Ecodriving acceptance: An experimental study on ...

Transportation Research Part F 22 (2014) 249–260

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Transportation Research Part F journal homepage: www.elsevier.com/locate/trf

Ecodriving acceptance: An experimental study on anticipation behavior of truck drivers R. Thijssen a,⇑, T. Hofman a, J. Ham b a b

Department of Mechanical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands Department of Human-Technology Interaction, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands

a r t i c l e

i n f o

Article history: Received 12 June 2013 Received in revised form 26 November 2013 Accepted 12 December 2013

Keywords: Ecodriving Anticipation Acceptance Fuel savings Driver behavior Commercial trucks

a b s t r a c t In this paper, it is researched to what extend truck drivers are willing to improve their anticipation behavior. For the purpose of this research, anticipation behavior is characterized by anticipation distance: the distance to a stopping point (e.g. roundabout), at which the accelerator pedal is released. A larger anticipation distance yields a lower fuel consumption. The goal of this research was to reveal the potential anticipation improvement without exceeding driver’s acceptance. Therefore, the driver’s natural anticipation distance, and the acceptance of prescribed distance was measured. The effects of this improved behavior, in terms of saved fuel and additional trip time, are analyzed. The analysis suggested that improved anticipation behavior can save up to 98 grams of fuel per deceleration event. Finally, natural driving behavior on public roads was measured as a baseline. By projecting the potential anticipation improvements on this baseline measurement, a potential fuel consumption reduction of 9.5% at the cost of 4.6% additional trip time was found. Overall, the current research suggested that truck drivers are willing to improve their anticipation behavior, and that this improvement can lead to substantial fuel consumption reduction. Furthermore, it was found that the potential fuel savings are often limited by visibility. This suggests a potential for GPS-based driver support systems which can help the driver to enhance their anticipation behavior even further. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction There is an ongoing trend to reduce the consumption of fossil fuels (Lanza & Verdolini, 2011). The most common motivations for this development are: to reduce the negative environmental impact of fossil fuels (Solomon et al., 2007); to decrease the dependency on oil and oil-producing countries (European Parliament & Council, 2003); and, to reduce fuel costs (Goodman, 2008). As for vehicle fuel consumption, such a reduction can be accomplished by enhancing any of the following three aspects of road transport (van Mierlo, Maggetto, van de Burgwal, & Gense, 2004), as is also shown in Fig. 1: Traffic infrastructure. By the construction of roundabouts or phased traffic lights to reduce vehicle idling time, or by lowering speed limits to reduce noise and air pollution, fuel consumption can be reduced. While most of such measures for road infrastructure are taken to increase traffic flow or reduce pollution, many of these also have a positive effect on fuel efficiency.

⇑ Corresponding author. Tel.: +31 6 496 583 43. E-mail addresses: [email protected] (R. Thijssen), [email protected] (T. Hofman), [email protected] (J. Ham). 1369-8478/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trf.2013.12.015

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R. Thijssen et al. / Transportation Research Part F 22 (2014) 249–260

1. 2. 3.

1. Traffic infrastructure 2. Vehicle technology 3. Driver behavior

• • •

Acceleration Cruising Braking/anticipation

Fig. 1. Three categories for fuel efficiency improvement. This research focusses on the anticipation aspect of driver behavior.

