individual vehicles, but ignore the pollutant emissions and the impacts at network levels. Finally, the challenges and future research directions of eco-driving technology are elaborated. [ABSTRACT FROM AUTHOR]

Highlights • An eco-driving model for mixed traffic flow including automated vehicles and human driven vehicles. • Platoon based cooperation between automated vehicles and human driven vehicles. • Dynamic platoon splitting and merging method in terms of cooperation. • Cooperation behaviour significantly smooth the traffic flow and reduce the travel time. Abstract The advancements in communication and sensing technologies can be exploited to assist the drivers in making better decisions. In this paper, we consider the design of a real-time cooperative eco-driving strategy for a group of vehicles with mixed automated vehicles (AVs) and human-driven vehicles (HVs). The lead vehicles in the platoon can receive the signal phase and timing information via vehicle-to-infrastructure (V2I) communication and the traffic states of both the preceding vehicle and current platoon via vehicle-to-vehicle (V2V) communication. We propose a receding horizon model predictive control (MPC) method to minimise the fuel consumption for platoons and drive the platoons to pass the intersection on a green phase. The method is then extended to dynamic platoon splitting and merging rules for cooperation among AVs and HVs in response to the high variation in urban traffic flow. Extensive simulation tests are also conducted to demonstrate the performance of the model in various conditions in the mixed traffic flow and different penetration rates of AVs. Our model shows that the cooperation between AVs and HVs can further smooth out the trajectory of the latter and reduce the fuel consumption of the entire traffic system, especially for the low penetration of AVs. It is noteworthy that the proposed model does not compromise the traffic efficiency and the driving comfort while achieving the eco-driving strategy. [ABSTRACT FROM AUTHOR]

Highlights • We highlight the energy saving potential of connected and automated vehicles. • Anticipative/Collaborative driving of CAVs can contribute to 3–20% energy saving. • With higher penetration, there will be system wide influences by such “eco-CAV”s. • CAVs offer opportunities for cooperative driving further improving energy efficiency. Abstract Connected and automated vehicles (CAV) are marketed for their increased safety, driving comfort, and time saving potential. With much easier access to information, increased processing power, and precision control, they also offer unprecedented opportunities for energy efficient driving. This paper is an attempt to highlight the energy saving potential of connected and automated vehicles based on first principles of motion, optimal control theory, and a review of the vast but scattered eco-driving literature. We explain that connectivity to other vehicles and infrastructure allows better anticipation of upcoming events, such as hills, curves, slow traffic, state of traffic signals, and movement of neighboring vehicles. Automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and save energy. Opportunities for cooperative driving could further increase energy efficiency of a group of vehicles by allowing them to move in a coordinated manner. Energy efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles. [ABSTRACT FROM AUTHOR]

Highlights • The algorithm provides a constantly updated set of speed profiles. • It optimizes energy consumption, running time and risk of delay in arrival. • Manual driving uncertainty is taken into account using fuzzy modelling. • Energy savings can be achieved while the risk of delay is controlled. • The higher the risk of delay the higher the energy consumption. Abstract Eco-driving is an energy efficient traffic operation measure that may lead to important energy savings in high speed railway lines. When a delay arises in real time, it is necessary to recalculate an optimal driving that must be energy efficient and computationally efficient. In addition, it is important that the algorithm includes the existing uncertainty associated with the manual execution of the driving parameters and with the possible future traffic disturbances that could lead to new delays. This paper proposes a new algorithm to be executed in real time, which models the uncertainty in manual driving by means of fuzzy numbers. It is a multi-objective optimization algorithm that includes the classical objectives in literature, running time and energy consumption, and as well a newly defined objective, the risk of delay in arrival. The risk of delay in arrival measure is based on the evolution of the time margin of the train up to destination. The proposed approach is a dynamic algorithm designed to improve the computational time. The optimal Pareto front is continuously tracked during the train travel, and a new set of driving commands is selected and presented to the driver when a delay is detected. The algorithm evaluates the 3 objectives of each solution using a detailed simulator of high speed trains to ensure that solutions are realistic, accurate and applicable by the driver. The use of this algorithm provides energy savings and, in addition, it permits railway operators to balance energy consumption and risk of delays in arrival. This way, the energy performance of the system is improved without degrading the quality of the service. [ABSTRACT FROM AUTHOR]

