Smooth driving: from eco-driving to automated vehicles
Intelligent Transport Systems (ITS) technologies can provide drivers with real-time feedback and directly control vehicle speed to improve traffic flow and reduce the incidence and severity of collisions. These technologies can also help reduce carbon emissions, especially when combined with speed management measures and reduce congestion.
Smooth driving applications are more or less market-ready. Commercially available applications include:
- Eco-driving systems involve either providing real-time feedback to the driver or partially controlling the vehicle's speed to save fuel and reduce emissions.
- Adaptive Cruise Control (ACC) automatically adjusts vehicle speed to maintain a safe distance from vehicles ahead.
- Eco Cruise Control (ECC) adjusts vehicle speed according to topography.
- Intelligent Speed Adaptation (ISA) automatically adjusts vehicle speed to stay within legal speed limits and alerts drivers in case of excess speed.
Applications that have not yet made it to market, either due to developmental technology or adequate policy frameworks:
- Collaborative ACC (CACC) uses vehicle-to-vehicle (V2V) communication to co-ordinate smoother driving on highways. Vehicles form platoons for long distances on motorways benefitting from co-ordination and aerodynamic efficiency.
- Eco Approach and Departure (EAD) broadcasts Signal Phase and Timing (SPaT) information to avoid idling and unnecessary acceleration at intersections.
- Autonomous Vehicles (AVs) that fully automate all of the above approaches.
The private sector is driving vehicle technology innovation with market opportunities leading to the adoption of many systems. Policy makers can stimulate technological adoption and shape its development. Authorities can determine whether applications like ISA are in the fully automated or partial advisory mode, for example. Policy makers also have a role in setting technical standards and regulations that define the functional requirements for AVs. They can develop methods to assess the safety of driving performance of automated driving systems, define technical specifications for vehicle-to-vehicle or vehicle-to-infrastructure communication, and ensure the interoperability of applications across different jurisdictions. Authorities can also strive to include incentives into regulatory developments that encourage a transition to vehicle technologies and forms of energy that reduce emissions. They can also promote algorithms that minimise emissions through more efficient driving.
Smooth driving measures lead to better energy efficiency, reducing tailpipe or upstream CO2 emissions per vehicle kilometre travelled. The size of efficiency gains depends on the general energy efficiency of the vehicles concerned. Therefore, the improvements will change over time with the uptake of LZEVs that have better energy efficiency levels.
The CO2 mitigation potential of smooth driving, and ITS measures in general, depends on limiting rebound effects like induced travel demand due to more efficient, less costly options. Furthermore, fully autonomous vehicles can increase private car use by more people and allow multitasking during trips. This can increase travel demand and adverse effects like congestion, unwanted land use changes and travel patterns, increased energy use and emissions. Significant reductions will also require significant changes in speed and congestion levels, which may be difficult to achieve in practice, given the mentioned potential rebound effects. When seeking to increase the uptake of ITS applications, policy makers should use demand management strategies to avert induced demand for driving and related adverse impacts.
In general, the identified CO2 mitigation potential of these technologies in the reviewed literature depends heavily on assumed framework conditions, like penetration rates and the fleet's energy efficiency. The literature often reveals large ranges of CO2 reduction potentials. The following estimates of CO2 tailpipe emissions reduction effects - based on field tests or simulations - only account for the direct impacts of the technologies on vehicle operations. They exclude any indirect effects like increased travel activity due to lower vehicle operation costs, enhanced driving experience or other:
- Eco-driving: real-time feedback
- Field Test
- 5-15% on urban roads in various contexts (Pandazis, 2015)
- 1.4% in California, United States (Martin, 2013)
- Simulation
- 5-10% on moderately driven trips; assuming intelligent driver feedback (Gonder, 2012)
- 10% (Kamal, 2009; Klunder, 2009)
- Field Test
- Eco-driving: automated driving
- Simulation
- 5-7% for transit (Xu, 2016)
- 5% with infrastructure-to-vehicle communication, without compromising travel time and under the assumption of 100 % penetration rate in the vehicle fleet (Barth, 2015)
- Simulation
- ACC
- Field Test
- 3% in various contexts (Klunder, 2009; Faber, 2012; Pandazis, 2015)
- Field Test
- ECC
- Field Test
- 3% average fuel economy improvements across several field tests (Rakha, 2013)
- Field Test
- ISA
- Simulation, assuming a speed reduction from 120 to 110 km/h
- 10-15% assuming 100% ISA penetration (and hence 100% compliance) (derived from EEA, 2020)
- Simulation, assuming a speed reduction from 120 to 110 km/h
- CACC
- Field Test
- 37% at signalised intersections, fully automated eco-CACC system, compared to an uninformed driver (Chen, 2017a)
- Up to 16% for a truck platooning at 5m distance behind another truck (or 8% at 15m distance) (Jootel, 2012)
- Simulation
- Up to 19% on a real-world motorway with on- and off-ramps (AERIS, 2016)
- 1-18% with 20-100% penetration (Yelchuru, 2015)
- 6% with platooning (Klunder, 2009)
- Field Test
- EAD at intersections
- Field Tests at research facilities in the United States
- 6% (Hao, 2017)
- 14% at a signalised intersection (Xia, 2012)
- Simulation
- 5-10% (AERIS, 2016)
- 2-8% with 20-100% penetration (Yelchuru, 2015)
- Field Tests at research facilities in the United States
- AVs
- Simulation
- CO2 increases of 1% (if AVs are connected to other vehicles and infrastructure) to 3% (if AVs are not connected) (Makridis et al., 2020). The micro-simulation shows that while AVs improve the energy efficiency of single-vehicle operations thanks to less aggressive driving, they have a negative impact on traffic flow overall; they lead to saturated networks more quickly than conventional vehicles, which leads to overall emission increases.