Vehicle technology. Car manufacturers focus largely on reducing CO2 emissions. This is done to comply with regulations (e.g. the European ‘‘Euro’’ emission standard (European Parliament & Council, 2007)) by, e.g., improving engine efficiency, developing hybrid drivetrains, or reducing vehicle weight. Generally, such measures also have a positive effect on the fuel efficiency. Driver behavior. Despite car manufacturer’s efforts, the mileage of a very fuel efficient vehicle can be lowered significantly if it is badly driven. Large improvements can be made by instructing drivers how to drive their vehicles fuel efficiently. It is required though, that drivers are willing to change their behavior. This research focusses on driver behavior as it is an effective method to reduce fuel consumption (Barkenbus, 2010). Driving can be subdivided into the following three stages: (i) acceleration, (ii) cruising, and (iii) braking. During each stage, different factors influence fuel consumption. During acceleration, the upshift behavior is of importance. A higher gear results in less engine revolutions per unit of driven distance, so less energy is dissipated by engine drag factors (e.g. air intake losses, camshaft timing, and turbo properties). Hence, during acceleration, fuel can be saved by upshifts at low engine speeds (Ngo et al., 2012). For cruising, two phenomena are of importance: engine drag and air drag. Due to the aforementioned gear selection effect, a lower velocity yields a higher engine drag due to the lower gear. At higher speeds, the air drag becomes significant due to its exponential relationship with vehicle speed. A fuel economy optimum is found between these effects, at 65–80 km/h for passenger cars (An & Ross, 1993). When braking, the kinetic energy of the vehicle, which was generated by the engine, is converted into heat by using friction brakes (passenger cars), and/or engine and transmission brakes (trucks). By releasing the accelerator pedal, the fuel supply to the engine is cut off, and no fuel is consumed. Hence, by looking further ahead, and releasing the accelerator pedal earlier, brake usage and fuel consumption decrease. To put these theories for decreasing fuel consumption into practice, general guidelines for drivers exist (Instituut voor Duurzame Mobiliteit, 2012). Fuel consumption can be reduced by: (i) accelerating moderately with upshifts between 2000 and 2500 rpm; (ii) anticipating traffic flow and signals and thereby avoiding sudden decelerations and stops; (iii) maintaining an even driving pace; (iv) driving at or below the speed limit; and, (v) shutting down the engine during short stops. These measures are commonly referred to as ‘Ecodriving’. Fuel savings of 5–26% are measured in studies with various types of vehicles, drivers, and countries, in which drivers employ Ecodriving strategies (van der Voort & van Maarseveen, 2001; Symmons & Rose, 2008; Forum, 2007; Barkenbus, 2010; Gonder, Earleywine, & Sparks, 2012; Ford Motor Company, 2008; Enviance, Inc.,, 2009; Takada, Ueki, Saito, Sawazu, & Nagatomi, 2007; Energy & Environmental Analysis, Inc.,, 2001). According to these measurements, Ecodriving appears to be effective for reducing fuel consumption. Such driving behavior can be stimulated by Ecodriving training and/or by Driver support systems, which is discussed next. 1.1. Ecodriving training Ecodriving tips are spread through governmental, commercial, or private initiatives. For instance, the Dutch government has launched television and billboard campaigns to promote Ecodriving (Instituut voor Duurzame Mobiliteit, 2012). Also, a multitude of companies specializes in Ecodriving workshops, classroom training, or in-vehicle training (Stichting EcoDriving Nederland, 2013). Finally, websites exist where drivers exchange Ecodriving tips, experiences, and fuel savings achievements (Hypermiler, 2012). Most of the aforementioned training methods require the driver to be proactive. 1.2. Driver support systems Although the aforementioned savings numbers of 5 to 26% are promising, most of these results are measured directly after Ecodriving training. The long term effects of Ecodriving training are less significant, as savings numbers decrease to 5–10% within three years, or to 2–3% after three years (Forum, 2007). It has also been reported that some drivers tend to fall back into their original driving habbits (Beusen et al., 2009). Driver support systems could play a significant role in main-


