Article

Eco-driving: An overlooked climate change initiative

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Abstract

The actions individuals can take to mitigate climate change are, in the aggregate, significant. Mobilizing individuals to respond personally to climate change, therefore, must be a complementary approach to a nation's climate change strategy. One action item overlooked in the United States has been changing driver behavior or style such that eco-driving becomes the norm rather than the exception. Evidence to date indicates that eco-driving can reduce fuel consumption by 10%, on average and over time, thereby reducing CO2 emissions from driving by an equivalent percentage. A sophisticated, multi-dimensional campaign, going well beyond what has been attempted thus far, will be required to achieve such savings on a large scale, however, involving education (especially involving the use of feedback devices), regulation, fiscal incentives, and social norm reinforcement.

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... Eco-driving is a driving style typified by behaviours such as gentle acceleration, gentle braking, anticipating traffic flow and driving below the speed limit (Barkenbus, 2010). Eco-driving has been extensively documented to reduce both fuel use and GHG emissions (Barkenbus, 2010). ...
... Eco-driving is a driving style typified by behaviours such as gentle acceleration, gentle braking, anticipating traffic flow and driving below the speed limit (Barkenbus, 2010). Eco-driving has been extensively documented to reduce both fuel use and GHG emissions (Barkenbus, 2010). Stillwater & Kurani (2013) estimate that emissions could be reduced by 5%-20% should the typical driver engage with eco-diving practices. ...
... Eco-driving can be largely characterised by the operational decisions which drivers make during their journey (Barkenbus, 2010). Driving behaviours commonly associated with this style include driving below the speed limit, early gear changes, gentle acceleration and minimization of braking. ...
Article
Despite both the environmental and financial benefits of eco-driving being well known, the psychological impact of engaging in eco-driving behaviours has received less attention within the literature. It was anticipated that being asked to engage in eco-driving behaviours not only has an impact on vehicle fuel usage, but also on the driver, both in terms of their overall mood and willingness to re-engage with the task at a later time. Results from a simulated driving study suggest that although eco-driving was beneficial in reducing fuel consumption, being asked to eco-drive had a negative effect on overall journey time and mood. Engaging in eco-driving did however have a positive effect on self-esteem, suggesting potential longer term psychological benefits of adopting this behaviour.
... Si bien todas estas políticas están bien vistas por los residentes de Bucarest, quienes están conscientes de los efectos negativos del transporte y preocupados por la contaminación que provoca, Ioncicai, Petrescu y Ioncica (2012) reportan un diferencial entre sus actitudes y conducta intencional ya que por ejemplo cuando adquieren un automóvil se preocupan más por el confort, el diseño y la potencia que por sus atributos ecológicos. Es por esto que, y en línea con el trabajo de Barkenbus (2010), los principios de mercadotecnia social resultan relevantes para no sólo elaborar una campaña informativa que destaque los beneficios de la conducción eficiente, sino para diseñar una intervención que integre políticas públicas, programas educativos y de capacitación, esquemas de retro-alimentación y normas sociales para atraer y convencer a los conductores de automóviles para que adopten mejores hábitos al conducir. ...
... Entre las más importantes están: regulaciones legales insuficientes que garanticen una capacitación de alta calidad en ecodriving; la poca concientización y motivación de los individuos; los estilos de manejo prevalentes en cada país (estas tres barreras fueron citadas por 9 de 13 países); aspectos financieros que limitan la educación y difusión del ecodriving; falta de iniciativas gubernamentales y de conocimiento sobre el tema entre entrenadores, instructores y examinadores de métodos de manejo. Autores como Barkenbus (2010) indican que las barreras culturales, técnicas y educativas son fuertes inhibidores para la adopción del ecodriving especialmente en países donde el automóvil es más que un medio transporte y donde la infraestructura urbana favorece el manejo agresivo. ...
... El tema de ecodriving ha empezado a ser tema de investigación; en ingeniería los trabajos se han enfocado hacia la identificación de patrones de manejo y cuantificación de los ahorros en combustible y la reducción de emisiones (Baric, 2013), mientras que desde el enfoque de administración el tema se ha abordado desde la perspectiva de teorías conductuales (Barkenbus, 2010) y de cambio organizacional (Dzenisiuk, 2013). Las teorías sobre la conducta reconocen que a menos que un individuo perciba que sus acciones conducen a resultados efectivos y no representan un esfuerzo exagerado, no modificará o adoptará una nueva conducta. ...
Book
El libro recopila ocho trabajos de investigación realizados en México que describen cómo el concepto de mercadotecnia verde se asocia con la sustentabilidad. Los capítulos están organizados en dos secciones, la primera vinculada con las actividades pro-ambiente de las empresas y la segunda con las conductas del consumidor "verde". El libro está dirigido a estudiantes de posgrado, profesores e investigación del área de mercadotecnia y también a profesionales interesados en implementar el concepto de marketing verde.
... Generally, EV drivers' eco-driving strategy can help them minimize the EV's energy consumption up to 25% (Helmbrecht et al., 2013). According to some research, operational decisions can be defined as eco-driving behavior (e.g., Barkenbus, 2010), while other studies used the term to refer to both strategic and tactical decisions (e.g., Stromberg et al., 2015). In this study, we will adopt Barkenbus' (2010) eco-driving definition and mainly concentrated on the operational level of drivers' eco-driving behavior. ...
... According to some research, operational decisions can be defined as eco-driving behavior (e.g., Barkenbus, 2010), while other studies used the term to refer to both strategic and tactical decisions (e.g., Stromberg et al., 2015). In this study, we will adopt Barkenbus' (2010) eco-driving definition and mainly concentrated on the operational level of drivers' eco-driving behavior. We also involved the strategic decisions by investigating the eco-driving related questions in the context of both EVs and ICEVs. ...
... For the specific definition of eco-driving behavior, Barkenbus (2010) defined it as a collection of driving behaviors or styles that characterize fuel-efficient driving, which could help reduce energy consumption by between 5% and 30% (Alam and McNabola, 2014). In general, eco-driving behavior included a choice of driving speed, distance to the preceding vehicle, frequency of overtaking other vehicles, and a proclivity for traffic violations (Elander et al., 1993). ...
Article
Eco-driving is one strategy for reducing transportation sector fuel usage and greenhouse gas emissions. With the advancement of connected-vehicle technology, the dynamic eco-driving concept can utilize real-time vehicle-specific information to optimize vehicle speed, thereby further reducing fuel consumption and emissions. The objective of this research was to determine the elements that influence drivers' intentions to practice eco-driving and their acceptance of eco-driving technology. A theoretical model of technology acceptance for both internal combustion engine vehicle (ICEV) and electric vehicle (EV) drivers was built using a mix of the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and Goal Framing. Drivers’ acceptance of eco-driving system was hypothesized to be based on their intention to perform eco-driving. The model's validity was verified using a structural equation modeling analysis of data from a survey with 340 replies from ICEV drivers and 315 responses from EV drivers. The findings corroborated the original hypotheses in TAM and TPB, and drivers' intention to practice eco-driving had an indirect effect on their intention to utilize the system via the construct of perceived ease of use. In comparison to ICEV drivers, EV drivers possessed a greater understanding of eco-driving. The four goal framing structures each played a different role in the ICEV and EV models. In the ICEV model, the altruistic goal contributed positively to the social norm construct. By contrast, the social norm was positively influenced by the biospheric and the egoistic goals, and negatively influenced by the hedonic goal in the EV model. This study's framework and results provide theoretical and practical guidelines for researchers, manufacturers, and policy-makers to understand drivers' motivation to perform eco-driving and increase drivers' acceptance of the eco-driving system.
... According to Renub Research (2021), the global automotive market is expected to accelerate at a compound annual growth rate of 3.71% from 85.32 million in 2020 to 122.83 million units by 2030. With the rapid growth in the number of road vehicles worldwide, the transportation sector is confronted with the arising challenges of greenhouse gas (GHG) emissions and traffic congestion (Barkenbus, 2010;Sullman et al., 2015). The legislative bodies worldwide have committed to address the alarming climate change challenge and set stringent regulatory standards to reduce the global CO2 emissions from road vehicles (Barkenbus, 2010). ...