As electric vehicles (EVs) have gained an increasing market penetration rate, the traffic on urban roads will tend to be a mix of traditional gasoline vehicles (GVs) and EVs. These two types of vehicles have different energy consumption characteristics, especially the high energy efficiency and energy recuperation system of EVs. When GVs and EVs form a platoon that is recognized as an energy-friendly traffic pattern, it is critical to holistically consider the energy consumption characteristics of all vehicles to maximize the energy efficiency benefit of platooning. To tackle this issue, this paper develops an optimal control model as a foundation to provide eco-driving suggestions to the mixed-traffic platoon. The proposed model leverages the promising connected vehicle technology assuming that the speed advisory system can obtain the information on the characteristics of all platoon vehicles. To enhance the model applicability, the study proposes two eco-driving advisory strategies based on the developed optimal control model. One strategy provides the lead vehicle an acceleration profile, while the other provides a set of targeted cruising speeds. The acceleration-based eco-driving advisory strategy is suitable for platoons with an automated leader, and the speed-based advisory strategy is more friendly for platoons with a human-operated leader. Results of numerical experiments demonstrate the significance when the eco-driving advisory system holistically considers energy consumption characteristics of platoon vehicles. [ABSTRACT FROM AUTHOR]

Abstract: Without questioning the fact that to achieve efficiency emitters should pay for the true costs of their actions (a core principle of economic policies such as pollution taxes), we find sufficient evidence in the literature to demonstrate that many other policy instruments can be used in combination with taxes and permits to ensure that the transport needs of the present generation can be met without compromising the ability of future generations to meet any needs of their own. The policies and policy aspects considered in this paper broadly fall into three categories: physical policies, soft policies, and knowledge policies. All three aim to bring about changes in consumers’ and firms’ behaviour, but in different ways. The first category includes policies with a physical infrastructure element: public transport, land use, walking and cycling, road construction, and freight transport. We also consider the particular challenges for mobility in developing countries, and how these may be addressed. Soft policies, on the other hand, are non-tangible aiming to bring about behavioural change by informing actors about the consequences of their transport choices, and potentially persuading them to change their behaviour. These measures include car sharing and car pooling, teleworking and teleshopping, eco-driving, as well as general information and advertising campaigns. Finally, knowledge policies emphasise the important role of investment in research and development for a sustainable model of mobility for the future. The main findings can be summarised as follows. Physical policies: An increase in the use of public transport, combined with a decrease in the use of private cars, can reduce traffic congestion and, more importantly, carbon dioxide (CO2) emissions, as public transport generally causes lower CO2 emissions per passenger kilometre than private cars. Public transport fares are subsidised in most places, which can be justified by economies of scale and by the fact that public transport can reduce total road transport externalities. London, Singapore, Portland and Curitiba are all examples of good practice at government level, having achieved reliable, frequent and integrated public transport. Policies to increase public transport use must be part of an integrated policy. Integrated policy refers to integration across different modes of transport, different government objectives (such as the economy, health and the environment), considering the needs of different social groups, and coordinating action between the relevant government institutions. There is evidence that a lack of coordination can jeopardise the achievement of policy objectives. A sustainable model for transport policy also requires integration with land-use policies. These may be somewhat limited within the bounds of existing cities, but as cities grow and new cities are built, urban planners must put more emphasis on land use for sustainable transport in order to reduce congestion and CO2 emissions. Sustainable land-use policy can direct urban development towards a form that allows