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taining the beneficial habit of Ecodriving, and thereby preventing a potential decrease in fuel savings. In recent years, many in-vehicle technologies are introduced aimed at assisting drivers to attain and maintain an economic driving style. A comprehensive list of existing products can be found in A. These technologies can be subdivided into three categories based on the type of feedback. Real-time feedback. These are systems which give instant feedback based on current operating conditions. Examples of such systems are shift-advisors, Miles-per-Gallon gauges, acceleration/deceleration feedback, and active accelerator pedals. Short-term feedback. Systems in this category provide feedback on a relatively short period of time. This enables drivers to track their average performance during the drive. This category includes systems which use symbols or colors to indicate fuel efficiency, and systems which give a score for various aspects of driving style. Long-term feedback. These systems give feedback on longer periods of time. This allows drivers to track their long-term progress. However, it does not allow drivers to notice the direct effects of a change in driving style. This category includes systems which present long-term fuel consumption trends, give long-term feedback using symbols or scoring, or systems which log data which can be analyzed afterwards on a personal computer. Little knowledge is available on the effect of the type of feedback on fuel efficiency. 1.3. Commercial vehicles This research focusses on the anticipation behavior of truck drivers. Anticipation behavior is characterized by the distance to a stopping point, at which the driver releases the accelerator pedal. For the purpose of this study, this distance is defined as the anticipation distance (xa ). By increasing xa , i.e. an earlier fuel cut-off, less fuel is consumed for the same traveled distance. Such behavior especially has a large savings potential for truck drivers, for the following reasons: (i) the fuel consumption of trucks is significantly higher than for cars; (ii) trucks and their drivers drive large distances; and, (iii) the inertia of a truck is large, resulting in potentially large coasting distances. However, the benefits of Ecodriving training or Driver support systems are less straightforward for trucks, for the following reasons: (i) Ecodriving can increase trip time, resulting in additional costs; (ii) Ecodriving might be enforced by the fleet owner, forcing drivers to adopt inconvenient behavior; and, (iii) Ecodriving might not have an effect on the driver’s own financial situation, decreasing the motivation for adopting such behavior. Hence, two parties with different interests can be involved if fuel should be saved by enhanced anticipation behavior of truck drivers, i.e., the driver and the fleet owner: 1. Interest of the driver. The driver’s comfort should not be compromised by demanding unacceptable anticipation behavior. Therefore the relationship between anticipation behavior and driver acceptance should be measured. 2. Interest of the fleet owner. The driver’s enhanced anticipation behavior should be economically beneficial. Hence, the savings due to reduced fuel consumption should outweigh the additional costs due to possible increased trip time. 1.4. Research question and objectives Related to the interests of the driver and fleet owner, the following research questions are defined: 1. To what extend can the anticipation behavior of truck drivers be enhanced, without exceeding driver’s acceptance limits? 2. How much fuel can be saved by such improvement? 3. What are the consequences for trip time? To find an answer to research question 1, the following data is required: drivers’ natural anticipation behavior; and, the most fuel economic behavior that is still acceptable for these drivers. The difference between these numbers is a measure for potential improvement. This leads to the following objectives: (i) measurement of natural anticipation behavior for common traffic situations; and, (ii) measurement of the acceptance of alternative anticipation behavior. To be able to answer research questions 2 and 3, natural driving behavior measurements on public roads are needed. Based on such a data set, the potential amount of saved fuel and corresponding additional trip time, due to enhanced anticipation behavior, can be calculated. This yields the following objectives: (iii) measurement of natural driving behavior on public roads; and, (iv) calculation of the potential fuel savings, and the corresponding increase in trip time, due to enhanced anticipation behavior. 1.5. Contributions and outline This research has two main contributions. Firstly, the measurements of driver’s natural anticipation behavior and driver’s acceptance of alternative anticipation behavior, provide an essential data-set for the implementation of a driver support system that gives feedback on anticipation behavior. Secondly, the calculation of the potential fuel savings and corresponding