... With the rapid growth in the number of road vehicles worldwide, the transportation sector is confronted with the arising challenges of greenhouse gas (GHG) emissions and traffic congestion (Barkenbus, 2010;Sullman et al., 2015). The legislative bodies worldwide have committed to address the alarming climate change challenge and set stringent regulatory standards to reduce the global CO2 emissions from road vehicles (Barkenbus, 2010). Furthermore, the transportation industry is transitioning towards sustainable future mobility solutions, thus resulting in a paradigm shift towards electrified powertrains. ...
... Furthermore, automated energy-optimal longitudinal control in vehicles is studied previously in several works (Sajadi-Alamdari et al., 2019), including ours (Chada et al., 2020(Chada et al., , 2021b. To enhance the energy efficiency in vehicles in a cost-effective manner, an area of research that has received significant attention over the years in both academia and automotive industry, is eco-driving (Barkenbus, 2010). The term eco-driving is referred to as an interim policy to improve the driver's driving style in an energy-efficient manner. ...
Preprint
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In this work, a predictive eco-driving assistance system (pEDAS) with the goal to assist drivers in improving their driving style and thereby reducing the energy consumption in battery electric vehicles while enhancing the driving safety and comfort is introduced and evaluated. pEDAS in this work is equipped with two model predictive controllers (MPCs), namely reference-tracking MPC and car-following MPC, that use the information from onboard sensors, signal phase and timing (SPaT) messages from traffic light infrastructure, and geographical information of the driving route to compute an energy-optimal driving speed. An optimal speed suggestion and informative advice are indicated to the driver using a visual feedback. Moreover, the warning alerts during unsafe car-following situations are provided through an auditory feedback. pEDAS provides continuous feedback and encourages the drivers to perform energy-efficient car-following while tracking a preceding vehicle, travel at safe speeds at turns and curved roads, drive at energy-optimal speed determined using dynamic programming in freeway scenarios, and travel with a green-wave optimal speed to cross the signalized intersections at a green phase whenever possible. Furthermore, to evaluate the efficacy of the proposed eco-driving assistance system, user studies were conducted with 41 participants on a dynamic driving simulator. The objective analysis revealed that the drivers achieved mean energy savings up to 10%, reduced the speed limit violations, and avoided unnecessary stops at signalized intersections by using pEDAS. Finally, the user acceptance of the proposed pEDAS was evaluated using the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). The results showed an overall positive attitude of users and that the perceived usefulness and perceived behavioral control were found to be the significant factors in influencing the behavioral intention to use pEDAS.
... However, the definition of eco-driving is inconsistently defined in academic and popular sources. Alam & McNabola (2014) and Barkenbus (2010) discussed eco-driving within the function of reducing fuel consumption and CO2 emissions from transportation. Sivak & Schoettle (2012) defined ecodriving as a decision that a driver could make to influence the fuel economy of light-duty vehicles, ranging from vehicle purchase to post-purchase decisions. ...
... With regard to eco-driving behavior, prior research has concluded that encouraging eco-driving behavior requires substantial investment in a multi-faceted approach involving public education, driver feedback, regulatory actions, economic and policy incentives, and social norm reinforcement in order to achieve a reduction on fuel consumption and CO2 emissions by 10% over time -an annual saving of 33 million metric tons of CO2 and a cost saving of $7.5-15 billion in U.S. (Barkenbus, 2010). Notably, Stern (2000) emphasized that the external or contextual factors may have different meanings for people with different attitudes or beliefs, and expected an interaction between contextual factors and attitudinal factors in the framework. ...
... 1) Accelerate and decelerate smoothly: Many studies have concluded that the driving maneuver of gentle acceleration and deceleration has become one of the most important indicators of eco-driving behavior (Barkenbus, 2010;Sivak & Schoettle, 2012). It has 10%-40% fuel economy benefits and $0.22-$0.87/gallon ...
Thesis
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The success of the change towards frequently undertaking eco-driving behavior is highly dependent on the individual drivers and appropriate in-vehicle feedback systems that drivers respond to. This work uses the data coming from 822 individuals who participated an online survey over a two-month period using 14 graphics of different types of in-vehicle eco-driving feedback interfaces and finds that researcher-identified eco-drivers are those ICEV drivers with strong environmental beliefs, and self-identified eco-drivers are those who have higher level of education, lower income, and strong environmental beliefs. This variation in researcher-identified and self-identified eco-drivers by demographics, vehicle characteristics, and motivational factors suggests an intention-behavior gap that self-identified eco-drivers are not those who are actually engaging in frequent eco-driving behaviors. Beyond the identification of eco-drivers, the use of in-vehicle eco-driving feedback itself plays an important role in encouraging eco-driving behavior. This work finds that eco-drivers are more likely than non-eco-drivers to be influenced by the use of eight different types of feedback. In the analyses of a causal chain leading from intentions to behavior, eco-driving intentions account for up to 22.4% of the variance in models explaining self-reported eco-driving behavior. The use of Biophilic Rewards and Eco-Driving Coach feedback types were found to play the most important role in motivating drivers to eco-drive. Ultimately, this research develops an integrated theoretical framework to better understand the role of the use of in-vehicle eco-driving feedback in encouraging eco-driving behavior. It posits that feedback design attributes are relevant in whether and how drivers perceive information and the impact on driver response towards frequent eco-driving behavior. It also provides direction for further research to expand on themes surrounding social norms, and a framework to undertake experimental fieldwork in the future.
... Especially for battery electric vehicles (BEVs) with long recharge times and lower ranges than internal combustion engines saving energy during operation plays a crucial role. Thus, the development of energy-efficient driving strategies to reduce fuel consumption have gained significant industrial interest [3]. As a vehicle can only be optimized during the development process and usually not influenced during operations, the development of eco-driving strategies to optimize the operation of vehicles has received great attention. ...
... It can be processed by a human driver using learned patterns to achieve low energy consumption; for example, through smooth acceleration or deceleration and maintaining a constant speed. Furthermore, eco-driving is capable of reducing traffic fatalities and can reduce the risk of traffic accidents [3]. However, without exact knowledge of the energy-optimal operating points of the vehicle and the upcoming driving situation, a human driver can only reach a sub-optimal driving strategy. ...
Article
Full-text available
Global warming forces the automotive industry to reduce real driving emissions and thus, its CO2 footprint. Besides maximizing the individual efficiency of powertrain components, there is also energy-saving potential in the choice of driving strategy. Many research works have noted the potential of model predictive control (MPC) methods to reduce energy consumption. However, this results in a complex control system with many parameters that affect the energy efficiency. Thus, an important question remains: how do these partially uncertain (system or controller) parameters influence the energy efficiency? In this article, a global variance-based sensitivity analysis method is used to answer this question. Therefore, a detailed powertrain model controlled by a longitudinal nonlinear MPC (NMPC) is developed and parameterized. Afterwards, a qualitative Morris screening is performed on this model, in order to reduce the parameter set. Subsequently, the remaining parameters are quantified using Generalized Sobol Indices, in order to take the time dependence of physical processes into account. This analysis reveals that the variations in vehicle mass, battery temperature, rolling resistance and auxiliary consumers have the greatest influence on the energy consumption. In contrast, the parameters of the NMPC only account for a maximum of 5% of the output variance.
... Specifically, many factors can affect fuel consumption and emissions, such as the engine type, power-train systems or road characteristics as shown for instance in [2], [3] among many other works. Yet, beside these factors, as shown in [4] or also by more recent studies such as [5], a substantial, and often overlooked factor is the driver himself, and fixed procedures for certification do not help in alleviating this influence. Therefore, there have been non control based approaches, referred to as ecodriving, where training and guidelines for drivers are applied, which are usually based on heuristic [5], [6], in order to improve the driving skills and sensitize people to lead them to more efficient driving. ...
... Yet, beside these factors, as shown in [4] or also by more recent studies such as [5], a substantial, and often overlooked factor is the driver himself, and fixed procedures for certification do not help in alleviating this influence. Therefore, there have been non control based approaches, referred to as ecodriving, where training and guidelines for drivers are applied, which are usually based on heuristic [5], [6], in order to improve the driving skills and sensitize people to lead them to more efficient driving. However, some studies, e.g., [7], investigated the impact of these ecodriving guidelines on nitrogen oxides (NO x ) emissions and found no significant improvement, therefore motivating further studies on emission aware ecodriving. ...
