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.

The urban signalized road network, characterized by its dynamic and complex nature due to frequent signal control adjustments and unpredictable demand fluctuations, presents significant challenges for predicting lane-level traffic flow. This study introduces the innovative MGCN-TAMA model, which addresses these challenges by integrating multi-graph convolutional networks with a temporal-aware multi-head attention mechanism. The proposed model employs three types of adjacency matrices-a geographical matrix, a signal matrix, and an attention matrix-to capture the complex spatial dependencies among various traffic approaches. Additionally, the model utilizes temporal-aware multi-head attention to discern the nonlinear correlations in traffic variations over time. Tested on a real-world dataset from Tongxiang City, the MGCN-TAMA model significantly outperforms traditional models. Notably, in the first 30-minute prediction interval, our model achieves the lowest Mean Absolute Error, with 2.5649 vehicles per 5-minute span. These results underscore the effectiveness of combining graph-based methods with advanced attention mechanisms to enhance the accuracy of predicting lane-level traffic volumes in urban networks.

Multi-section license plate recognition (LPR) data has emerged as a valuable source for lane-based queue length estimation, providing both input–output information and sampled travel times. However, existing studies often rely on restrictive assumptions such as the first-in-first-out (FIFO) rule and uniform arrival processes, which fail to capture the complexity of multi-lane scenarios, particularly regarding overtaking behaviors and traffic flow variations. To address this issue, we propose a probabilistic approach to derive the stochastic queue length by constructing a conditional probability model of no-delay arrival time (NAT), i.e., the arrival time of vehicles without experiencing any delay, based on multi-section LPR data. First, the NAT conditions for all vehicles are established based on upstream and downstream vehicle departure times and sequences. To reduce the computational dimensionality and complexity, a dynamic programming (DP)-based algorithm is developed for vehicle group partitioning based on potential interactions between vehicles. Then, the conditional probability of NATs of each vehicle group is derived and a Markov Chain Monte Carlo (MCMC) sampling method is employed for calculation. Subsequently, the stochastic queue profile and maximum queue length for each cycle can be derived based on the NATs of vehicles. Eventually, we extend our approach to multi-lane scenarios, where the problem can be converted to a weighted general exact coverage problem and solved by a backtracking algorithm with heuristics. Empirical and simulation experiments demonstrate that our approach outperforms the baseline method, demonstrating significant improvements in accuracy and robustness across various traffic conditions, including different V/C ratios, matching rates, miss detection rates, and FIFO violation rates. The estimated queue profiles demonstrate practical value for offset optimization in traffic signal control, achieving a 6.63% delay reduction compared to the conventional method.

Network signal coordination control is a crucial means to improve the traffic operation efficiency of the overall roadway network. Accurate identification of critical paths does play an important role in determining the scope of network coordination control. Therefore, this paper proposed the definition of critical path from the perspective of traffic control and management. Under the detection environment of connected vehicle (CV), a comprehensive quantitative indicator system for path criticality evaluation from three aspects, supply side, demand side and operation side, which are arranged in the form of a tower structure. A critical path identification method (CPIM) was then proposed based on the analytic hierarchy process (AHP) theory, which was hereinafter referred to as AHP-CPIM. In order to evaluate the feasibility and effectiveness of the proposed method, a case study set in an urban network in Tongxiang, Zhejiang Province in China, is conducted through simulation models built through VISSIM and Synchro. Two scenarios were set, one is coordination control based on the coordination subarea obtained from Synchro (namely without critical path identification), and another one is coordination control with critical paths obtained from AHP-CPIM. Results showed that, compared with the control of Synchro and Multiband method under the scenario of coordination control without critical path identification, network signal coordination control optimization based on AHP-CPIM improved about 37.9% and 35.9% in average delay, respectively, justifying the effectiveness of CV-driven critical path identification for network signal coordination control.