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The erratic nature of human driving tends to trigger undesired waves thatamplify as successive driver reactions propagate from the errant vehicle tovehicles upstream. Known as phantom jams, this phenomenon has been identifiedin the literature as one of the main causes of traffic congestion. This paperis based on the premise that vehicle automation and connectivity can helpmitigate such jams. In the paper, we design a controller for use in a connectedand autonomous vehicle (CAV) to stabilize the flow of human-driven vehicles(HDVs) that are upstream of the CAV, and consequently to lower collision riskin the upstream traffic environment. In modeling the HDV dynamics in the mixedtraffic stream, we duly consider HDV driver heterogeneity and the time delaysassociated with their perception reaction time. We can find that the maximumnumber of HDVs that a CAV can stabilize is lower when human drivers potentialtime delay and heterogeneity are considered, compared to the scenario wheresuch are not considered. This result suggests that heterogeneity and time delayin HDV behavior impairs the CAVs capability to stabilize traffic. Therefore, indesigning CAV controllers for traffic stabilization, it is essential toconsider such uncertainty-related conditions. In our demonstration, we alsoshow that the designed controller can significantly improve both the stabilityof the mixed traffic stream and the safety of both CAVs and HDVs in the stream.The results are useful for real-time calibration of the model parameters thatcharacterize HDV movements in the mixed stream.

Leveraging Vehicle Connectivity and Autonomy to Stabilize Flow in Mixed  Traffic Conditions: Accounting for Human-driven Vehicle Driver Behavioral  Heterogeneity and Perception-reaction Time Delay