Article
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Advanced automation and information systems are shaping the future of transportation by enabling advanced approaches, such as ecodriving control strategies, which improve safety and energy efficiency of personal transport. This paper contributes to the field of ecodriving control by developing a novel approach capable to deal with curvy roads, combining road grade and curvature look ahead to keep the driving comfortable, while also considering pollutant emissions. The formulation and solution of emission aware ecodriving as an optimal control problem is presented and, more specifically, a novel switching Nonlinear Model Predictive Control (NMPC) is proposed. Its feasibility and potential are shown by means of simulation case studies based on real world test cases, a validated vehicle model, and measured road topology. Hence, it is shown that emission aware ecodriving is a viable option and that developers of future ecodriving systems should expand their focus from solely energy efficiency towards additional targets.
... Avail-able methods include powertrain control [10], regenerative braking control [11], and eco-driving control [12]. Among them, the ecodriving control that targets an energy-efficient speed profile is recognized as one of the most effective methods [13,14] to improve vehicle energy efficiency and traffic throughput [15,16], and therefore have great potential for commercialization in the near future [17]. ...
... To predict the acceleration of each vehicle, we employ the IDM, which is a well-accepted model for single-lane traffic flow [50]. From the IDM, the acceleration of the jth vehicle follows (15) with where and are respectively the maximum deceleration and acceleration of each vehicle for comfort purposes. is the desired velocity, which equals the predefined maximum allowed speed. ...
Article
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This paper takes into consideration of vehicle queues at the intersection and proposes an energy-efficient driving strategy to improve vehicle energy efficiency and overall traffic throughput in an urban traffic environment. The proposed strategy is applicable for both electric vehicle and internal combustion engine vehicle, and the control framework is formed by three sections, a vehicle queue discharge predictor, a spatial-domain optimal control strategy for energy consumption minimization, and a speed tracker with consideration of collision avoidance constraints. The former is based on the intelligent driver model, which predicts an accurate vehicle queue discharge time. Then the iterative dynamic programming is utilized to find the optimal solutions with fast computational speed. Finally, the optimal speed profile is followed by a Proportion-Integration controller while keeping a safe inter-vehicular distance. A Monte-Carlo simulation is designed to evaluate the energy efficiency of the proposed strategy in the stochastic traffic environment. Compared to the regular eco-approach and departure and constant speed strategies that lack awareness of the queue, significant energy saving can be achieved of the proposed strategy. In addition, three typical cases are selected to study the energy efficiency when the proposed strategy is applied to internal combustion engine and electric vehicles, respectively. The results show the energy efficiency of electric vehicles is less sensitive to the queuing effect at the intersection because of regenerative braking and the overall higher efficiency of the electric motor in contrast to the internal combustion engine, especially in stop-and-go scenarios.
... Thus, extant survey evidence casts doubt on a gender effect on climate policy support and mitigation behavior, but this result needs further exploration, as gender is here usually included only as a sociodemographic control. This dearth of knowledge on the relationship between gender and climate policy support and behavior is troublesome; after all, political action is likely crucial to meet ambitious climate targets (Ergas et al., 2021;IPCC, 2021), and everyday mitigation behavior may also play an important part in reaching such goals (Vandenbergh & Steinemann, 2007;Barkenbus, 2010;European Commission, 2011). ...
Article
Full-text available
It is well-known that men and women differ in their views regarding the severity of climate change, but do they also differ in their support for climate policy and in undertaking climate action in their everyday lives? Previous survey evidence on these questions is inconclusive, but we can answer them using unique survey data from the Swedish Environmental Protection Agency (SEPA). Regression analysis confirms that Swedish women believe more strongly than men that climate change will affect Sweden. Furthermore, women engage in more climate-mitigating behavior than men, even conditional on climate beliefs. The association between gender and climate policy support is less robust, and disappears altogether when climate beliefs are controlled for, demonstrating that climate beliefs are the main mechanism explaining the relationship between gender and policy support.
... To enhance the energy-efficiency in both on-road and offroad vehicles, an area of research that has received significant attention in both the automotive industry and the academia over the years, is eco-driving [1]. Previous studies in this research area focussed primarily on coaching drivers on the eco-driving techniques to promote energy-efficient driving style behaviors [2]. ...
... In case of driving in a hilly terrain, the optimal speed has a varying behaviour, where the vehicle typically decelerates when climbing uphill, and accelerates when rolling downhill. This reduces non-recuperable energy waste at the braking pads, compared to driving with a constant speed [6]. To obtain an eco-driving velocity profile over complex road topographies, model-based optimal control strategies are employed to optimally coordinate energy use, see e.g., [7]- [10]. ...
Preprint
Full-text available
This paper addresses optimal battery thermal management (BTM), charging, and eco-driving of a battery electric vehicle (BEV) with the goal of improving its grid-to-meter energy efficiency. Thus, an optimisation problem is formulated, aiming at finding the optimal trade-off between trip time and charging cost. The formulated problem is then transformed into a hybrid dynamical system, where the dynamics in driving and charging modes are modeled with different functions and with different state and control vectors. Moreover, to improve computational efficiency, we propose modelling the driving dynamics in a spatial domain, where decisions are made along the traveled distance. Charging dynamics are modeled in a temporal domain, where decisions are made along a normalized charging time. The actual charging time is modeled as a scalar variable that is optimized simultaneously with the optimal state and control trajectories, for both charging and driving modes. The performance of the proposed algorithm is assessed over a road with a hilly terrain, where two charging possibilities are considered along the driving route. According to the results, trip time including driving and charging times, is reduced by 44 %, compared to a case without battery active heating/cooling.
... Households' existing vehicle stock need not be the only factor affecting the swiftness of their response. Other factors can include the distance between home and workplace, the availability of other transportation modes, or even the driving style of household members -in particular, it has been estimated that so-called "eco-driving", an assortment of driving best practices, can reduce gasoline consumption on any given car by up to 10% (Barkenbus, 2010). ...
... Tabel 1. Indikator Eco-driving (Barkenbus, 2010;ecodrive.org;Y. Huang, et. ...
Article
Nowadays, more people choose to live in urban areas for economic, technological, sociological or political reasons. This is the cause of increasing population and population density in urban areas. The drastic increase in population can cause various problems. One of the most important problems that can be caused is traffic congestion and the increase in the resulting CO2 emissions. Urban communities have very dynamic mobility and in their mobility they prefer to use private vehicles rather than existing public transportationAlthough there has been Trans Sarbagita as a government effort to provide sustainable public transportation, not many people have taken advantage of the existence of Trans Sarbagita. As a result, Trans Sarbagita's total income is not proportional to its operational costs. In order to reduce operational costs, there are several ways that can be done, one of which is eco-driving. Eco-driving has been widely introduced as an environmentally friendly and economical way of driving that supports aspects of sustainable transportation, but not everyone practices or even knows this, including Trans Sarbagita drivers. Therefore, there is a need for an assessment of the level of eco-driving, especially on Trans Sarbagita to support the concept of sustainable transportation.The data used is a score based on the Guttman scale from the results of observations of five eco-driving indicators for 24 drivers on Trans Sarbagita Corridor II. Based on the analysis that has been carried out, the trans Sarbagita Corridor II eco-driving level is generally included in the medium category. The category level of the bus stop segment is divided into 2 categories, namely 8 bus stop segments with a high eco-driving category and 18 bus stop segments with a moderate eco-driving level. Based on the three aspects of sustainable transportation that must be met, Trans Sarbagita Corridor II is only able to meet all the criteria in the social aspect. In the economic aspect, Trans Sarbagita Corridor II is only able to meet the area accessibility criteria of the 3 existing criteria. Meanwhile, on the environmental aspect, Trans Sarbagita failed to meet the only criterion that must be met, namely the minimization of environmental pollution due to the impact of transportation. Based on the description above, from the three existing aspects, Trans Sarbagita Corridor II has not been supported by economic and environmental aspects, so it can be concluded that Trans Sarbagita Corridor II has not fully supported the concept of sustainable transportation.
... The increasing interest in environmental protection and resource conservation has prompted researchers to develop eco-friendly technologies to reduce vehicle energy consumption (Stoicescu, 1995;Boriboonsomsin et al., 2012;Wang et al., 2014). A promising method involves driving the vehicle at an appropriate speed, which is also called eco-driving control (Barkenbus, 2010). By operating vehicles at smooth speeds, eco-driving can reduce excessive energy consumption and greenhouse emissions, and even improve traffic throughput (Mensing et al., 2014;Dib et al., 2014). ...