public transport as well as walking and cycling to be at the core of urban mobility. Walking and cycling, which improve general health and produce no tailpipe emissions, constitute an excellent alternative to motorised transport on short-distance trips within towns and cities. The policies which can incentivise walking and cycling include crime reduction to make streets safer, well-maintained and clean pavements, attractive street furniture, safe crossings with shorter waiting times, dedicated cycle paths, showers in offices, and lower speed limits, to name but a few. Road construction and expansion used to be seen as one of the most promising ways to reduce traffic congestion. However, in the mid-1990s, the issue was reassessed and it was found that building and expanding roads, increased, rather than decreased, congestion, and ultimately induced higher levels of travel demand. The reason for this is that the extra capacity reduces the general cost of travelling and the less expensive the travel, the more it will be demanded. Regarding freight modal shift, road transport is much more polluting than rail per tonne-km of goods transported and therefore a shift towards greater use of rail in freight transport is desirable. Inadequate infrastructure is the main obstacle preventing this modal shift taking place. Developing countries face great mobility challenges: rural areas are often extremely poorly connected to transport infrastructure, such that, in contrast to the situation in developed countries, the benefits of road construction can strongly outweigh the total costs (including environmental ones). The main challenge, however, is to develop a solution to the problems arising from the combination of urbanisation and motorisation. Integration of transport and land-use policy will be key to rising to this challenge. Soft policies: Car sharing and car clubs can also potentially reduce CO2 emissions, although the aggregate reduction in congestion and emissions has not been measured with an adequate degree of precision in the literature. Teleworking and teleshopping can potentially reduce congestion and also CO2 emissions. However, the evidence for this reduction is rather mixed, as it is unclear whether these measures lead to overall reductions in road transport. Eco-driving campaigns aim to inform and educate drivers in order to induce them to drive in a fuel-efficient and thus environmentally friendly way. There seems to be some consensus in the literature that eco-driving could lead to reductions in CO2 emissions of around 10 per cent. Information and education policies have often been advocated as instruments which may affect behavioural change. We find in this paper that these types of measures are necessary, but not sufficient for behavioural change. Advertising and marketing may go a long way in changing peoples’ behaviour. In California, for example, finds the “Prius” effect: the Toyota Prius is preferred by consumers relative to other similarly green vehicles, probably due to extensive marketing and celebrity endorsements. Family life changes are also found to trigger changes in behaviour (). People whose lives are being changed by some important development (birth of a child, retirement, etc) tend to respond more to changes in the relative attractiveness of different transport modes. Advertising campaigns promoting a modal shift towards public transport, for instance, may thus be more successful if targeted at people in the process of important life transitions. Knowledge policies: Research and Development is crucial for developing sustainable and low-carbon transport for the future, and it is essential that governments provide incentives to undertake R&D, so that new low-carbon technologies in the transport sector can be demonstrated and applied at a large scale. Finally, we consider the issue of policy combination and integration. There is evidence that the combination and integration of policies can lead to positive side-effects and synergies. Policy integration is crucial in order to rise to the challenges we face in moving towards a sustainable mobility model. We conclude that classical economic policies may be successfully combined with a number of policy measures discussed in this paper in order to achieve sustainability in transport. [Copyright &y& Elsevier]

• Use of PRISMA approach for selection of relevant naturalistic driving studies. • Parameters for profiling drivers using naturalistic driving data. • Methods for development of driver behavior profiles. • Applications of driver behavior profiling. 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. [ABSTRACT FROM AUTHOR]

