Ecodriving and Carbon Footprinting: Understanding How Public Education Can Reduce Greenhouse Gas Emissions and Fuel Use

Ecodriving is a collection of changes to driving behavior and vehicle maintenance designed to impact fuel consumption and greenhouse gas (GHG) emissions in existing vehicles. Because of its promise to improve fuel economy within the existing fleet, ecodriving has gained increased attention in North America. One strategy to improve ecodriving is through public education with information on how to ecodrive. This report provides a review and study of ecodriving from several angles. The report offers a literature review of previous work and programs in ecodriving across the world. In addition, researchers completed interviews with experts in the field of public relations and public message campaigns to ascertain best practices for public campaigns. Further, the study also completed a set of focus groups evaluating consumer response to a series of websites that displayed ecodriving information. Finally, researchers conducted a set of surveys, including a controlled stated-response study conducted with approximately 100 University of California, Berkeley faculty, staff, and students, assessing the effectiveness of static ecodriving web-based information as well as an intercept clipboard survey in the San Francisco Bay Area. The stated-response study consisted of a comparison of the experimental and control groups. It found that exposure to ecodriving information influenced people’s driving behavior and some maintenance practices. The experimental group’s distributional shift was statistically significant, particularly for key practices including: lower highway cruising speed, driving behavior adjustment, and proper tire inflation. Within the experimental group (N = 51), fewer respondents significantly changed their maintenance practices (16%) than the majority that altered some driving practices (71%). This suggests intentionally altering driving behavior is easier than planning better maintenance practices. While it was evident that not everyone modifies their behavior as a result of reviewing the ecodriving website, even small shifts in behavior due to inexpensive information dissemination could be deemed cost effective in reducing fuel consumption and emissions

Characterizing driving behavior and link to fuel consumption for university campus shuttle minibuses

Abstract: This paper focuses on the effect of aggressive driving behavior on fuel consumption of a vehicle. Different from the traditional statistical analysis method, this paper adopts the frequency domain analysis method to analyze driving aggressiveness and apply a quantitative driving aggressiveness evaluation metric. At the same time, the fuel consumption impact caused by the driving aggressiveness under different driving situations is analyzed. The results are demonstrated for two university shuttle bus. Fuel consumption rate of each vehicle is determined by using available on-board diagnostics (OBD) data including intake air mass flow rate of engine and air/fuel equivalence ratio. The experimental results show that the degree of influence of driving aggressiveness on fuel consumption is not the same in different driving situations. The higher the speed of the driving situation, the greater the difference in fuel consumption caused by driving aggressiveness.Communication présentée lors du congrès international tenu conjointement par Canadian Society for Mechanical Engineering (CSME) et Computational Fluid Dynamics Society of Canada (CFD Canada), à l’Université de Sherbrooke (Québec), du 28 au 31 mai 2023

Driving behaviour and sustainable mobility-policies and approaches revisited

Climate change is receiving increasing attention in recent years. The transportation sector contributes substantially to increased fuel consumption, greenhouse gas (GHG) emissions, and poor air quality, which imposes a serious respiratory health hazard. Road transport has made a significant contribution to this effect. Consequently, many countries have attempted to mitigate climate change using various strategies. This study analysed and compared the number of policies and other approaches necessary to achieve reduced fuel consumption and carbon emission. Frequency aggregation indicates that the mitigation policies associated with driving behaviours adopted to curtail this consumption and decrease hazardous emissions, as well as a safety enhancement. Furthermore, car-sharing/carpooling was the least investigated approach to establish its influence on mitigation of climate change. Additionally, the influence of such driving behaviours as acceleration/deceleration and the compliance to speed limits on each approach was discussed. Other driving behaviours, such as gear shifting, compliance to traffic laws, choice of route, and idling and braking style, were also discussed. Likewise, the influence of aggression, anxiety, and motivation on driving behaviour of motorists was highlighted. The research determined that driving behaviours can lead to new adaptive driving behaviours and, thus, cause a significant decrease of vehicle fuel consumption and CO2 emissions. - 2018 by the authors.Scopu

Reducing Greenhouse Gas Emissions From U.S. Transportation

Outlines the need to cut transportation emissions to limit climate change effects, mitigation options and technologies, policies to promote mitigation, and various scenarios for public attitudes, public policy, technological progress, and energy prices