Article
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Signalized intersections dominate traffic flow in urban areas, resulting in increased energy consumption and travel delay for the vehicles involved. To mitigate the negative effect of traffic lights on eco-driving control of electric vehicles, a multi-intersections-based eco-approach and departure strategy (M-EAD) is proposed to improve vehicle energy efficiency, traffic throughput, and battery life, while maintaining acceptable driving comfort. M-EAD is a two-stage control scheme that includes efficient green signal window planning and speed trajectory optimization. In the upper stage, the traffic light green signal window planning is formulated as a shortest path problem, which is solved using an A* algorithm for travel delay reduction. In the lower stage, the speed optimization problem is solved by resorting to a receding horizon framework, in which the energy consumption and battery life losses are minimized using an iterative dynamic programming algorithm. Finally, Monte Carlo simulation with randomized traffic signal parameters is conducted to evaluate the performance of the proposed M-EAD strategy. The results show the various advancements of the proposed M-EAD strategy over two benchmark methods, constant speed and isolated-intersection-based eco-approach and departure strategies in terms of energy efficiency, travel time, and battery life in stochastic traffic scenarios. In addition, the performance of M-EAD on actual road conditions is validated by on-road vehicle test.
... The energy in fuel consumed in driving is lost in many ways, including engine inefficiency, aerodynamic drag, rolling friction, and kinetic energy lost to braking (and to a lesser extent regenerative braking). Driver behavior can influence all of these, (2). ...
Article
Green driving describes techniques that drivers can use to optimize their automobile fuel economy. The energy in fuel consumed in driving is lost in many ways, including engine inefficiency, aerodynamic drag, rolling friction, and kinetic energy lost to braking (and to a lesser extent regenerative braking). Driver behavior can influence all of these. Since climate change and humanity responsibility has been widely accepted, many drivers have a new goal in mind: fuel efficiency. Eco-driving style is therefore often referred as smart driving because of the necessary complex tradeoff between the multiple goals the driver has to manage with. Studies usually simplifies the green way to drive using simple advices easily understood by drivers, but sometimes leading to a misunderstanding of the fuel efficient driving strategy. Other studies used trial experiments before and after a training program to assess the eco-driving impact. Effects of eco-driving on fuel consumption are well described in the literature, but results are often optimistic: CO2 emissions reduction can be up to 30% according to many studies. The key question for policy makers is “how big” of an emission reduction we can get by encouraging an eco-driving style, taking into account the diversity in the way to learn eco�driving: just reading a few driving tips, taking a course with a professional, or doing practical exercises with equipped vehicles? Moreover, there is a need to understand the best way to teach and learn eco-driving style, especially for young drivers. MOTOR VEHICLES & MOTORS 2012
... e windows remained closed to keep the aerodynamic drag the same throughout each test drive. e test vehicle, a 2017 Volkswagen eGolf, is equipped with a 35.8 kWh battery pack and offers multiple driving modes (i.e., Normal, Eco, and Eco+) and recuperation intensities (i.e., B, D, D1, D2, and D3) [36, 37], which in turn have a [38,39]. In this research, the driving mode "normal" was used since it uses the full power of the electric engine and, therefore, makes the best use of the vehicle's performance. ...
Article
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The widespread adoption of battery electric vehicles (BEVs) is hindered by their limited ranges and long charging times. Optimizing eco-driving strategies and BEV-specific routing through a thorough understanding of the BEV discharge behavior is vital to overcome these barriers in the short term. Therefore, this study investigates the impact of road types on the BEV discharge behavior while accounting for explanatory variables (i.e., ambient temperature, the initial state of charge, and driver). Thirty participants drove a 2017 Volkswagen eGolf along two predefined routes in Rhode Island. The results illustrate that BEVs are the most efficient on-road types with medium speed and low variation (i.e., “major collectors,” “minor arterials,” and “other principal arterials”). Meanwhile, findings confirmed a significantly higher average energy consumption rate on roads with higher average speeds (“interstates” and “other freeways/expressways”). Moreover, “local roads,” associated with a low average travel speed and a high variation in speed, showed a negative effect on BEV efficiency. The study further supported previous findings that BEVs are less efficient in colder temperatures. Thus, adapting eco-driving strategies, including the alteration of route choice to avoid “local roads” and “interstates,” can offer BEV drivers the potential for energy savings and range extensions. We propose a consideration of these findings to mitigate the effects of BEV range limitations and ease BEV adoption and ownership.
... In general, the fuel consumption of internal combustion engine vehicles (ICEVs) and EVs depends on: (1) vehicle characteristic parameters, such as engine size and age of the vehicle [3] and (2) driving behavior, such as driving speed and number of passengers [4]. In the literature, several studies have analyzed the influence of driving behavior on the change of fuel consumption [5][6][7]. These studies indicated that it is important to study the correlation between driving behavior factors and vehicle fuel consumption, which also concluded that the driving behavior factor was suitable to predict the fuel consumption of each vehicle. ...
... Studies have shown that AVs operated by petrol engines can positively impact the environment (Liu et al., 2019). Eco-driving will positively affect the fuel economy since it can provide moderate acceleration or deceleration, maintain low revolutions per minute (RPM), and prevent unnecessary idling (Barkenbus, 2010). According to Li et al. (2015), AVs would be connected to other vehicles and infrastructure, potentially decreasing fuel consumption, especially at signalized intersections. ...
Article
The factors affecting the acceptance of Autonomous Vehicles (AVs) in Saudi Arabia were examined by conducting a stated choice survey among 500 participants. Descriptive analysis showed that the participants believed that using AVs will decrease the risk of car crashes and help them safely reach their destination. Parametric analysis and prediction models showed a wide variation in public opinion regarding willingness to use AVs, despite an average high score on this factor. The study found that the trust in AVs was low, and women favored AVs more than men. Prediction models showed that age, trust, and being tech-savvy determine the willingness to use AVs. As younger participants had a high willingness to use AVs, we recommended focusing on changing the perception of older drivers to increase overall AV acceptance by increasing their trust in this new technology and highlighting the features of AVs. From the findings of this study, it is expected that wide-scale adoption of AV depends upon its competitiveness with the traditional and its performance in terms of enhancing road safety.
... Furthermore, FOTs follow a certain methodology for the study design and their respective experiments, generally the FESTA methodology (2). Both NDS and FOT are designed to investigate essential research objectives, such as improving road safety, transportation planning, and eco-driving (3)(4)(5)(6)(7)(8). Since their ultimate objective is similar to this research's scope, the terms are used interchangeably throughout the paper. ...
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The increasing accessibility of mobility datasets has enabled research in green mobility, road safety, vehicular automation, and transportation planning and optimization. Many stakeholders have leveraged vehicular datasets to study conventional driving characteristics and self-driving tasks. Notably, many of these datasets have been made publicly available, fostering collaboration, scientific comparability, and replication. As these datasets encompass several study domains and contain distinctive characteristics, selecting the appropriate dataset to investigate driving aspects might be challenging. To the best of the authors’ knowledge, this is the first paper that performs a systematic review of a substantial number of vehicular datasets covering various automation levels. In total, 103 datasets have been reviewed, 35 of which focused on naturalistic driving, and 68 on self-driving tasks. The paper gives researchers the possibility of analyzing the datasets’ principal characteristics and their study domains. Most naturalistic datasets have been centered on road safety and driver behavior, although transportation planning and eco-driving have also been studied. Furthermore, datasets for autonomous driving have been analyzed according to their target self-driving tasks. A particular focus has been placed on data-driven risk assessment for the vehicular ecosystem. It is observed that there exists a lack of relevant publicly available datasets that challenge the creation of new risk assessment models for semi- and fully automated vehicles. Therefore, this paper conducts a gap analysis to identify possible approaches using existing datasets and, additionally, a set of relevant vehicular data fields that could be incorporated in future data collection campaigns to address the challenge.