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increase in trip time, resulting from improved anticipation behavior, is essential for determining if such a change is economically beneficial. Research question 1 is answered in Section 2, research questions 2 and 3 are treated in Section 3. Finally, the research is concluded in Section 4. 2. Acceptance of prescribed anticipation behavior In this section an experiment is described, that was designed to measure drivers’ natural anticipation behavior and the acceptance of prescribed anticipation behavior. The used method, results and corresponding conclusions are discussed. 2.1. Method 2.1.1. Participants and apparatus Eight experienced test truck-drivers (8 male and 0 female; age M ¼ 36:8 years, SD ¼ 11:0) participated in this experiment. The test-vehicle driven by these drivers was a 460hp DAF XF105 truck, that weighed 19 tonnes including load. The vehicle was equipped with a data logging system that logged all available CAN-bus signals. The experiments were held at a vehicle proving ground. An overview of the set-up on this 2 km oval test-track is depicted in Fig. 2. The experiment consisted of two parts: measurement of natural behavior; and, measurement of prescribed behavior acceptance. 2.1.2. Measurements Firstly, the natural anticipation behavior was measured. During each lap, the driver was given one of the following deceleration assignments: (i) 80–0; (ii) 50–0; or, (iii) 80–50 km/h. These intervals represent the most common speed changes, based on the legal speed limits for trucks on the Dutch public roads: 50 km/h in urban zones, and 80 km/h in rural/highway zones. A lap started at the ‘stop’-sign, where the driver was asked to accelerate to the initial speed of the given speed interval, and to maintain that pace. Furthermore, the driver was told to arrive at the ‘stop’-sign at the given final speed (50 or 0 km/h). Hence, each lap consisted of one deceleration event, in which the driver anticipated and decelerated according to their own judgements. The natural anticipation distance (xa ), defined by Definition 1, was extracted from the CAN-bus data afterwards. Definition 1. Anticipation distance xa : the distance to a stopping point, at which the driver releases the accelerator pedal. An example of a lap is depicted in Fig. 3. Each driver repeated the aforementioned procedure three times for each of the three deceleration intervals. This experiment resulted in 72 measurements of drivers’ natural anticipation behavior. Secondly, the acceptance of prescribed anticipation behavior was measured. In this experiment, the driver was again asked to attain and maintain a given speed (50 or 80 km/h). However, instead of anticipating naturally, the driver now had to release the accelerator pedal at a given location, indicated by a cone number on the test track. Cones were set out at 100 m intervals from the ‘stop’-sign as shown in Fig. 2. After releasing the accelerator pedal, it was not allowed to use the accelerator pedal. Again, the driver had to arrive at the ‘stop’-sign at a prescribed final speed (50 or 0 km/h), only by coasting and braking. Hence, the anticipation distance xa was prescribed in this experiment. After each deceleration event, the experiment leader, who was seated in the passenger seat, asked the driver the following two questions to measure their acceptance: How willing are you to apply such behavior on an empty road? How willing are you to apply such behavior on a road with normal traffic around you?

Fig. 2. Overview of the test track set-up. Cones are set out at 100 m intervals towards the stop-sign. Anticipation distance xa is the distance between the cone where the driver releases the accelerator pedal, and the stop-sign.


Velocity [km/h]

Acceleration & cruising phase 80

Coasting phase Braking phase

50

Driver releases accelerator pedal

0

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0

500

1000

Anticipation distance (x a) 1500

2000

Travelled distance [m] Fig. 3. Example of a lap with the following deceleration assignment for the driver: 80–0 km/h. The lap begins and ends at the same position at the ‘stop’sign. The anticipation distance xa is extracted from the CAN-bus data afterwards.

The participants had to answer these questions by telling the experimenter which option they chose on a 1 to 10 rating scale. To guarantee a similar usage of the scale amongst the drivers, four important scale anchors were explained to the driver as indicated in Table 1. The answers were recorded by the experimenter. The aforementioned method to measure acceptance was applied to the following speed intervals: 80–0; 50–0; and, 80– 50 km/h. The prescribed anticipation distances ranged from 100 up to 1600 meters. A total of 22 acceptance measurements were done per driver, resulting in a total set of 176 measurements. 2.2. Results The measured natural anticipation behavior and the acceptance of prescribed anticipation behavior, for the different speed intervals, are depicted in Fig. 4 (from top to bottom: 80–0; 50–0; 80–50 km/h). The horizonal axes represent the anticipation distance. A perfect coast-down, i.e. deceleration by only releasing the accelerator pedal without using brakes, occurred at 1400, 500 and 800 meters anticipation distance for 80–0, 50–0, and 80– 50 km/h, respectively. The vertical axes represent the acceptance on a 1 to 10 rating scale. The dotted vertical line represents the average natural anticipation distance, which is denoted as xa . The dark gray lines in these figures represent the acceptance of prescribed anticipation behavior; the circles represent the average acceptance, denoted as xa , and the error bars represent the standard deviation, denoted as rxa . The horizontal (light gray colored) dashed line represents the acceptance threshold value, distinguishing acceptable (P6) from unacceptable behavior (