Green Eco-Driving Effects in Non-Congested Cities
- Strategic decisions: vehicle selection and maintenance.
- Operational decisions: driving style geared to maintaining a constant speed, smooth acceleration, etc.
- Tactical decisions: route selection and vehicle load.
- Eco-drivers influence not only their own vehicle driving characteristics but also the surrounding vehicles and thus the traffic flow in the corridor.
- The effects of eco-driving are not the same on a local road as on an urban motorway and also depend on the traffic volumes.
- Eco-driving behaviour could produce more congestion; driving smoothly increases distances among cars, reducing density of road use. Therefore, when traffic flow increases, eco-driving could reduce the number of car per road section, increasing congestion accordingly.
- Campaigns were organised to collect data on driver behaviour following routes with different types of city roads and traffic conditions. Four itineraries were selected with different levels of service (LOS). Level of Service is a quality measure describing operational conditions within a traffic stream; these conditions affect to speed and travel time, freedom to maneuver, traffic interruptions, and comfort and convenience. The test was first performed with drivers driving normally, and then a second set of car runs was recorded after they had taken an eco-driving course, following the same routes and at the same times of day.
- Individual driving variations produced by eco-driving were measured using an OBD-key (KBM Systems Ltd., London, UK) installed on board (see Section 2.5), the corresponding CO2 emissions savings were estimated also.
- The combined effects of route selection and eco-driving were calculated for the different types of urban road in the test city as a proxy for eco-routing.
- Drivers were surveyed to capture changes in their way of driving and perceptions once they were trained in eco-driving techniques.
2.1. Case Study: City of Caceres, Spain
2.2. Selecting Routes to Represent Different Road Types and Traffic Conditions
2.3. Driver Selection, Eco-Driving Training, and Assignment
- 1st period. Morning peak: 7:30–11:30 am
- 2nd period. Lunchtime: 12:00–4:00 pm
- 3rd period. Evening peak: 4:30–8:30 pm
2.4. Experimental Car Data Collection
2.5. Measured Variables
- GPS position (longitude and latitude) and distance travelled (km);
- Travel time (h);
- Instantaneous speed (km/h);
- Fuel consumption (L);
- Number of stops, rpm, acceleration, and deceleration (m/s2).
2.6. Survey to Capture Individual Driving Perceptions
3.1. Driver Behaviour
- There is no significant difference in driving difficulty before and after eco-driving training. More than 90% of drivers consider vehicle handling easy (scores 1 and 2). This implies that the eco-driving technique is easy to learn and practice.
- The driving environment has very little influence, although some drivers admit to having more difficulty in bad traffic and weather conditions. Here, 74% of drivers consider the driving circumstances to be easy (scores 1 and 2) before eco-driving, while only 66% do so after eco-driving. Some 5% of eco-drivers reported difficult driving conditions (scores 6 and 7).
- The drivers’ feelings are modified with efficient driving. Here, 74% feel entertained before eco-training (scores 6 and 7) vs. 59% after eco-driving, and 15% of drivers therefore consider eco-driving to be less entertaining.
- Finally, it is worth noting that eco-driving causes a 10% decrease in drivers’ relaxation during the trip. Here, 79% of drivers are relaxed (scores 1 and 2) before eco-driving, compared to 68% who are afterwards.
3.2. Fuel Consumption and CO2 Emissions
3.3. Travel Times
4. Conclusions and Policy Recommendations
Conflicts of Interest
Appendix A. Eco-Driving Course
Part 1—Theoretical (1 h) in a Group Class
Part 2—Practical (1 h) Individual
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|Gear shift type||Manual||Manual|
|Maximum authorized mass||2010||1305|
|Dimensions (LxWxH) (mm)||4419 × 1814 × 1510||3546 × 1627 × 1488|
|Classification by relative consumption|
|Vehicle||Total Km Travelled||Route 1 Local||Route 2 Collector||Route 3 Perimeter||Route 4 Bypass||Total||Men||Women|
|Parameters||Driving Mode||R1 Local||R2 Collector||R3 Perimeter||R4 Bypass|
|Max positive acceleration m/s2||Non-eco||2.31||2.07||2.25||2.66|
|Max negative acceleration m/s2||Non-eco||−2.35||−2.31||−2.54||−2.95|
|Average positive acceleration m/s2||Non-eco||0.35||0.36||0.44||0.54|
|Average negative acceleration m/s2||Non-eco||−0.41||−0.40||−0.48||−0.57|
|Average number of times driving below 5 km/h||Non-eco||6.54||4.97||4.97||3.01|
|Driving the vehicle was easy (1)—difficult (7).||Non-eco||72||20||-||4||4||-||-|
|The driving environment was easy (1)—difficult (7)||Non-eco||45||29||13||6||7||-||-|
|During the trip you were bored (1)—entertained (7)||Non-eco||-||1||3||7||13||34||40|
|During the trip you felt relaxed (1)—stressed (7)||Non-eco||52||27||10||4||6||-||1|
|R1 Local||R2 Collector||R3 Perimeter||R4 Bypass||R1 Local||R2 Collector||R3 Perimeter||R4 Bypass|
|Fiat (gasoline)||Fuel (L)||0.50||0.51||0.52||0.76||0.41||0.42||0.43||0.59|
|Fuel (L/100 km)||8.44||8.03||8.06||7.54||6.96||6.63||6.69||5.84|
|estimated CO2 (g)||1173.50||1196.97||1220.44||1783.72||962.27||985.74||1009.21||1384.73|
|Astra (diesel)||Fuel (L)||0.34||0.31||0.38||0.74||0.27||0.28||0.29||0.51|
|Fuel (L/100 km)||5.76||4.84||5.90||7.39||4.53||4.45||4.56||5.09|
|estimated CO2 (g)||903.72||823.98||1010.04||1966.92||717.66||744.24||770.82||1355.58|
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Coloma, J.F.; García, M.; Wang, Y.; Monzón, A. Green Eco-Driving Effects in Non-Congested Cities. Sustainability 2018, 10, 28. https://doi.org/10.3390/su10010028
Coloma JF, García M, Wang Y, Monzón A. Green Eco-Driving Effects in Non-Congested Cities. Sustainability. 2018; 10(1):28. https://doi.org/10.3390/su10010028Chicago/Turabian Style
Coloma, Juan Francisco, Marta García, Yang Wang, and Andrés Monzón. 2018. "Green Eco-Driving Effects in Non-Congested Cities" Sustainability 10, no. 1: 28. https://doi.org/10.3390/su10010028