Vehicle Parameters Estimation and Driver Behavior Classification for Adaptive Shift Strategy of Heavy Duty Vehicles

Commercial vehicles fulfill the majority of inland freight transportation in the United States, and they are very large consumers of fuels. The increasingly stringent regulation on greenhouse-gas emission has driven manufacturers to adopt new fuel efficient technologies. Among others, advanced transmission control strategy can provide tangible improvement with low incremental cost. An adaptive shift strategy is proposed in this work to optimize the shift maps on-the-fly based on the road load and driver behavior while reducing the initial calibration efforts. In addition, the adaptive shift strategy provides the fleet owner a mean to select a tradeoff between fuel economy and drivability, since the drivers are often not the owner of the vehicle. In an attempt to develop the adaptive shift strategy, the vehicle parameters and driver behavior need to be evaluated first. Therefore, three research questions are addressed in this dissertation: (i) vehicle parameters estimation; (ii) driver behavior classification; (iii) online shift strategy adaption. In vehicle parameters estimation, a model-based vehicle rolling resistance and aerodynamic drag coefficient online estimator is proposed. A new Weighted Recursive Least Square algorithm was developed. It uses a supervisor to extracts data during the constant-speed event and saves the average road load at each speed segment. The algorithm was tested in the simulation with real-world driving data. The results have shown a more robust performance compared with the original Recursive Least Square algorithm, and high accuracy of aerodynamic drag estimation. To classify the driver behavior, a driver score algorithm was proposed. A new method is developed to represent the time-series driving data into events represented by symbolic data. The algorithm is tested with real-world driving data and shows a high classification accuracy across different vehicles and driving cycles. Finally, a new adaptive shift scheme was developed, which synthesizes the information about vehicle parameters and driver score developed in the previous steps. The driver score is used as a proxy to match the driving characteristics in real time. Drivability objective is included in the optimization through a torque reserve and it is subsequently evaluated via a newly developed metric. The impact of the shift maps on the objective drivability and fuel economy metrics is evaluated quantitatively in the vehicle simulation. The algorithms proposed in this dissertation are developed with practical implementation in mind. The methods can reduce the initial calibration effort and provide the fleet owner a mean to select an appropriate tradeoff between fuel economy and drivability depending on the vocation

The Effects of the Use of Serious Game in Eco-Driving Training

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

Managing the transition toward self-sustaining alternative fuel vehicle markets : policy analysis using a dynamic behavioral spatial model

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 68-75).Designing public policy or industry strategy to bolster the transition to alternative fuel vehicles (AFVs) is a formidable challenge as demonstrated by historical failed attempts. The transition to new fuels occurs within a dynamically complex system with many distributed actors, long time delays, several important feedback relationships, and multiple tipping points. A broad-boundary, behavioral, dynamic model with explicit spatial structure was previously developed to represent the most important AFV transition barriers. Using California as an illustrative testing region, the model simulates the spatial diffusion of entrant vehicle/fuel technology pairs individually or in competition with other entrants. In this work, the integrated model is carefully parameterized for various specific alternative vehicle technologies. Structural and parametric sensitivity analyses are used to build understanding of system behavior and to identify policy leverage points or the need for further model calibration.(cont.) The qualitative impacts of policies are tested individually and then in multi-policy combinations to find synergies. Under plausible assumptions and strong policies, AFVs can achieve successful diffusion but this process requires long time periods. Findings indicate some commonly suggested policies may provide little leverage and be very costly. The analysis reveals the importance of designing policy cognizant of the system structure underlying its dynamic behavior. Several examples demonstrate how policy leverage varies with context such as key attributes of the alternative vehicle technology. Broadly, coordinated portfolios of policy instruments should be designed to simultaneously develop consumer familiarity, well distributed fueling infrastructure, and manufacturer knowledge at similar rates and over long enough duration to surpass thresholds in these complementary assets before alternative fuel and vehicle markets become self-sustaining. Further, policy should dynamically adapt to observed conditions to lessen the transition constraints dominant at the time. Policy and strategy makers must recognize from the outset that incentives must be stable over long durations for AFV transitions to succeed.by Derek R. Supple.S.M

