Impacts of automated driving on energy demand and emissions in motorway traffic

Abstract

Automated Vehicles (AVs) are expected to reduce CO2 emissions and energy demand of road transportation by mechanisms such as more stable vehicle control, but realisation of these benefits depends on AV deployment and use. Simulation studies have reported a wide range of potential impacts, depending on the driver model and assumptions. Studies have focused on the total impact on CO2 emissions in specific traffic volume and speed limit conditions and have not separated impacts for different road users. Heavy-duty vehicles (HDVs) have often been omitted entirely. This study assessed the potential impacts of conditionally automated driving on the CO2 emissions and energy demand of equipped and unequipped passenger cars (MVs) and unequipped HDVs with a systematic approach, covering different speed limits, traffic volumes and AV penetration rates on motorways. The methodology incorporated traffic microsimulation, an emissions calculation tool, and a formula for tractive energy demand. Replacing passenger cars with AVs in traffic simulation affected emissions of all road users, and the magnitude and direction of impacts differed between vehicle types. Whereas average energy demand and CO2 emissions of AVs were lower in most conditions compared to MVs at baseline, benefits for all vehicle types were seen only at the highest traffic volumes. Changed traffic dynamics can lead to increases in energy demand and emissions of HDVs and MVs already at low AV penetration rates in moderate traffic. Thus, future studies on AV impacts should include more variation in simulated conditions and consider impacts on different vehicle types separately.

Keywords

• Automated driving
• Heavy-duty vehicles
• Motorway
• Traffic simulation
• CO2 emissions
• Energy demand