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Simulator-Based Eco-drive Training for Fleet Drivers
Abstract
This paper investigates the role of human factors, in particular driving behaviour, in managing fleet fuel consumption. The results presented are from the Driving Simulator Eco-Drive Training (DSET) project, a collaborative project with a municipal fleet in Ontario, Canada, to test the effectiveness of simulator-based training in eco-driving techniques. Likert-scale questionnaires assessed the drivers’ self-reported motivation toward the training programme. In the six weeks following their simulator training session, we found that the average rate of hard acceleration (≥1.5 m/s²) among all acceleration events decreased from 13.3 to 11.4 % and the average rate of hard deceleration (≤−1.5 m/s²) decreased from 10.5 to 9.8 %. Based on these reductions in average acceleration and deceleration, approximately 18 % savings in fuel consumption is possible.
The fuel efficiency of the transportation sector has become a key factor to reduce greenhouse gas emissions and fuel consumption in response to the negative impacts of global warming. As an approach to energy saving and environmental sustainability, eco-driving has attracted considerable research interest in the past decades. This review aims to provide a comprehensive review of the research on eco-driving using methodologies of literature bibliometrics and content analysis through VOSviewer software. The following keywords “ecological-driving”“, ecological-routing”“, ecological-bus”“, ecological-car”“, ecological-vehicle”“, eco-driving”“, eco-routing”“, eco-driver”“, eco-bus”“, eco-car” and “eco-vehicle” are used for paper retrieval. The query was conducted on January 20, 2021. The results take account of all journal articles, proceedings papers, and reviews without time limitation. Finally, a total of 767 documents were retrieved as total publications, which were viewed over the period 2001–2020 based on the Web of Science (WoS) Core Collection database. The publication year, leading countries, leading sources, leading institutions, leading authors, document citation, and document co-citation were analyzed to explore the primary trends. The In-depth analysis reveals five clusters of keywords, and the review of relevant studies on eco-driving from five different perspectives is carried out to identify potential trends and future research hot spots of eco-driving.
This work investigates the commuting habits of young (18–24 year old) drivers, in Duluth, Minnesota (MN), with the goal of encouraging sustainable commuting. Sustainable commuting modes are cleaner, cheaper and healthier. Encouraging younger drivers to increase their use of public transportation would contribute to a more sustainable use of transportation infrastructures. Transportation infrastructure asset management is the allocation of available funds across infrastructure classes (e.g. pavements, bridges, signs) or programs (e.g., maintenance, construction). Sustainable commuting attempts to encourage the more efficient and sustainable use of transportation infrastructures. By focussing on younger drivers, it is hypothesized that early adoption of sustainable habits will lead to long-term sustainable habits. The research questions becomes how to encourage young drivers to reduce their dependence on personal vehicles? This work presents the results of a survey of 370 18–24 year old drivers from the University of Minnesota Duluth (UMD) campus to understand their commuting habits and problems with the current bus system. Approximately 46% of UMD students choose their personal vehicle as their primary commuting option. Among these students, 24% cite “inconvenience of the bus schedule” as the reason that prevents them from riding the bus more often even when using the bus can save them more than $2,000 per semester in vehicle maintenance costs. It seems as though poor marketing of bus routes and lack of education toward using the bus system has led to efficient routes being cancelled and lack of ridership.
The Driver Behaviour Questionnaire (DBQ) has mainly been used as a predictor of self-reported road traffic accidents. The associations between crashes and the violation and error factors of the DBQ, however, may be spuriously high due to reporting bias. In the present study, the DBQ was tested as a predictor of self-reported and recorded accidents in four samples of private and professional drivers. The findings show that the DBQ scale only predicts self-reported accidents, not recorded crashes, despite the higher validity of company data and the higher means of the recorded data across these samples. The results can be explained by a common method variance bias. In a review of the DBQ research, the use of the instrument was found to be heterogeneous concerning the number of items, scales used and factor analytic methods applied. Thus, the DBQ may not be as homogeneous and as successful in predicting accidents as is often claimed.
The effects of training in fuel-efficient driving for bus drivers in a city environment were evaluated. Three dependent variables, hypothetically associated with such training, were used; fuel and accident data from the bus company, and driver acceleration behavior from five buses, over time periods of several years. Effects of temperature and number of passengers on fuel consumption were held constant. Fuelling and acceleration data yielded fairly similar results. It was found that, although the effects on these variables during training were very strong (as found in a previous study), these did not transfer well into the drivers’ working situation. Overall, the effect was about two percent fuel consumption reduction as a mean over 12 months after training. No effect was found for accidents, although a two percent reduction would not have been detectable. In a second phase of the study, 28 buses were equipped with Econen feedback equipment, which give an indication on how much fuel is used concurrently, resulting in a further reduction of consumption of about two percent.
In this paper the long-term impact of an eco-driving training course is evaluated by monitoring driving behavior and fuel consumption for several months before and after the course. Cars were equipped with an on-board logging device that records the position and speed of the vehicle using GPS tracking as well as real time as electronic engine data extracted from the controller area network (CAN). The data includes mileage, number of revolutions per minute, position of the accelerator pedal, and instantaneous fuel consumption. It was gathered over a period of 10 months for 10 drivers during real-life conditions thus enabling an individual drive style analysis. The average fuel consumption four months after the course fell by 5.8%. Most drivers showed an immediate improvement in fuel consumption that was stable over time, but some tended to fall back into their original driving habits.
