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Advanced connected and automated vehicle technologies enable us to modify driving behavior and control vehicle trajectories, which have been greatly constrained by human limits in existing manually-driven highway trac. In order to maximize benets from these technologies on highway trac management, vehicle trajectories need to be not only controlled at the individual level but also coordinated collectively for a stream of trac. As one of the pioneering attempts to highway trac trajectory control, Part I of this study (Zhou et al., 2015) proposed a parsimonious shooting heuristic (SH) algorithm for constructing feasible trajectories for a stream of vehicles considering realistic constraints including vehicle kinematic limits, trac arrival patterns, car-following safety, and signal operations. Based on the algorithmic and theoretical developments in the preceding paper, this paper proposes a holistic optimization framework for identifying a stream of vehicle trajectories that yield the optimum trac performance measures on mobility, environment and safety. The computational complexity and mobility optimality of SH is theoretically analyzed, and veries superior computational performance and high solution quality of SH. A numerical sub-gradient-based algorithm with SH as a subroutine (NG-SH) is proposed to simultaneously optimize travel time, a surrogate safety measure, and fuel consumption for a stream of vehicles on a signalized highway section. Numerical examples are conducted to illustrate computational and theoretical ndings. They show that vehicle trajectories generated from NG-SH signicantly outperform the benchmark case with all human drivers at all measures for all experimental scenarios. This study reveals a great potential of transformative trajectory optimization approaches in transportation engineering applications. It lays a solid foundation for developing holistic cooperative control strategies on a general transportation network with emerging technologies.

This article addresses the problem of controlling the speed of a number of automated vehicles before they enter a speed reduction zone on a freeway. We formulate the control problem and provide an analytical, closed-form solution that can be implemented in real time. The solution yields the optimal acceleration/deceleration of each vehicle under the hard safety constraint of rear-end collision avoidance. The effectiveness of the solution is evaluated through a microscopic simulation testbed and it is shown that the proposed approach significantly reduces both fuel consumption and travel time. In particular, for three different traffic volume levels, fuel consumption for each vehicle is reduced by 19-22% compared to the baseline scenario, in which human-driven vehicles are considered, by 12-17% compared to the variable speed limit algorithm, and by 18-34% compared to the vehicular-based speed harmonization (SPD-HARM) algorithm. Similarly, travel time is improved by 26-30% compared to the baseline scenario, by 3-19% compared to the VSL algorithm, and by 31-39% compared to the vehicularbased SPD-HARM algorithm. His research spans several fields, including analysis, optimization, and control of cyber-physical systems; decentralized systems; and stochastic scheduling and resource allocation problems. The emphasis is on applications related to sociotechnical systems, energy efficient mobility systems, and sustainable systems. He is currently an Associate Editor of the IEEE Transactions on Intelligent Vehicles and IEEE Transactions on Intelligent Transportation Systems. He is a member of SIAM and a Fellow of the ASME. Seongah Hong received the B.S. degree in Urban Planning and Engineering from Yonsei University, Seoul, Korea in 2012 and the M.S. from the Civil Engineering in the University of Minnesota, Duluth, MN in 2014, and a Ph.D. degree in the Civil and Environmental Engineering Department at the University of Virginia, Charlottesville, VA, USA. She has participated in various research project about traffic operations, intelligent transportation systems, connected automated vehicles applications, cybersecurity and monitoring system, and sustainable traffic control devices. She has conducted extensive work of modeling and analyzing the effectiveness of the traffic operations applications with the customized control algorithms under diverse scenarios.