Green Light Optimal Speed Advisory Systems Under Multi-modal Traffic Environments for Reducing Fuel Consumption
- Resource Type
- Conference
- Authors
- Lu, Gang; Ge, Yuming; Wang, Miaoqiong; Wang, Jian; Wei, Da; Kang, Chen; Yu, Rundong
- Source
- 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) ISPA-BDCLOUD-SOCIALCOM-SUSTAINCOM Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 2020 IEEE Intl Conf on. :1470-1474 Dec, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Signal Processing and Analysis
Adaptation models
Intelligent vehicles
Veins
Simulation
Vehicle driving
Green products
Carbon dioxide
Multi-modal Traffic
GLOSA
Fuel Consumption
CO2 emissions
- Language
This paper proposes a Green Light Optimal Speed Advisory (GLOSA) system aiming at reducing vehicle fuel consumption, which can be implemented as an IOV solution in multi-modal traffic environments to improve regional traffic conditions. We use VEINS architecture to couple SUMO simulator and OMNET++ simulator to support the change of simulated vehicle driving mode to verify the proposed system. We chose the Haidian District of Beijing as the research target because of its traffic characteristics. Experimental results show that the GLOSA system proposed in this study also performs well in multi-modal traffic environments, with a reduction of 11.28% in waiting time and 9.27% CO 2 emissions.