- Simulation
The cost of eco-driving ITS applications vary. Some other applications have seen costs decline sharply, like advanced cruise-control technologies, increasingly integrated by default.
- Eco-driving
- A Dutch communications campaign cost EUR 10 per ton CO2 mitigated (ITF, 2008).
- Real-time driver feedback displays are becoming standard features.
- ISA
- In widespread commercial use worldwide for speed alert configuration.
- Compulsory in new vehicles sold in the EU from 2022. The cost of the switch from alert to control mode is negligible.
- ACC
- Initially (low volume) estimated at USD 3 000 per vehicle in 2006 (Peirce, 2006).
- Priced as low as USD 300 in 2015, already (Atiyeh, 2015).
- CACC
- Similar to ACC, but subject to the availability of a regulatory framework on vehicle-to-infrastructure (V2X) communication (Shladover, 2018).
- EAD (SPaT broadcasting infrastructure)
- USD 8 600 per roadside unit
- USD 3 500 per intersection
- USD 1 000 per onboard unit (Georgia DOT, 2020)
Where smooth driving improves traffic flow energy efficiency overall, it can reduce local air pollution (for non-zero emission vehicles), reduce travel times and increase network capacity. These improvements, especially with regards to travel times and network capacity, will be limited and likely short-lived in practice, due to discussed rebound effects.
Autonomous vehicles may increase vehicle activity due to both induced demand and a potential diversion from low-carbon modes. Intermediate technologies could have similar effects. Increased vehicle automation should go hand-in-hand with demand management measures to reduce adverse rebound effects. Demand management helps keep travel activity in check and ensure it is covered by the most efficient modes of transport. The transition to LZEVs reliant on low-emission energy pathways should also accompany smooth driving measures.
Variable Speed Limits (VSL) attempt to moderate traffic via roadside displays without in-vehicle devices. Research is not conclusive as to whether VSL effectively reduces emissions.
ITF (2021) Transport Climate Action Directory – Smooth driving: from eco-driving to automated vehicles
https://www.itf-oecd.org/policy/smooth-driving-eco-driving-automated-veh...
AERIS (2016) Applications for the Environment: Real-Time Information Synthesis (AERIS) Capstone Report: 2009 to 2014 Executive Summary, https://www.its.dot.gov/research_archives/aeris/pdf/AERIS_Capstone_ExecSummary.pdf
AERIS (2013) Eco-Approach and Departure at Signalized Intersections: Preliminary Modeling Results, https://www.its.dot.gov/research_archives/aeris/pdf/UCR_eco-approach_final2.pdf
Altan O. D. et al. (2017) GlidePath: Eco-Friendly Automated Approach and Departure at Signalized Intersections, https://ieeexplore.ieee.org/abstract/document/8095005
Atiyeh C. (2015) Come on Down: Lexus Discounting Driver-Assist Safety Options, https://www.itscosts.its.dot.gov/its/benecost.nsf/ID/20E56F7BF807D92D8525813E005C29D7?OpenDocument&Query=Home
Barth M. et al. (2011) Dynamic ECO-driving for arterial corridors, https://ieeexplore.ieee.org/abstract/document/5973594
Barth M. and Boriboonsomsin K. (2009) Energy and emissions impacts of a freeway-based dynamic eco-driving system, https://www.sciencedirect.com/science/article/pii/S1361920909000121
Barth M., Wu G. and Boriboonsomsin K. (2015) Intelligent Transportation Systems and Greenhouse Gas Reductions, https://link.springer.com/article/10.1007/s40518-015-0032-y
Chen H. et al. (2017) Field Implementation of an Eco-Cooperative Adaptive Cruise Control System at Signalized Intersections, https://trid.trb.org/view/1438903
Chen P. et al. (2017) Dynamic eco-driving speed guidance at signalised intersections: Multivehicle driving simulator based experimental study, https://trid.trb.org/view/1515398
European Environment Agency (2020) Do lower speed limits on motorways reduce fuel consumption and pollutant emissions?, https://www.eea.europa.eu/themes/transport/speed-limits-fuel-consumption-and
Faber F. (2012) European Large-Scale Field Operational Tests on In-Vehicle Systems: Final results - Impacts on traffic efficiency and environment, https://www.eurofot-ip.eu/download/library/deliverables/eurofotsp620121121v11dld65d66_final_results_impacts_on_traffic_efficiency_and_environment.pdf