In this paper, a multi-level, comprehensive control technique for a heavy duty vehicle, capable of reducing fuel consumption while complying with engine constraints on both operating conditions and smoke emissions has been developed. The higher control layer, using road data, engine and vehicle parameters, computes the optimal speed and gear shifting profiles through an off-line, dual step dynamic programming algorithm. Since fuel consumption alone could be potentially decreased by driving at lower speeds and thus increasing travel time, this last aspect is also taken into account in the presented algorithm. The lower control layer, operating in real time, computes suitable powertrain control signals assuring the tracking of both reference speed and gear trajectories, while respecting the constraints imposed on engine operation. After the general description of the optimal control problem aimed at minimizing fuel consumption, an approach for its solution, implemented in the higher control layer, is presented. Afterwards, the lower control layer is described. In conclusion, the effectiveness of the developed control structure and the achievable improvement in terms of fuel consumption in comparison with a traditional fixed-point cruise controller are assessed through simulations in different driving scenarios. [ABSTRACT FROM AUTHOR]

Eco-driving is a traffic operation measure that may lead to important energy savings in high speed railway systems. Eco-driving optimization has been applied offline in the design of commercial services. However, the benefits of the efficient driving can also be applied on-line in the regulation stage to recover train delays or in general, to adapt the driving to the changing conditions in the line. In this paper the train regulation problem is stated as a dynamic multi-objective optimization model to take advantage in real time of accurate results provided by detailed train simulation. If the simulation model is realistic, the railway operator will be confident on the fulfillment of punctuality requirements. The aim of the optimization model is to find the Pareto front of the possible speed profiles and update it during the train travel. It continuously calculates a set of optimal speed profiles and, when necessary, one of them is used to substitute the nominal driving. The new speed profile is energy efficient under the changing conditions of the problem. The dynamic multi-objective optimization algorithms DNSGA-II and DMOPSO combined with a detailed simulation model are applied to solve this problem. The performance of the dynamic algorithms has been analyzed in a case study using real data from a Spanish high speed line. The results show that dynamic algorithms are faster tracking the Pareto front changes than their static versions. In addition, the chosen algorithms have been compared with the typical delay recovery strategy of drivers showing that DMOPSO provides 7.8% of energy savings. [ABSTRACT FROM AUTHOR]

In this paper, the calculation of eco-driving cycles for a Hybrid Electric Vehicle (HEV), using Dynamic Programming (DP), is investigated from the complexity-solving method 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 decoupling the optimization of the control variables into two steps: an eco-driving cycle is calculated assuming that the vehicle is propelled only by the engine. Then, knowing 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 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. [ABSTRACT FROM AUTHOR]

A simple, efficient, and realistic fuel consumption model is essential to support the development of effective eco-freight strategies, including eco-routing and eco-driving systems. The majority of the existing heavy duty truck (HDT) fuel consumption models, however, would recommend that drivers accelerate at full throttle or brake at full braking to minimize their fuel consumption levels, which is obviously not realistic. To overcome this shortcoming, the paper applies the Virginia Tech Comprehensive Power-based Fuel consumption Model (VT-CPFM) framework to develop a new model that is calibrated and validated using field data collected using a mobile emissions research laboratory (MERL). The results demonstrate that the model accurately predicts fuel consumption levels consistent with field observations and outperforms the comprehensive modal emissions model (CMEM) and the motor vehicle emissions simulator (MOVES) model. Using the model it is demonstrated that the optimum fuel economy cruise speed ranges between 32 and 52 km/h with steeper roads and heavier trucks resulting in lower optimum cruise speeds. The results also demonstrate that the model generates accurate CO 2 emission estimates that are consistent with field measurements. Finally, the model can be easily calibrated using data collected using non-engine instrumentation (e.g. Global Positioning System) and readily implemented in traffic simulation software, smartphone applications and eco-freight programs. [ABSTRACT FROM AUTHOR]