... As shown in Figure 5, several information policies were analyzed: standards (labelling), awareness campaigns, information provision, knowledge creation, and education. From these, both eco-driving and green public procurement, may have positive effects in reducing GHG emissions and air pollution [54][55][56][57]. However, awareness campaigns for eco-driving can have a short-term effect with drivers returning to their habitual behaviors [56]. ...
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Cities across the world are becoming more engaged in tackling climate change and contributing to the achievement of international agreements. The city of Curitiba in Brazil is no exception. In December 2020, the city published PlanClima (Plano Municipal de Mitigação e Adaptação às Mudanças Climáticas), a climate plan developed with local and international organizations. PlanClima aims to guide policies and actions to mitigate and adapt to climate change. This study focuses on selecting and qualitatively evaluating transport policies that contribute to the city’s 2030 climate and Sustainable Development Goals (SDGs). With PlanClima’s analysis for the transport sector in mind, nine targets for 2030 are identified and connected to different transport policies. To evaluate the possible interactions between the policies and the different dimensions of the SDGs, four types of linkages were designed: essential, uncertain, limited, and opposite. These categories were developed to evaluate the several dimensions in which a policy can have a positive or negative impact. The results show that the implementation of zero emission zones/low emission zones, green public procurement, subsidy schemes for the uptake of clean vehicle technology, and the digitalization of the transport system through smarter public transport and digital platforms that couple bike sharing, taxis, and public transport are some of the measures that can contribute to the achievement of Curitiba’s targets and ensure a positive impact on the sustainable development of the city. The study highlights how different policy instruments can contribute to achieve the city’s targets, thus providing guidance to policymakers.
... Par ailleurs, la fonction mathématique ainsi obtenue peut être aussi utilisée afin de générer une trajectoire de référence optimale en tenant compte de l'accélération latérale acceptable, du rayon de courbure minimale, de la vitesse maximale, de la présence d'un obstacle, etc. comme suggéré dans [Daniel et al., 2011]. [Barkenbus, 2010]. ...
Thesis
Le véhicule autonome ouvre des perspectives très intéressantes en matière de sécurité routière et de mobilité. Dans ce contexte, les travaux proposés visent à contribuer au guidage d’un véhicule autonome par la conception d’une architecture globale de commande en situations dites non conventionnelles de conduite. Ici, nous faisons face à deux types de situations non conventionnelles, à savoir, la limite de performance et la défaillance d’un actionneur. Dans un premier temps une architecture globale de commande pour le guidage latéral en situations classiques a été proposée. Cette architecture repose sur une commande par retour d’état robuste couplée à une action par feed-forward. Deux stratégies de commande ont été comparées l’une repose sur le calcul de l’erreur latérale par rapport au CoG et l’autre par rapport au CoP. Ces deux stratégies font l’objet d’une validation expérimentale à l’aide du véhicule d’essais du laboratoire et leurs performances sont discutées. L’architecture globale a été améliorée pour surmonter les limites de performances vis-à-vis de la discontinuité de la trajectoire de référence. L’amélioration proposée repose sur l’unification de l’approche géométrique avec l’approche dynamique. La limite de performance rencontrée lors de la perte de manœuvrabilité a été traitée en proposant un superviseur pour déclencher le système de sécurité qui combine les différents critères de stabilité. Le système de sécurité a été aussi amélioré par la technique d’allocation de commande permettant de mieux gérer le freinage différentiel. En utilisant la même technique, la défaillance du système de direction du véhicule a été étudiée en proposant une architecture globale de guidage d’urgence comportant les niveaux de génération de trajectoire et de commande afin d’assurer le guidage latéral et longitudinal du véhicule.
... To enhance the energy-efficiency in both on-road and offroad vehicles, an area of research that has received significant attention in both the automotive industry and the academia over the years, is eco-driving [1]. Previous studies in this research area focussed primarily on coaching drivers on the eco-driving techniques to promote energy-efficient driving style behaviors [2]. ...
Preprint
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This work proposes an eco-driving assistance system (EDAS) based on model predictive control (MPC) with a primary objective to improve the driver's driving style in an energy-efficient manner. To improve the efficiency of an EDAS, a learning-based approach to model the driver behavior from urban driving data collected using a dynamic driving simulator is presented. To cluster the driving data of thirty-four participants, unsupervised learning techniques such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used. Furthermore, to predict the driver speed error while tracking an advisory speed, both stochastic and deterministic models, namely Stochastic Volatility (SV) and Gated Recurrent Unit (GRU) respectively, are trained. Six new drivers evaluated the proposed concept, whose driving style is classified using a trained temporal convolution network (TCN). Using the predicted driver speed error, the eco-driving advisory speed is compensated and provided as a feedback to the driver via a human-machine interface (HMI). The results reveal that the deterministic model has been able to achieve higher prediction accuracy as compared to the stochastic model. Furthermore, the results also suggest that the drivers using EDAS with driver error compensation have been able to perform better advisory speed tracking and achieve improved energy savings.
... One potential strategy is eco-driving, referring to economical driving aimed at reducing fuel use and CO 2 emissions, or ecological driving aimed at reducing air pollutant emissions (Mensing et al., 2014). Typically, eco-driving focuses only on modifying driving behaviors (Barkenbus, 2010;Huang et al., 2018). Based on this scope, eco-driving requires modification of vehicle speed trajectories (hereafter referred to as trajectories) (He et al., 2015;Mensing et al., 2013Mensing et al., , 2011Xu et al., 2017;Yuan and Frey, 2020 The study objective is to quantify real-world mesoscale and microscale fuel use and emission rates reduction potential associated with LDGV eco-driving. ...
Article
Eco-driving offers potential to reduce fuel use and emission rates for light-duty gasoline vehicles (LDGVs). The objective is to quantify real-world route-level and segment-level fuel use and emission rates reduction potential for LDGV eco-driving. Three million seconds of real-world speed trajectory data were analyzed based on predominantly naturalistic driving of 160 drivers on eight mesoscale routes. The routes were further divided into 199 segments. A Vehicle Specific Power modal model was used to estimate trajectory-average fuel use and emission rates of CO2, CO, hydrocarbons, NOx, and particulate matter and to identify eco-driving trajectories. For route-level eco-driving, fuel use and emission rates reduction potential ranges from 6% to 40%, compared to average fuel use and emission rates estimated based on all trajectories. Eco-driving focused on fuel savings typically reduced air emissions and vice versa. Route-level eco-driving typically but not always concurrently reduces segment-level fuel use and emission rates. These co-benefits and tradeoffs can be used to guide LDGV eco-driving decisions.
... Sanguinetti et al. define 10 eco-driving behaviors [24], which provide a solid basis for developing policies and interventions. Recently, research on driver behavior occurring in electric vehicles has attracted wide attention [25][26][27][28][29]. Through proper information feedback and driver training, eco-driving will facilitate a reduction in fuel consumption and greenhouse gas (GHG) emissions [30]. ...
Article
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The widespread adoption of electric public buses has a positive effect on energy conservation and emission reduction, which is significant for sustainable development. This study aims to assess the safety and economy of electric buses based on drivers’ behavior. To this end, based on the remotely acquired travel data of buses, the driving operation behavior is analyzed, and four safety and four economic characteristic indicators are respectively extracted via safety analysis, correlation examination, and an R2 test. Then, the extreme learning machine (ELM) is leveraged to establish the safety evaluation model, and Elman neural network is employed to construct the economic evaluation model. A comparative analysis and a comprehensive evaluation are conducted for the behaviors of ten drivers. The results highlight that the proposed evaluation model that us based on the ELM and Elman neural network algorithm can efficiently distinguish the safety and economy of driving behavior. Furthermore, a graph of driving operation behavior is constructed and the analysis results also manifest that the change of driving operation behavior of buses with higher safety and economy lead to relatively stable characteristics. When the fluctuation of vehicle speed is smooth, the driver can implement moderate driving operation in real-time. One critical conclusion that was revealed through the study is that there exists a certain correlation between driving safety and economy, and buses with higher safety tend to be more economical. This study can provide a theoretical basis for planning the maneuvering and operation of electric buses, including driving speed, braking, and acceleration operations.
... This gives flexibility in adjusting speed and dwell times. The first strategy-eco-driving-can reduce energy consumption by 10% on average and is an overlooked climate-change initiative [23,24]. It requires anticipation of what is happening ahead to minimise the number braking events. ...
