Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA; OSTI
Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA
Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA; College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt
Charles E. Via, Jr. Department of Civil and Environmental Engineering, Center for Sustainable Mobility, Virginia Tech Transportation Institute, Blacksburg, VA
The paper evaluates an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network considering a combination of internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and battery-only electric vehicles (BEVs) in a microscopic traffic simulation environment. We used a novel integrated control system that: (1) routes ICEVs, HEVs, and BEVs in a fuel/energy-efficient manner; (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles along freeways within a cooperative platoon to minimize fuel/energy consumption. The study tested the system using the INTEGRATION software on the Los Angeles (LA), U.S., downtown network for three different demand levels: no congestion, mild congestion, and heavy congestion. The results demonstrated that the Eco-CAC system effectively reduces vehicle fuel and energy consumption, travel time, total delay, and stopped delay in heavily congested conditions. However, different vehicle compositions produced different results. In particular, the maximum energy consumption savings for BEVs (36.9%) for a current vehicle composition occurred at a 10% market penetration rate (MPR) of connected automated vehicles (CAVs) in mild congestion, while the maximum savings for a future vehicle composition (35.5%) occurred at a 50% CAV MPR in no congestion. The system reduced fuel consumption for ICEVs and HEVs by up to 5.4% and 6.3% at a 25% CAV MPR in heavy congestion for current and future vehicle compositions, respectively. However, the system increased total fuel consumption by up to 4.6% at a 50% CAV MPR in no congestion for a current vehicle composition. The study demonstrates that the effectiveness of the Eco-CAC system depends on traffic conditions, including congestion level, network configuration, CAV MPR, and vehicle composition.
Ahn, Kyoungho, et al. "Evaluating an Eco-Cooperative Automated Control System." Transportation Research Record: Journal of the Transportation Research Board, vol. 2677, no. 2, Aug. 2022. https://doi.org/10.1177/03611981221113315
Ahn, Kyoungho, Du, Jianhe, Farag, Mohamed, & Rakha, Hesham A. (2022). Evaluating an Eco-Cooperative Automated Control System. Transportation Research Record: Journal of the Transportation Research Board, 2677(2). https://doi.org/10.1177/03611981221113315
Ahn, Kyoungho, Du, Jianhe, Farag, Mohamed, et al., "Evaluating an Eco-Cooperative Automated Control System," Transportation Research Record: Journal of the Transportation Research Board 2677, no. 2 (2022), https://doi.org/10.1177/03611981221113315
@article{osti_2418020,
author = {Ahn, Kyoungho and Du, Jianhe and Farag, Mohamed and Rakha, Hesham A.},
title = {Evaluating an Eco-Cooperative Automated Control System},
annote = {The paper evaluates an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network considering a combination of internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and battery-only electric vehicles (BEVs) in a microscopic traffic simulation environment. We used a novel integrated control system that: (1) routes ICEVs, HEVs, and BEVs in a fuel/energy-efficient manner; (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles along freeways within a cooperative platoon to minimize fuel/energy consumption. The study tested the system using the INTEGRATION software on the Los Angeles (LA), U.S., downtown network for three different demand levels: no congestion, mild congestion, and heavy congestion. The results demonstrated that the Eco-CAC system effectively reduces vehicle fuel and energy consumption, travel time, total delay, and stopped delay in heavily congested conditions. However, different vehicle compositions produced different results. In particular, the maximum energy consumption savings for BEVs (36.9%) for a current vehicle composition occurred at a 10% market penetration rate (MPR) of connected automated vehicles (CAVs) in mild congestion, while the maximum savings for a future vehicle composition (35.5%) occurred at a 50% CAV MPR in no congestion. The system reduced fuel consumption for ICEVs and HEVs by up to 5.4% and 6.3% at a 25% CAV MPR in heavy congestion for current and future vehicle compositions, respectively. However, the system increased total fuel consumption by up to 4.6% at a 50% CAV MPR in no congestion for a current vehicle composition. The study demonstrates that the effectiveness of the Eco-CAC system depends on traffic conditions, including congestion level, network configuration, CAV MPR, and vehicle composition.},
doi = {10.1177/03611981221113315},
url = {https://www.osti.gov/biblio/2418020},
journal = {Transportation Research Record: Journal of the Transportation Research Board},
issn = {ISSN 0361-1981},
number = {2},
volume = {2677},
place = {United States},
publisher = {SAGE},
year = {2022},
month = {08}}
Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
DOE Contract Number:
EE0008209
OSTI ID:
2418020
Journal Information:
Transportation Research Record: Journal of the Transportation Research Board, Journal Name: Transportation Research Record: Journal of the Transportation Research Board Journal Issue: 2 Vol. 2677; ISSN 0361-1981
2011 14th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2011), 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)https://doi.org/10.1109/ITSC.2011.6083084