The advanced cruise control system has expanded the energy-saving potential of the hybrid electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only optimize the energy management strategy (EMS) or integrate eco-driving through a hierarchically optimized assumption that optimizes EMS and eco-driving separately. Such kinds of approaches may lead to sub-optimal results. To fill this gap, we design a multi-agent reinforcement learning (MARL) based optimal energy-saving strategy for HEV, achieving a cooperative control on the powertrain and car-following behaviors to minimize the energy consumption and keep a safe following distance simultaneously. Specifically, a plug-in HEV model is regarded as the research object in this paper. Firstly, the HEV energy management problem in the car-following scenario is decomposed into a multi-agent cooperative task into two subtasks, each of which can conduct interactive learning through cooperative optimization. Secondly, the energy-saving strategy is designed, called the independent soft actor–critic, which consists of a car-following agent and an energy management agent. Finally, the performance of velocity tracking and energy-saving are validated under different driving cycles. In comparison to the state-of-the-art hierarchical model predictive control (MPC) strategy, the proposed MARL method can reduce fuel consumption by 15.8% while ensuring safety and comfort. [ABSTRACT FROM AUTHOR]

Eco-driving is one of the many options to reduce fuel consumption and emissions from transit operations. However, it is not yet clear how effective eco-driving is for different transit service and fuel types. As policymakers consider implementing eco-driving, they also need comparisons of eco-driving against other fuel-conserving strategies, such as purchasing alternative fuel vehicles. Using a case study of transit operations in Atlanta, Georgia, United States, this paper evaluated eco-driving for two very different service types – local urban service and express service. The authors simulated the implementation of transit eco-driving strategies using an innovative, streamlined algorithm designed to minimize fuel consumption by limiting instantaneous vehicle specific power while maintaining average speed and conserving total distance. Fuel consumption and fuel-cycle emissions were compared across the monitored driving cycles and their modified eco-driving cycles. The savings from eco-driving were also compared to fuel and emissions reductions expected via the conversion of the transit fleets to compressed natural gas (CNG), another popular fuel conservation strategy. The results showed that eco-driving would be a potentially very cost-effective strategy for local and express bus transit operations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Eco-driving for connected Fuel Cell Hybrid Electric Vehicles (FCHEVs) is a coupled problem of speed planning and energy management. To reduce the computational burden, bi-level optimization decouples and hierarchically solves the upper-level and lower-level subproblems. This paper proposes a bi-level convex approach for eco-driving of a connected FCHEV proceeding through multiple signalized intersections. On the upper level, the non-linear traffic light constraints are transformed into time-varying linear state constraints and the cost function becomes quadratic after using the average speed. On the lower level, model convexification is carried out for the fuel cell system and battery. Then the upper-level speed planning and lower-level energy management are sequentially solved by the MOSEK solver and the Alternating Direction Method of Multipliers (ADMM) algorithm. The results show that the proposed bi-level convex approach greatly reduces the computational cost while maintaining high energy efficiency, with only 6.59% computational time and almost the same fuel economy compared to the bi-level Dynamic Programming (DP) method. • Bi-level convex optimization of eco-driving is proposed. • Upper-level speed planning is constructed as a convex quadratic programming problem. • Lower-level energy management is solved by the ADMM algorithm. • A comparison of the proposed method and benchmarking methods is conducted. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Whilst driving is inherently a safety–critical task, awareness of fuel-efficient driving techniques has gained popularity in both the public and commercial domains. Green driving, whether motivated by financial or environmental savings, has the potential to reduce the production of greenhouse gases by a significant amount. This paper focusses on the interaction between the driver and their vehicle – what type of eco-driving information is easy to use and learn whilst not compromising safety. A simulator study evaluated both visual and haptic eco-driving feedback systems in the context of hill driving. The ability of drivers to accurately follow the advice, as well as their propensity to prioritise it over safe driving was investigated. We found that any type of eco-driving advice improved performance and whilst continuous real-time visual feedback proved to be the most effective, this modality obviously reduces attention to the forward view and increases subjective workload. On the other hand, the haptic force system had little effect on reported workload, but was less effective that the visual system. A compromise may be a hybrid system that adapts to drivers’ performance on an on-going basis. [ABSTRACT FROM AUTHOR]