Article
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Transition to net zero greenhouse gas (GHG) emissions from urban transport requires strategies improving energy efficiency and contributing to energy conservation. Efficiency gains can be achieved via combination of new technologies, such as electrification, connectivity, and automation. Energy conservation focuses on reducing the total miles travelled by private cars. Supporting modal shift to public transport (PT) is the essential element of that strategy. It starts with policy support enabling time and space prioritisation of PT vehicles. Next, the emerging technologies can optimise performance and comfort of PT vehicles by making the best use of the assigned resources. This article shows how these technologies can reduce GHG emissions directly, as well as indirectly by making PT an attractive choice boosting patronage. A case study illustrating the improvement of the environmental performance of full hybrid buses via connectivity and geofencing is given.
... Extensive efforts have been made to improve vehicle efficiency and lower emissions of on-road vehicles in response to the increasingly stringent emission standards [2]- [4]. As a crucial technology in saving energy consumed by vehicles, eco-driving has been extensively discussed [5]- [9], with the core idea of adjusting vehicle speed and maintaining an energy-efficient driving style [10]. More recently, the development of connectivity and automation technologies has provided another promising opportunity to further cut down energy consumption through the deployment of connected and automated vehicles (CAVs). ...
Preprint
This paper presents an adaptive leading cruise control strategy for the connected and automated vehicle (CAV) and first considers its impact on the following human-driven vehicle (HDV) with diverse driving characteristics in the unified optimization framework for improved holistic energy efficiency. The car-following behaviors of HDV are statistically calibrated using the Next Generation Simulation dataset. In a typical single-lane car-following scenario where CAVs and HDVs share the road, the longitudinal speed control of CAVs can substantially reduce the energy consumption of the following HDV by avoiding unnecessary acceleration and braking. Moreover, apart from the objectives including car-following safety and traffic efficiency, the energy efficiencies of both CAV and HDV are incorporated into the reward function of reinforcement learning. The specific driving pattern of the following HDV is learned in real-time from historical speed information to predict its acceleration and power consumption in the optimization horizon. A comprehensive simulation is conducted to statistically verify the positive impacts of CAV on the holistic energy efficiency of the mixed traffic flow with uncertain and diverse human driving behaviors. Simulation results indicate that the holistic energy efficiency is improved by 4.38% on average.
... One of the prominent technology to achieve efficient vehicle operation is eco-driving control [3]. Eco-driving can reduce excessive energy consumption and greenhouse emissions, improve traffic throughput by adjusting such vehicle movement as avoiding heavy acceleration, deceleration, and idling [4]. Several researches have demonstrated eco-driving can improve energy efficiency from 15 % to 40 % effectively [5,6]. ...
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Signalized intersection can interrupt the traffic flow, potentially increasing vehicle energy consumption levels and extending travel time. This paper presents an eco-approach and departure strategy (EAD) using a double-layer control scheme, to achieve the optimization of energy economy and traffic throughput for connected electric vehicle (EV) at the route with multiple intersections and varying road slopes. In the first layer, for improving traffic throughput, we design a traffic light green window planning method with consideration of the spatio-temporal coupling of intersections. Then, in the second layer, a distance-domain optimal control problem (OCP) is formulated considering the intersection segment, green window intervals, and speed limitations. This OCP is solved by dynamic programming algorithm, to derive the energy-optimal speed. Finally, a simulation is conducted using the route from a real-world scenario in Nanjing, China, to evaluate the performance of the proposed strategy. The results indicate the proposed EAD strategy can improve EV energy efficiency and traffic throughput dramatically compared with the regular constant speed strategy.
Article
Accurate real-time energy consumption prediction of electric buses (EBs) is essential for bus operation and management, which can effectively mitigate the driving range anxiety while reducing the operation cost simultaneously. This paper presents a machine learning-based energy consumption prediction method for EB, which combines driving data with road characteristics data (such as road type), traffic condition (such as peak hour), and meteorology data (such as temperature). The importance of driving behavior features affecting energy consumption is quantitatively revealed by the novel Shapley additive explanation (SHAP). Given the road characteristics, traffic condition and meteorology information, a Long Short-Term Memory (LSTM) network is then used to predict driving microscopic parameters, including speed, acceleration, gas pedal position and brake pedal position. Finally, the instantaneous electricity consumption is predicted using an Extreme Gradient Boosting (XGBoost) model based on the predicted values from the LSTM. The results show that the proposed LSTM-XGBoost model with accurate time series prediction and regression is powerful for efficiently fitting the complex volatility of energy consumption. Moreover, the proposed model chain outperforms other model combinations (such as artificial neural networks and conventional regression methods) in terms of root mean squared error (RMSE = 0.079), mean absolute error (MAE = 0.086) and R-square (R² = 0.814).
Article
Heavy-duty diesel truck (HDDT) is one of the major sources of air pollution and energy consumption. To reduce the estimation bias and improve the interpretability, the random parameters logit (RPL) model was employed to examine the effects of influencing factors on fuel consumption of HDDTs in the real world. The unobserved heterogeneity effects varying across the samples on fuel efficiency were extracted from the long-term daily trip-based data. In order to further illustrate the advantages of the RPL model in explaining the impacts of factors, a fixed parameters logit model with twenty parameters was constructed and compared. The Akaike information criterion and the Bayesian information criterion were used to select a more reasonable model structure. The findings show that the RPL model performs better and the unobserved heterogeneity would affect the effects of factors of rolling without engine load proportion and temperature and, consequently, map the level of fuel consumption. This reveals the variability of the fuel consumption among the samples. Driving compensation effects were also identified in this study (i.e., the drivers tend to perform the fuel-saving operations in adverse driving circumstances and vice versa). The methodology proposed in this paper can provide a new insight for researchers to identify the instability of energy-related factors under real road conditions. Future research could be implemented to assess the similar effects of alternative fuel vehicles.
Article
Eco-driving is the driving behavior that improves fuel efficiency and reduces emissions. It is important to develop a method to accurately evaluate driving behaviors to support eco-driving training. However, the lack of evaluation baseline in the previous studies leads to uncertainty in the eco-driving evaluation under different traffic conditions. This study develops a method for evaluating driving behaviors in relation to the vehicle trajectory data of 19,779 drivers based on speed-specific distributions of vehicle-specific power (VSP), and develops the speed-specific baseline of VSP distributions and the baseline of fuel rates from the drivers’ driving behaviors. The results show the following: (1) the speed-specific VSP distributions can evidently characterize the differences among individuals’ eco-driving behavior; (2) the proposed evaluation method based on the baseline is able to overcome the uncertainty of eco-driving evaluation under different traffic conditions. The findings are helpful in supporting eco-driving training.
Book
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Available at https://www.mdpi.com/journal/sustainability/special_issues/Mobility This Special Issue (SI) aims to increase understanding of the impacts and effects of mobility and transport in sustainability. In particular, we are seeking papers focused on the challenges and obstacles on a system-level decision making of clean mobility and indirect effects caused by these changes. The proposed papers should have an international context and they should contribute cutting-edge studies in the relevant fields. These include but are not limited to sustainability, transport geography, mobility studies, transport research, and social scientific technology studies. The SI welcomes both theoretical as well as empirical papers on these topics. In addition, public sector analysis and transport planning papers are most welcome. The articles should be targeted to the academic community as well as to practitioners, such as developers, planners, and officers in order to increase understanding of the dynamics of sustainable mobility and transport. The identified broad research themes may be addressed through potent case studies or international comparisons. The SI explores academic, conceptual, methodological, or application-based research work conducted across the globe, with a strong connection to technological development and/or the analysis of mobility/transportation projects.
Article
Although most people are aware of the harmful CO2 emissions produced by the transport sector threatening life on earth now and in the future, they do not eco-drive. Eco-driving improves the vehicle’s fuel or energy economy and reduces greenhouse gas emissions. We investigated the motivational predictors of eco-driving based on the theory of self-concordance (i.e., the consistency between a behavior/goal with the person’s pre-existing values and interests). Data from a cross-sectional online survey with 536 German drivers revealed that self-reported eco-driving was significantly predicted by sustained effort towards eco-driving, which in turn was predicted by self-concordance variables. Therefore, individuals pursuing eco-driving out of strong interest or deep personal beliefs (i.e., autonomous motivation) as opposed to external forces or internal pressures (i.e., controlled motivation) reported greater effort towards this behavior. Furthermore, biospheric striving coherence, i.e., the coherence between personal valuable biopsheric values (i.e., values addressing the well-being of the environment/biosphere) and eco-driving, significantly predicted effort towards eco-driving. In sum, our results suggest that autonomous rather than controlled motives and coherence between behavior and intrinsic rather than extrinsic values are relevant predictors for eco-driving. We discuss implications for future strategies and interventions fostering eco-driving in the long term.
