Cycle-based arrival profiles can describe temporal demand distribution within a signal cycle for signalized intersections, which can be used to calculate indicators such as traffic volume, queue length, and facilitate fine-grained signal control. However, few studies address cycle-level arrival profile estimation based on connected vehicles (CVs). Besides, studies addressing privacy issues for cross-company collaboration in traffic management are still in their infancy. To fill these research gaps, this study proposes a data-driven method for privacy-preserving cycle-based arrival profile estimation using cross-company CV data. The cyclic arrival curve is discretized as an arrival rate vector whose elements are calculated using sampled CV trajectories, thus transforming the arrival profile estimation into a matrix completion problem. Considering cross-company collaboration, a privacy-preserving technique, secure multi-party computation, is used to encrypt initial arrival rate matrices of multiple companies. In particular, a perturbation approach is combined to enhance protection against inference attacks with prior knowledge of the matrix construction process. Then, matrix completion is realized through a singular value thresholding (SVT) algorithm, meanwhile achieving denoising. Empirical evaluation shows that the estimation accuracy of traffic volume and queue length derived from the proposed arrival profile estimation method can reach 87.6% and 78.4%, respectively, meanwhile protecting the privacy of multiple participating companies and outperforming existing methods. Simulation evaluation on a large-scale network further demonstrates the reliability of the proposed method considering ever-changing demand scenarios. A comprehensive sensitivity analyses exhibit its robustness to CV sample size, number of participating parties and data disparity, showing wide popularization and application prospects.

Complex traffic scenes greatly challenge the road safety of automated vehicles (AVs). Recent work only provides an independent perspective from the fundamental modules. This paper integrates the decision-making and path-planning modules to ensure the autonomous driving performance in the high-speed cruising scenario. First, to guarantee deep exploration of the reinforcement learning method, a Bootstrapped deep-Q-Network (BDQN) is proposed to address the adaptive decision-making of AVs. Then, quantifying the multi-performance requirements of AVs under high-speed cruising can be complex. We employ an inverse reinforcement learning (IRL) approach to learn path-planning ability from skilled drivers, generating a reference path for executing lane changes. The simulation results demonstrate the proposed framework can ensure the autonomous cruising performance with safety guarantees.