One of the main priorities for metro line operators is the reduction of energy consumption, due to the environmental impact and economic cost. The new moving block signalling system CBTC (Communication Based Train Control) is being installed in order to increase the transport capacity of new metro lines, and to upgrade lines equipped with the former fixed block signalling systems. In addition, this new technology could be used to improve energy efficiency in the traffic operation, due to its capability to update the ATO (Automatic Train Operation) driving commands not only at stations but also along the journey. In this paper a new optimization algorithm is proposed to design ATO CBTC speed profiles to minimize energy consumption, generating the Pareto optimal curve. The algorithm is a multi-objective NSGA-II-F based on simulation of the train motion under uncertainty. The main source of uncertainty in the consumption calculation is the mass of the train, which is dependent on the passenger load, and it is modeled as a fuzzy number. In addition, a new method is proposed to fill running time gaps with dominated points, providing a pseudo-Pareto curve. The NSGA-II-F algorithm has been applied to design ATO CBTC optimal speed profiles in a case study of Metro de Madrid, providing additional energy savings up to 7.3% compared to former fixed block signalling systems and a well time-distributed pseudo-Pareto curve for the running times required by the traffic regulation system. [ABSTRACT FROM AUTHOR]

This paper investigates the fuel efficiency of commercial hybrid electric vehicles (HEVs) and compares their performance with respect to standard gasoline vehicles in the context of cold Canadian urban environments. The effect of different factors on fuel efficiency is studied including road driving conditions (link type, city size), temperature, speed, cold-starts and eco-driving training. For this study, fuel consumption data at the link level in real-world conditions was used from a sample of 74 instrumented vehicles. From the study fleet, 21 vehicles were HEVs. Among other results, the beneficial fuel efficiency merits of hybrid vehicles were demonstrated with respect to gasoline cars, in particular at low speeds and in urban (city) environments. After controlling for other factors, sedan HEVs were 28% more efficient than sedan gasoline vehicles. However, the low temperatures (below 0 °C) observed regularly during winter season in the study cities were identified as a detrimental factor to fuel economy. In winter, the fuel efficiency of HEVs decrease about 20% with respect to summer. Other factors such as eco-driving training, city size, cold start and vehicle type were also found to be statistically significant. [ABSTRACT FROM AUTHOR]

Eco-Driving has received significant attention in literature and among policy makers for its claimed benefits in reducing CO2 emissions and fuel consumption. Many investigations of Eco-Driving policy have reported potential reductions in fuel consumption and CO2 emissions ranging from 5% to 40% across various jurisdictions and initiatives. This paper comprises a review and assessment of Eco-Driving policy and its claimed benefits. The possible negative impacts of Eco-Driving, often neglected in previous research, are also highlighted. These include policy limitations which may result in increases in accident risk, and CO2 emissions at traffic network level. In addition, the limitations of certain Eco-Driving technology are also highlighted. The results of this review and assessment reveal that Eco-Driving Policy has the potential to reduce CO2 emission and fuel consumption in certain circumstances, but in congested city centre traffic many conflicting views exist in the literature, resulting in some doubt over the effectiveness of the policy in such circumstances. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers an energy management problem for an electric vehicle compliant with online requirements for ‘eco-driving’ application. Eco-driving techniques are discussed and formulated as an optimal control problem that consists of the minimization of the vehicle consumption over a time and distance horizon. Then, a closed-form solution of the optimal trajectories is presented. Numerical and experimental results assess the validity of the proposed eco-driving system. [Copyright &y& Elsevier]