Transportation Behavioral Data and Climate Change

In 2017, transportation became the largest single source of greenhouse gas emissions from the United States. Globally, the 2014 Intergovernmental Panel on Climate Change report found that, without far more aggressive policies, “transportation emissions could increase at a faster rate than emissions from other energy end use sectors” reaching 12 Gt CO2-eq/year by 2050 (Sims et al., 2014). The overwhelming challenge of combatting these emissions is made far more difficult by the fact that so little is known about transportation behavior. To use a cliché – if we can’t measure it, we can’t manage it. And transportation must be managed if we are to avoid the most catastrophic consequences of climate change. In this dissertation, I propose that better data collection is necessary to achieve reduction of transportation-related emissions. Happily, advances in technology make this more feasible today than at any time in the past. The costs of massive computing resources have gone down, the world is swarming with mobile devices like smartphones and connected cars collecting massive (if messy) amounts of data, and new techniques in data science and machine learning have emerged to help get clean answers out of all that data in a privacy-appropriate manner. In some cases, these new techniques will displace older ones. In other cases, the old ways have inherent advantages. In other cases yet, fusing new and old techniques will yield the most productive results.In Chapter One, I lay out a framework to organize the types of transportation behavioral data that must be collected regularly to adequately measure and manage transportation’s impact on climate. This builds on classic climate impact frameworks, adapting them to the particular measurement challenges presented by transportation. In Chapter Two, I provide a history of US transportation data collection since World War II as well as a review of traditional, modern, and emerging transportation data collection technologies. I then map each technology onto each behavioral data collection need identified in Chapter One, matching each behavior to the best respective data collection technique.Chapters Three and Four provides an example of analysis done using the traditional data collection techniques, notably Household and Commercial Travel Surveys, to explore changes in PMT related to shopping and retail freight since 1969, as well as freight for fuel transportation. They demonstrate and take advantage of the key benefits of traditional techniques: that they go back in history, that they collect clearly stated trip purposes, vehicle occupancies, demographics (including gender, an important demographic but particularly difficult to deduce from the new data collection sources), trip distances, chaining behavior, commodities logged, and more. As it turns out, these benefits are critical: the historical trends of the past 40 years allow behavioral insight that would not have been possible with a shorter term study, and gender dynamics are key to understanding the behaviors at hand. However, the analysis in Chapters Three and Four also highlights some of the key limitations of survey-based analysis. The fact that data was only collected every five to ten years severely limits the analysis, such as limiting the exploration that can be done on the impacts of the Great Recession. In addition, fallibilities in human memory are especially pronounced in short trips, trip chains, and non-work related trips, all of particular importance to this study. Chapters Five lays out theoretically, and then Chapter Six demonstrates via case study in India, how personal GPS diary devices can be used to log detailed data about individual trips. It demonstrates the key benefit of this data – highly individualized characteristics. Taking the example of vehicle electrification, this chapter demonstrates two ways such granular data is important: in one example, such data to give feedback to an individual to influence their car buying behavior. In the second, the granularity found with this new data collection techniques reveals the importance of highly localized policy making and emissions modeling based on driving patterns in different cities.Chapter Seven uses the emerging technology of mass amounts of locational data, collected passively via smart phones, to explore how urban density at home and work interacts with total, work-related, and non-work-related miles driven. This demonstrates the great strength of this type of data – massive sample size combined with high spatial granularity and longitudinal data collection. These strengths enable the analysis at statistically meaningful scale of patterns across many geographies, individuals, and times of year. Thus, this data can shed light on questions about the relationship of density and miles travelled which previously have not been answered conclusively due to data constraints

University of Richmond Climate Action Plan

The Climate Action Plan for the University of Richmond establishes the framework for achieving the University’s climate action goals under the American College and University Presidents Climate Commitment. The University of Richmond’s goal is to reduce greenhouse gas emissions 30% by 2020 and 100% by 2050. In addition to emissions reduction, the Climate Action Plan articulates goals for embedding sustainability into the curricular and co-curricular aspects of a Richmond education. The plan has been developed under the leadership of the Sustainability Working Group and the University’s Sustainability Coordinator. Climate Action Plan subgroups, with representation of staff, faculty, and students, drafted sections of the plan establishing the time frame and action items associated with the ambitious goals of carbon neutrality and sustainability education. A draft of the Climate Action Plan was published online and input from the entire University of Richmond community was sought and incorporated as appropriate into the final draft