Georgia DOT (2020) Lessons Learned from Comprehensive SPaT Deployment and Future Plans, https://www.itscosts.its.dot.gov/its/benecost.nsf/ID/087BB15032F3188D85258519006723DD?OpenDocument&Query=Home
Gonder J., Earleywine M., and Sparks W. (2012) Analysing Vehicle Fuel Saving Opportunities through Intelligent Driver Feedback, https://www.sae.org/publications/technical-papers/content/2012-01-0494/
Hao P., Wu G. and Boriboonsomsin K. (2017) Eco-Approach and Departure (EAD) Application for Actuated Signals in Real-World Traffic, https://ieeexplore.ieee.org/abstract/document/8319909
ITF (2008) Ecodriving: More than a drop in the ocean?, https://oecdobserver.org/news/fullstory.php/aid/2596/Ecodriving:_More_than_a_drop_in_the_ocean_.html
Jin Q. et al. (2013) Platoon-based multi-agent intersection management for connected vehicle, https://ieeexplore.ieee.org/document/6728436?reload=true&arnumber=6728436
Jootel P. S. (2012) Safe Road Trains for the Environment, https://www.itsbenefits.its.dot.gov/its/benecost.nsf/ID/FB9643671F15740085257BBA00631A44?OpenDocument&Query=Home
Kamal M.A.S. et.al. (2009) Development of Ecological Driving Assist System: Model Predictive Approach in Vehicle Control, https://trid.trb.org/view/908442
Kamalanathsharma R. (2014) Ecodriving in the Vicinity of Roadway Intersections - Algorithmic Development, Modeling, and Testing, https://vtechworks.lib.vt.edu/handle/10919/56987
Khondaker B. and Kattan L. (2014) Variable speed limit: an overview, https://www.tandfonline.com/doi/full/10.1179/1942787514Y.0000000053?casa_token=ifeU0hY0ZWQAAAAA%3Aa1sfblznI--sYV-hzE24uQoTFEipj8TaOmvsSJXr6xhGSgYq5vE-RJVyZG0icOC9xCKC9JgTUA
Klunder G. A. (2009) Impact of Information and Communication Technologies on Energy Efficiency in Road Transport: Final Report, https://www.narcis.nl/publication/RecordID/oai:tudelft.nl:uuid:2a2c6c59-0ddd-4a93-91b2-0ca7d363918c
Li L., Gan J. and Li W. (2018) A Separation Strategy for Connected and Automated Vehicles: Utilising Traffic Light Information for Reducing Idling at Red Lights and Improving Fuel Economy, https://trid.trb.org/view/1530721
Makridis M. et al. (2020) The impact of automation and connectivity on traffic flow and CO2 emissions. A detailed microsimulation study, https://doi.org/10.1016/j.atmosenv.2020.117399
Martin E. W. et al. (2013) Dynamic ecodriving in northern california: A study of survey and vehicle operations data from an ecodriving feedback device, http://innovativemobility.org/wp-content/uploads/2015/08/Dynamic-Ecodriving-in-Northern-California_A-Study-of-Survey-and-Vehicle-Operations-Data-from-an-Ecodriving-Feedback-Device.pdf
Pandazis J.-C. and Winder A. (2015) Study of Intelligent Transport Systems for reducing CO2 emissions for passenger cars, https://erticonetwork.com/wp-content/uploads/2015/09/ITS4rCO2-Report-Final-2015-09-10-submitted.pdf
Peirce S. and Lappin J. (2006) Private Sector Deployment of Intelligent Transportation Systems: Current Status and Trends, https://rosap.ntl.bts.gov/view/dot/4313
Rakha H. A., Ahn K. and Park S. (2013) Predictive Eco-Cruise Control (ECC) System: Model Development, Modeling, and Potential Benefits, https://trid.trb.org/view/1245471
Shladover S. E. et al. (2018) Cooperative Adaptive Cruise Control (CACC) For Partially Automated Truck Platooning, https://dot.ca.gov/-/media/dot-media/programs/research-innovation-system-information/documents/final-reports/ca18-2623-finalreport-a11y.pdf
Tientrakool P., Ho Y.-C., and Maxemchuk N. F. (2011) Highway Capacity Benefits from Using Vehicle-to-Vehicle Communication and Sensors for Collision Avoidance, https://ieeexplore.ieee.org/document/6093130?arnumber=6093130
Xia H. et al. (2012) Field operational testing of ECO-approach technology at a fixed-time signalised intersection, https://ieeexplore.ieee.org/abstract/document/6338888
Xia H., Boriboonsomsin K. and Barth M. (2013) Dynamic Ecodriving for Signalized Arterial Corridors and Its Indirect Network-Wide Energy/Emissions Benefits, https://www.tandfonline.com/doi/full/10.1080/15472450.2012.712494?
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Xu Y. A. (2016) Ecodriving for transit: An effective strategy to conserve fuel and emissions, https://www.sciencedirect.com/science/article/abs/pii/S0306261916314040
Yelchuru B. et al. (2015) AERIS: eco-signal operations modeling report, https://rosap.ntl.bts.gov/view/dot/3537
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