With inevitable random disturbance in traffic scenarios, electric vehicles (EVs) may face the driving safety issue, while, if operated over cautiously, the frequent speed variation deteriorates the energy economy of the EV. This conflict provokes a desire to understand the energy consumption behavior of EVs in a stochastic driving environment and reveal the corresponding energy optimal control policy. For addressing these issues, this paper develops a chance constraint stochastic model predictive control (CC-MPC) method for simultaneously optimizing the speed planning and the powertrain energy management strategy, which cooperates with a bi-level prediction model for estimating the future driving environment. Validated by massive car-following cases in the urban traffic flow, the proposed CC-MPC increases the success rate (no constraint violation) to 87%, while the deterministic MPC methods only achieve a success rate of 27%. Although the proposed CC-MPC method generates a sensitive driving style to variations of the driving environment, the conflict between energy economy and driving safety has been successfully removed. Validations suggest that when safety probability is 0.9, the success rate is 84% with only 0.8% deterioration in energy economy compared with the energy consumption resulting from the MPC with perfect knowledge of the leading vehicle speed. ● Individual speed prediction is corrected by the traffic state ● Driving safety in random environment is significantly improved by the proposed CC-MPC ● The CC-MPC removes the conflict between driving safety and the energy economy ● The novel solver improves optimality by 12% and computation efficiency by 25 times. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time dynamic predictive cruise control (PCC) system to minimize the energy consumption for electric vehicles (EVs) under integrated traffic situations with synthetic driving scenarios, considering both constraints from the preceding vehicle and the influence of traffic signal lights. The proposed PCC system is working based on the bi -level model predictive control (MPC) algorithm. The Signal Phase and Timing (SPaT)-oriented MPC calculates a desired acceleration command as the optimal control signal at each sampling step based on the forthcoming SPaT information with the purpose of passing the nearest signalized intersection during the green light interval without stop. The car-following-oriented MPC executes preceding vehicle tracking task through maintaining a safe inter-distance using a customized variable time headway (VTH) strategy. The instantaneous energy consumption for EV in different traffic scenarios was quantified by a data-driven model. The developed system was validated through comparison with IDM and human driver's maneuver in both suburban and urban areas road in the city of Fukuoka, Japan, during off-peak and peak hours, using the real traffic system and SPaT data. To further evaluate the performance of the proposed PCC system in high speed driving situation, another case study with transitions from highway to urban road was conducted. The simulative results showed that the proposed PCC system can realize the energy-saving rates by 8.5%–15.6%. And it was working well and robustly under high speed driving situation. • A real-time PCC system was developed to minimize the energy consumption of EVs. • Two scenarios of car-following and signal anticipation were evaluated. • An ANN model was developed to predict the energy economy performance of the EV. • The proposed PCC can realize 8.5%–15.6% of the energy saving in the urban areas. [ABSTRACT FROM AUTHOR]

This paper quantifies the system-wide impacts of implementing a dynamic eco-routing system, considering various levels of market penetration and levels of congestion in downtown Cleveland and Columbus, Ohio, USA. The study concludes that eco-routing systems can reduce network-wide fuel consumption and emission levels in most cases; the fuel savings over the networks range between 3.3% and 9.3% when compared to typical travel time minimization routing strategies. We demonstrate that the fuel savings achieved through eco-routing systems are sensitive to the network configuration and level of market penetration of the eco-routing system. The results also demonstrate that an eco-routing system typically reduces vehicle travel distance but not necessarily travel time. We also demonstrate that the configuration of the transportation network is a significant factor in defining the benefits of eco-routing systems. Specifically, eco-routing systems appear to produce larger fuel savings on grid networks compared to freeway corridor networks. The study also demonstrates that different vehicle types produce similar trends with regard to eco-routing strategies. Finally, the system-wide benefits of eco-routing generally increase with an increase in the level of the market penetration of the system. [ABSTRACT FROM AUTHOR]

Abstract: This paper compares and assesses fuel consumption models, cost functions, and solution methods, as they all have an influence on the resulting profile and associated fuel savings of an eco-cruise control system for passenger vehicles. An eco-cruise control system uses road topographical data obtained from a high-resolution digital map to control the vehicle velocity to optimize its fuel consumption. The optimal velocity profile is the result of an optimal control problem. [Copyright &y& Elsevier]

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. 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 10months 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. [Copyright &y& Elsevier]