Economical, ecological and safe driving (eco-driving) is aimed at reducing fuel consumption, greenhouse gas emissions and accidents. Eco-driving is concerned about driving in a way compatible with modern engine technology: smart, smooth and safe techniques that lead to potential fuel savings of 10–15%. The Centre for Renewable Energy Sources of Greece conducted an eco-driving pilot study in collaboration with the Organization of Urban Transportation of Athens, and the Thermo-Bus Company to assess the effects of changing urban bus drivers' driving style.
Surface transportation consumes a vast quantity of fuel and accounts for about a third of the US CO2 emissions. In addition to the use of more fuel-efficient vehicles and carbon-neutral alternative fuels, fuel consumption and CO2 emissions can be lowered through a variety of strategies that reduce congestion, smooth traffic flow, and reduce excessive vehicle speeds. Eco-driving is one such strategy. It typically consists of changing a person’s driving behavior by providing general static advice to the driver (e.g. do not accelerate too quickly, reduce speeds, etc.). In this study, we investigate the concept of dynamic eco-driving, where advice is given in real-time to drivers changing traffic conditions in the vehicle’s vicinity. This dynamic strategy takes advantage of real-time traffic sensing and telematics, allowing for a traffic management system to monitor traffic speed, density, and flow, and then communicates advice in real-time back to the vehicles. By providing dynamic advice to drivers, approximately 10–20% in fuel savings and lower CO2 emissions are possible without a significant increase in travel time. Based on simulations, it was found that in general, higher percentage reductions in fuel consumption and CO2 emission occur during severe compared to less congested scenarios. Real-world experiments have also been carried out, showing similar reductions but to a slightly smaller degree.
This study is aimed at finding independent measures to describe the dimensions of urban driving patterns and to investigate which properties have main effect on emissions and fuel-use. 62 driving pattern parameters were calculated for each of 19 230 driving patterns collected in real traffic. These included traditional driving pattern parameters of speed and acceleration and new parameters of engine speed and gear-changing behaviour. By using factorial analysis the initial 62 parameters were reduced to 16 independent driving pattern factors. Fuel-use and emission factors were estimated for a subset of 5217 cases using two different mechanistic instantaneous emission models. Regression analysis on the relation between driving pattern factors and fuel-use and emission factors showed that nine of the driving pattern factors had considerable environmental effects. Four of these are associated with different aspects of power demand and acceleration, three describe aspects of gear-changing behaviour and two factors describe the effect of certain speed intervals.
Driving patterns (i.e., speed, acceleration and choice of gears) influence exhaust emissions and fuel consumption. The aim here is to obtain a better understanding of the variables that affect driving patterns, by determining the extent they are influenced by street characteristics and/or driver-car categories. A data set of over 14,000 driving patterns registered in actual traffic is used. The relationship between driving patterns and recorded variables is analysed. The most complete effect is found for the variables describing the street environment: occurrence and density of junctions controlled by traffic lights, speed limit, street function and type of neighbourhood. A fairly large effect is found for car performance, expressed in terms of the power-to-mass ratio. For elderly drivers, the average speed systematically decreases for all street types and stop time systematically increases on arterials. The results have implications for the assessment of environmental effects through appropriate street categorisation in emission models, as well as the possible reduction of environmental effects through better traffic planning and management, driver education and car design.
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.
The behavioral validation of an advanced driving simulator for its use in evaluating speeding countermeasures was performed for mean speed. Using mature drivers, 24 participants drove an instrumented car and 20 participants drove the simulator in two separate experiments. Participants drove on roads which contained transverse rumble strips at three sites, as well as three equivalent control sites. The three pairs of sites involved deceleration, and were the approaches to stop sign intersections, right curves, and left curves. Numerical correspondence (absolute validity), relative correspondence (or validity), and interactive (or dynamic) relative validity were analyzed, the latter using correlations developed from canonical correlation. Participants reacted to the rumble strips, in relation to their deceleration pattern on the control road, in very similar ways in both the instrumented car and simulator experiments, establishing the relative validities. However, participants generally drove faster in the instrumented car than the simulator, resulting in absolute validity not being established.
To validate a laboratory-based driving simulator in measuring on-road driving performance, 129 older adult drivers were assessed with both the simulator and an on-road test. The driving performance of the participants was gauged by appropriate and reliable age-specific assessment criteria, which were found to be negatively correlated with age. Using principal component analysis, two performance indices were developed from the criteria to represent the overall performance in simulated driving and the on-road assessment. There was significant positive association between the two indices, with the simulated driving performance index explaining over two-thirds of the variability of the on-road driving performance index, after adjustment for age and gender of the drivers. The results supported the validity of the driving simulator and it is a safer and more economical method than the on-road testing to assess the driving performance of older adult drivers.
Denver’s driving change program reduces vehicular CO2 emissions
- Enviance
Enviance. (2009). Denver's driving change program reduces vehicular CO 2 emissions [Online]
Available
at:
http://www.enviance.com/about-enviance/PressReleaseView.aspx?id=53.
The role of a driving simulator in driver training to improve fuel economy
- H Scott
- M Knowles
- A Morris
- D Kok
Scott, H., Knowles, M., Morris, A., & Kok, D. (2012). The role of a driving simulator in driver
training to improve fuel economy. In Driving Simulation Conference, Paris.