Conference Paper
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The African Union's Agenda 2063 and NDS30 place university research at the center of the conception of ideas, tools, and instruments aimed at the industrial emergence of Cameroon. The National Higher Polytechnic School of Douala (NHPSD), as a component of the University of Douala is firmly committed to this perspective. The research presented in this book emanates from fundamentally industrial themes and is in line with the objectives of the SND30. The researchers and the Ph.D. students of the NHPSD work actively to implement the training and research match to support the development of Cameroonian industries declared by the Government. The NHPSD through the Energy Materials Modeling and Methods Laboratory (LE3M) focuses its research themes in 07 typical courses, namely: - Mechanical and Materials Engineering; - Civil Engineering; - Electrical and Robotics Energy; - Telecommunications and Information Systems; - Methods; - Energy; - Bioprocesses and Applied Chemistry. These typical courses respond to the request of the axes of the NDS30 on the strategic orientation in the fields of Construction-Services-Professional Scientific-Technical for which four (04) orientations are retained: (i) structure, articulate, and optimize the operational link and functional between the construction industry and professional, scientific, and technical activities; (ii) build an efficient and competitive construction industry; (iii) make a solid national capacity for the management of industrial and infrastructural projects and programs; (iv) create conditions favoring the development of national orders of critical professional disciplines. This pioneering work at the NHPSD aims to place the University of Douala on the starting block of the emergence of Cameroon by 2035, the annual publication of these acts will be firmly established over time. It will constitute a decision-making aid tool for decision-makers, industrialists, and scientists who work every day for the development of Cameroon.
Article
Eco-driving strategies have been shown to provide significant reductions in fuel consumption. This paper outlines an active driver assistance approach that uses a residual policy learning (RPL) agent trained to provide residual actions to default power train controllers while balancing fuel consumption against other driver-accommodation objectives. Using previous experiences, our RPL agent learns improved traction torque and gear shifting residual policies to adapt the operation of the powertrain to variations and uncertainties in the environment. For comparison, we consider a traditional reinforcement learning (RL) agent trained from scratch. Both agents employ the off-policy Maximum A Posteriori Policy Optimization algorithm with an actor-critic architecture. By implementing on a simulated commercial vehicle in various car-following scenarios, we find that the RPL agent quickly learns significantly improved policies compared to a baseline source policy but in some measures not as good as those eventually possible with the RL agent trained from scratch.
Article
Vehicle fuel efficiency (VFE) has a pivotal role in solving energy shortage issue due to the increasing global demand for energy. The high frequency of go-stop movements and long waiting times at intersections significantly reduce the VFE. Such negative impacts are particularly severe when the traffic flows are regulated by poorly designed traffic signal control. Existing works have successfully applied deep reinforcement learning (DRL) techniques to improve the efficiency of traffic signal control. However, to the best of our knowledge, few studies have explored traffic signal control for VFE through eco-driving techniques. To fill the gap, we propose a DRL-based fuel-economic traffic signal control for improving vehicle fuel efficiency. Briefly, we adopt the DRL-technique to develop an agent that can efficiently control traffic signals based on real-time traffic information at intersections, and adjust speed profiles for approaching vehicles to smooth traffic flows. We tested our method on both synthetic traffic dataset and real-world traffic dataset from surveillance cameras in Toronto. Through comprehensive experiments, we demonstrate that our method surpassed the performance of both pure eco-driving and pure traffic signal control techniques by significantly reducing vehicle fuel consumption and improving the efficiency of traffic signal control.
Article
During the operation of motor vehicles, their operational parameters change. It is related to the influence of working and external factors on the combustion engine while driving. For many years, there has been a discussion on the improvement of the ecological properties of vehicles related to technical and legal issues as well as the technique of vehicle operation and use. The article presents selected research results of a vehicle used in urban traffic using the eco-driving technique in relation to a normal driving style. The results of the influence of selected parameters, such as: average speed, driving time, mileage, road type, number of stops, number of brakes are presented. The object of the research was a passenger car powered by a spark-ignition engine, running in the so-called routine route, in a selected area of the city of Lublin in south-eastern Poland. The analysis shows that the use of eco-driving techniques has some benefits in urban traffic.
Article
During the operation of vehicles, their operational parameters change, which is related to the influence of working factors, the current situation on the road and its capacity, as well as the wear processes of the vehicle components. Improving the operational and ecological properties of motor vehicles, taking into account technical, legal and operational factors, is a constantly important issue. The article presents selected test results of a vehicle used in the eco-driving technique in urban traffic. The main objective of the study was to verify the possibility of limiting intensive braking and the number of braking thanks to the use of eco-driving. Limitations of intensive braking allow extending the service life of the friction elements of the braking system. The research was carried out on the so-called routine route from work to home in a selected area of the city of Lublin in south-eastern Poland. The obtained research results focus on the influence of selected driving parameters, such as: mileage, road characteristics, number of vehicle stops, number of vehicle stops at intersections and in front of pedestrian crossings, depending on the driving style. The analysis of the results shows the benefits of eco-driving in city traffic.
Article
The demand for motor fuel should decline when its price rises, but how exactly does that happen? Do people drive less, do they drive more carefully to conserve fuel, or do they do both? To answer these questions, we use data from the German Mobility Panel from 2004 to 2019, taking advantage of the fluctuations in motor fuel prices over time and across locales to see how they affect Vehicle Kilometers Traveled (VKT) and on-road fuel economy (expressed in kilometers per liter) for gasoline and diesel cars. Our reduced-form regressions show that while the VKTs driven by gasoline cars decrease when the price of gasoline rises, there is virtually no response among diesel cars. Likewise, the on-road fuel economy is largely unresponsive to fuel price changes, irrespective of the fuel type. Since the price elasticity of fuel consumption is the difference between the price elasticity of VKT and the price elasticity of the fuel economy, our results suggest that the fuel economy might be the “weakest link” of price-based policies that seek to address environmental externalities.
Article
This paper proposes a simulation framework that quantitatively assesses the direct and indirect effects of connected and autonomous vehicles (CAVs) on a supply chain system by varying the levels of CAV market penetration and driverless truck adoption. To quantify CAV effects on transportation network, this paper first collects secondary data and adopts simulation parameters and equations from existing literature. The results from transportation analysis are then incorporated into supply chain analysis to evaluate how CAVs would change supply chain performance measured by total travel time, greenhouse gas emissions, and supply chain cost. As the performance of supply chain systems involving perishable or semi-perishable products is highly sensitive to CAV market penetration rate and driverless truck adoption rate mainly because of reduced travel time, this paper uses fresh potato supply chain systems as an illustrative example. The case study results indicate that CAVs can greatly improve supply chain performance directly and indirectly by decreasing total travel time and supply chain costs, whereas emissions are reduced primarily through the adoption of driverless trucks in the supply chain system. The effect of CAVs on supply chain performance becomes even greater when commodities travel longer distances. This study will allow supply chain managers (and grocery delivery companies) to better understand how supply chain design and operation could be transformed and reoptimized in response to the introduction of CAV technologies. The research outcomes would help them better utilize the opportunities and address possible challenges that may arise as a result of CAVs.
Article
Ecological cruising control methods of vehicles have been extensively studied to further cut down energy consumption by optimizing vehicles’ speed profiles. However, most controllers cannot be put into practical application because of future terrain data requirements and excessive computational demand. In this paper, an eco-cruising strategy with real-time capability utilizing deep reinforcement learning is proposed for electric vehicles (EVs) propelled by in-wheel motors. The deep deterministic policy gradient algorithm is leveraged to continuously regulate the motor torque in response to road elevation changes. By comparing the proposed strategy to the energy economy benchmark optimized with dynamic programming (DP), and traditional constant speed (CS) strategy, its learning ability, optimality, and generalization performance are verified. The simulation results show that without a priori knowledge about the future trip, the proposed strategy provides 3.8% energy saving compared with the CS strategy. It also yields a smaller gap than the globally optimal solution of DP. By testing on other driving cycles, the trained strategy reveals good generalization performance and impressive computational efficiency (about 2 ms per simulation step), making it practical and implementable. Additionally, the model-free characteristic of the proposed strategy makes it applicable for EVs with different powertrain topologies.
