Table 1.
Reduction of GHG emission at different levels of vehicle automation.
| Study | Level of Automation | Cause of Reduction in GHG | Results | Condition |
|---|---|---|---|---|
| Stephens (2016) [17] | Partial Automation | Driver profile and Traffic flow calming | 0–10% 0–5% |
During peak hours During non-peak hours |
| Full Automation | 10–21% 5–11% |
During peak hours During non-peak hours |
||
| Barth and Boriboonsomsin (2009) [15] | Full Automation | Eco-driving | 10–20% nearly 0% |
Congested highway traffic. Free flow |
| Xia et al. (2013) [65] | 5–10% | Under congested city traffic | ||
| Li and Gao (2013) [37] | 10% | Under congested city traffic | ||
| Rakha (2012) [40] | 8–23% | Under different speed, congestion level and design characteristics | ||
| Yelchuru (2014) [42] | Partial automation | Eco-traffic signal timing V2i/i2v communication |
1.8–2% | City driving |
| Full Automation | 2–6% | City driving | ||
| Schrank et al. (2012) [46] | Partial Automation | Collision avoidance | 0–0.95% | City driving |
| Stephens (2016) [17] | Full Automation | 0–1.9% | ||
| Stephens (2016) [17] | Partial Automation | Platooning | 0–12.5% | During peak hours |
| Schito (2012) [50] | Full Automation | 12.5–25% | During non-peak hours | |
| 22.5–27.5% | During non-peak hours | |||
| Zabat et al. (1995) [53] | 10% to 30% | During peak hours | ||
| 20–25% | During non-peak hours | |||
| Wadud et al. (2016) [22] | 3% to 25% | During non-peak hours | ||
| Wadud et al. (2016) [22] | Full Automation | Vehicle/powertrain resizing | 45%– | No condition mentioned |
| Burns et al. (2013) [66] | roughly 50% | |||
| Shoup (2006) [34] | Full Automation | Less Hunting for Parking | 2–11% | During city driving |
| Brown et al. (2014) [35] | Full Automation | 5–11% | ||
| Barth (2009) [15] | Partial Automation | 2–5% | ||
| Brown et al. (2014) [35] | Full Automation | Increase in Ridesharing | Roughly 12% | During city driving |
| Stephens (2016) [17] | Partial Automation | Faster travel | 0–10% | During peak hours |
| Full Automation | 10–40% | During non-peak hours | ||
| Haan et al. (2007) [67] | Full Automation | 20–40% | During non-peak hours | |
| Brown et al. (2014) [35] | Full Automation | 0–40% | During non-peak hours | |
| Partial Automation | 0–10% | During non-peak hours | ||
| Stephens (2016) [17] | Partial Automation | Easier travel | 4–13% | No condition mentioned |
| Stephens (2016) [17] | Full Automation | 30–156% | Living farther | |
| Childress et al. (2015) [68] | Full Automation | 3.6–19.6% | Capacity will increase and value of travel time cost will reduce | |
| Gucwa (2014) [69] | Partial Automation | 4–8% | Living farther | |
| Brown et al. (2014) [35] | Full Automation | 50% | ||
| MacKenzie et al. (2014) [58] | Partial Automation | 4–13% | ||
| Stephens (2016) [17] | Full Automation | Increased Travel by Underserved Populations | 2–40% | Elderly and disabled would travel as much as drivers without medical conditions |
| MacKenzie et al. (2014) [58] | Partial Automation | Mode Shift from Walking, Transit and Regional Air | 2–10% | No condition mentioned |
| Harper et al. (2016) [70] | Partial Automation | Up to 12% | ||
| Brown et al. (2014) [35] | Full Automation | Up to 40% | ||
| Fagnant and Kockelman (2014) [71] | Full Automation | Increased empty miles travelled | 5% to 11% | On city driving |