Traffic congestion imposes a high cost on individuals and communities with the rapid growth of vehicle ownership. In addition, the resulting problems, such as vehicle emissions and environmental contamination, have become common. Two approaches have been generally applied to deal with the increasing travel demand: (1) increasing capacity by road transportation infrastructure construction and (2) reasonable traffic allocation by demand management. Facts proved that the second method is more sustainable and efficient without large investments compared with the first method; therefore it has attracted widely attention. The connected vehicle technology is rapidly developing as a substantial technology for monitoring and collecting data; traffic information collected from connected vehicles includes detailed spatial, temporal, and operational characteristics contrasting with the information provided by the traditional resources and traffic sensors. In the connected vehicle environment, each network vehicle acts as a formidable traffic information collector. Meanwhile, the information collected by the vehicles will be transmitted to the connected vehicles, which can obtain accurate, real-time, and comprehensive traffic information by processing the multi-source information. The information feedback to each vehicle provides changes in driving modes and improves safety significantly meanwhile . Evidently, connected vehicles serve as a feedback loop between traffic supply and demand based on the traffic information. Therefore, connected vehicles can properly realize the time-variant traffic distributed to the traffic network . This condition contributes to smooth traffic oscillation and reducing environmental impacts, which can lessen the costs of individuals and the entire system; as a major development direction, connected vehicles have attracted increasing attention.
For the purpose of evaluation of cooperative coordinated adaptive corridor signal timing optimization (CCACSTO) algorithm, it was tested under mixed traffic flow conditions (CAVs and conventional vehicles) and compared with existing pre-timed traffic control. Four different scenarios were studied in this research, first in which all the cars on the roads are traditional non-automated vehicles (conventional vehicles). Second where half of all the vehicles are conventional and the other half are the CAVs with all-knowing functionality, third where the half of the vehicles are conventional but with the coexistence of CAVs: 30% CAVs with normal functionality, 10% CAVs with all-knowing functionality and the remaining 10% CAVs with cautious functionality. The fourth scenario is the one where 100% of the vehicles are CAVs with all-knowing functionality.
Results
1) Comparison between CCACSTO and the existing pre-timed traffic control (100 % conventional vehicles):
The average delay(s/Veh.) for CCACSTO reduced by 40.30%, 31.68% and 33.2% for light traffic scenario being scenario I, scenario II being mild traffic and scenario III that is heavy traffic respectively in comparison to existing pre-timed traffic signal control. The results are represented in Figure 1:
Average delay(s/Veh.) comparison between CCAC0