• A microscopic power-based energy consumption model of electric buses is proposed. • The model leverages on the analysis of e-Bus data from two months of operations. • The simulation of auxiliary systems energy consumption is integrated in the model. • The model was calibrated against field data, yielding an average error of 2.5%. • Model validation was conducted at both the microscopic and macroscopic levels. In this paper a microscopic energy consumption model of electric buses (e-Buses) is developed, leveraging on a vast data collection campaign of e-Bus daily operations. The model is composed of two modules, which estimate the instantaneous power required for traction and the average power required by the auxiliary systems. The model simulates the instantaneous energy consumption as a function of line and vehicle characteristics, and operating conditions including ambient temperature, bus load and encountered traffic. The model was calibrated against trajectory data from 435 trips, showing an error in reproducing the instantaneous battery state-of-charge of 5.5% in 95% of experiments (2.5% on average). A two-level model validation was performed against data from 110 trips. At a microscopic level, results proved that the calibrated model is robust against variability of line operating conditions. At a macroscopic level, the model was validated against the macroscopic speed-consumption function, showing an error of 6%. [ABSTRACT FROM AUTHOR]

With the development of connected and automated vehicles, eco-driving control is reckoned to generate unprecedented potential on energy-saving in electrified powertrain. In this paper, a data-driven based eco-driving control strategy with efficient computation capacity is proposed for plug-in hybrid electric vehicles to achieve approximate optimal energy economy. An efficient hierarchical optimal control scheme is designed to mitigate the massive computational cost during velocity optimization and powertrain control. A data-driven optimal energy consumption cost model and an optimal battery current model are respectively constructed via two neural networks and served as the critic model and the system model during velocity optimization. Furthermore, the neural network-based dynamic programming is exploited to optimize the vehicle velocity by merging the data-driven models and Bellman optimality principle. The simulation results demonstrate that the proposed method can remarkably improve fuel economy by up to 16.7% in complicated driving conditions, compared with conventional sequential optimization methods. Furthermore, the data-driven control scheme can drastically improve the computational efficiency with slight sacrifice on fuel economy, compared with the optimum benchmark. • A data-driven based eco-driving control method is proposed. • Optimal energy consumption and battery current are modeled by two neural networks. • A neural network-based dynamic programming scheme is exploited. • The devised method obviously promotes fuel economy with high computation efficiency. [ABSTRACT FROM AUTHOR]

Eco-Driving has great potential in reducing the fuel consumption of road vehicles, especially under the connected and automated vehicles (CAVs) environment. Traditional model-based Eco-Driving methods usually require sophisticated models and cannot deal with complex driving scenarios. This paper proposes a hybrid reinforcement learning (RL) based Eco-Driving algorithm considering both the longitudinal acceleration/deceleration and the lateral lane-changing operations. A deep deterministic policy gradient (DDPG) algorithm is designed to learn the continuous longitudinal acceleration/deceleration to reduce the fuel consumption as well as to maintain acceptable travel time. Collecting the critic's value of each single lane from DDPG and integrating the information of adjacent lanes, a deep Q-learning algorithm is developed to make the discrete lane-changing decision. Together, a hybrid deep Q-learning and policy gradient (HDQPG) method is developed for vehicles driving along multi-lane urban signalized corridors. The method can enable the controlled vehicle to learn well-established longitudinal fuel-saving strategies, and to perform appropriate lane-changing operations at proper times to avoid congested lanes. Numerical experiments show that HDQPG can reduce fuel consumption by up to 46% with marginal or no increase of travel times. • Continuous state space for longitudinal accelerations and discrete state space for lateral lane changing. • A hybrid deep Q-learning and policy gradient (HDQPG) method is developed for Eco-Driving. • HDQPG can enable vehicles to learn well-established Eco-Driving strategies. • HDQPG can save fuel consumption by 46% compared with the baseline model. • HDQPG outperforms existing RL-based Eco-Driving methods. [ABSTRACT FROM AUTHOR]