Chapter
Fuel efficiency calibrated by the vehicle manufacturer is rarely obtained in real-time transient driving conditions. This happens because the fuel consumption of a vehicle depends on not only internal parameters like vehicle model, engine efficiency, and type of fuel but also on external conditions like driving behavior, traffic conditions, and road type. Out of these, driving behavior is one of the important factors affecting fuel consumption. The purpose of this chapter is to define an accurate, simple, and parsimonious model, the clustered generalized linear model (GLM), to predict fuel consumption using real-time trip data gathered using naturalistic conditions. The stepwise forward GLM regression method is used to select the most appropriate model using the Akaike information criterion. Different GLM models were evaluated on mean average percentage error and root mean square error metrics. The proposed method significantly outperformed other models in terms of accuracy as well as simplicity.
Article
Limited capacity and short life cycle of a battery are the major impediments in development of practical Electric Vehicles (EVs). Eco-driving is an optimization technique through which a velocity trajectory that consumes minimum energy is advised to the driver. However, presence of traffic signals to control large traffic network degrades the performance of eco-driving; as applying brakes to stop and then maximum re-acceleration to restart a trip consumes lot of energy. Eco-driving problem with multiple traffic signals and static model of battery has been proposed as Two Point Boundary Value Problem (TPBVP). TPBVP fails to solve multi-phase problem as a single phase due to discontinuity of the co-states at the junction, that is, start of a new phase. This paper investigates an optimal solution with both EV and battery dynamics in the presence of multiple traffic signals as Multi Point Boundary Value Problem (MPBVP) using multiple shooting technique. Traffic signals come at some intermediate points of a trip. MPBVP ensures continuity at the junction to solve the multi-phase problem as a single phase through inter dependencies between each phases. Goal of this work is not only to solve constrained eco-driving problem with traffic signals but also include charging and discharging limits on battery that indirectly improves battery’s life cycle. Results indicate that EV has crossed all the traffic signals during their green duration without applying brakes with also satisfying all the other constraints and continuity condition. Moreover, it can be seen that energy consumption using MPBVP is also marginally lesser as compared to TPBVP.
Article
Eco-driving is a term used to refer to a strategy for operating vehicles so as to minimize energy consumption. Without any hardware changes, eco-driving is an effective approach to improving vehicle efficiency by optimizing driving behavior, particularly for autonomous vehicles. Several approaches have been proposed for eco-driving, such as dynamic programming, Pontryagin’s minimum principle, and model predictive control; however, it is difficult to control the speed of the vehicle optimally in various driving situations. This study aims to derive an eco-driving strategy for reducing the energy consumption of a vehicle in diverse driving situations, including road slopes and car-following scenarios. A reinforcement learning-based energy efficient speed planning strategy is proposed for autonomous electric vehicles, which learn an optimal control policy through a data-driven learning process. A model-based reinforcement learning algorithm is developed for the eco-driving strategy; based on domain knowledge of the vehicle powertrain, a battery energy consumption model and longitudinal dynamics model of the vehicle are approximated from the driving data and are used for reinforcement learning. The proposed algorithm is tested using a vehicle simulation, and is compared to a global optimal solution obtained using an exact dynamic programming method. The simulation results show that the reinforcement learning algorithm can adjust the speed of the vehicle by considering driving conditions such as the road slope and a safe distance from the leading vehicle while minimizing energy consumption. The reinforcement learning algorithm achieves a near-optimal performance of 93.8% relative to the dynamic programming result.
Chapter
In recent years, modern society has been facing more traffic jams, higher fuel prices and an increase in Carbon Dioxide emissions. According to NASA Global Climate Change, the current warming trend is extremely likely (greater than 95% probability) to be the result of human activity since the mid-20th century. Although general awareness in sustainability issues has improved in recent years through mass media coverage, this knowledge is not always translated into actual sustainable practice. The transportation sector consumes more petroleum than any other sector, and that share has increased over time from about 50% in 1950 to about 70% in 2018. In 2016, light-duty vehicles accounted for 58.5% of transportation energy use while medium/heavy-duty trucks and buses accounted for 23.9%. Vehicle miles travelled was seven times higher in 2017 than in 1950. In the transportation sector, the Fourth Industrial Revolution (Industry 4.0) emphasizes advances in communication and connectivity with breakthroughs in emerging technologies in fields such as fully autonomous vehicles sector. Small to mid-sized cities are not always wealthy enough to adopt these infrastructure changes so sustainable transportation falls on the decision of commuters. This paper shows how gamification can be linked to the components of Industry 4.0 to encourage drivers to drive less aggressively and, thus, more environmentally friendly. The gamification approach is illustrated using a sample of nine college-aged drivers but can be extended to fleet drivers.
Article
Significant effects at signalized intersections include the cost associated with extra travel time, fuel consumption, and gas emissions due to the stop-and-go or idle process of vehicles waiting for traffic lights. Based on vehicle instantaneous states, this paper firstly develops a mathematical function of ecological cost to quantify the monetary impact of the signal control scheme on users and the environment. And then, this paper contributes to developing a vehicle kinematic model to describe the vehicle movement process and estimate the instantaneous states (i.e., speed, acceleration/deceleration) by second, and correspondently proposing a new eco-oriented signal control framework at isolated intersections. Finally, the proposed travel time cost and ecological cost are taken as measures of effectiveness to identify four key factors of the eco-oriented model in traffic volume in key signal phases, directional volume ratio, electric vehicle ratio, and bus ratio. This study found the eco-oriented signal method led to a significant difference in ecological cost reduction of 5% or more compared to the delay-oriented control group under the low traffic volume in key phases, the electric vehicles dominate and no less than 5% of bus ratio.
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Tested the hypothesis that providing immediate feedback to homeowners concerning their daily rate of electricity usage would be effective in reducing electricity consumption. In the 29 physically identical 3-bedroom homes used in the study, central air conditioning was the largest single source of electricity usage during the summer. Accordingly, it was possible to predict the household's expected electricity consumption in terms of the average daily outdoor temperature. Feedback was expressed as a percentage of actual consumption over predicted consumption, and it was displayed to the homeowners 4 times/wk for approximately 1 mo. Results confirm the hypothesis. Before feedback began, the feedback and control groups were consuming electricity at approximately equal rates. During the feedback period, the feedback group used 10.5% less electricity. The effectiveness of the feedback procedure is discussed in terms of its cuing, motivational, and commitment functions. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Despite the large contribution of individuals and households to climate change, little has been done in the US to reduce the CO2 emissions attributable to this sector. Motor vehicle idling among individual private citizens is one behavior that may be amenable to large-scale policy interventions. Currently, little data are available to quantify the potential reductions in emissions that could be realized by successful policy interventions. In addition, little is known about the motivations and beliefs that underlie idling. In the fall of 2007, 1300 drivers in the US were surveyed to assess typical idling practices, beliefs and motivations. Results indicate that the average individual idled for over 16Â min a day and believed that a vehicle can be idled for at least 3.6Â min before it is better to turn it off. Those who held inaccurate beliefs idled, on average, over 1Â min longer than the remainder of the sample. These data suggest that idling accounts for over 93Â MMt of CO2 and 10.6 billion gallons (40.1 billion liters) of gasoline a year, equaling 1.6% of all US emissions. Much of this idling is unnecessary and economically disadvantageous to drivers. The policy implications of these findings are discussed.
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The development and manufacture of hybrid vehicles, which combine a conventional engine, and a battery-powered electric motor to achieve improved fuel economy and performance, are discussed. A full-fledged hybrid car, such as a Toyota Prius travels twice the distance that of an average American car on a gallon of gas. A full-fledged hybrid vehicle provides fuel economy of 60 percent, which is achieved from regenerative braking, that captures as electrical power much of the energy lost as frictional heat. Another benefit of the hybrid vehicles is that the efficient use of gasoline results in lower emissions of carbon dioxide, which is the primary greenhouse gas.
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