This paper proposes a two-stage multi-lane unsignalized intersection cooperative control strategy based on mixed platoons (MICS-PF) in the mixed traffic environment of connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs). In the first stage, we propose a 0-1 integer programming model based on vehicles’ intersection movements and potential car-following costs to organize vehicles into mixed platoons led by CAVs. Meanwhile, we design a rule-based algorithm to coordinate the relative distribution of platoons. Then, the lane-changing and car-following models are integrated to ensure that the platoons organization plan can be completed in the limited functional zone. In the second stage, we design an improved minimum covering clique algorithm to generate a conflict-free passing order of platoons by considering the platoon-level conflict relation and the potential yield delay. The proposed strategy couples the process of organizing vehicles into mixed platoons to pass through the intersection. The optimal platoon organization enables CHVs to receive accurate guidance when entering the intersection and avoids the disorderly lane change at the upstream road sections. The mixed platoon control also allows vehicles to avoid stop-and-go, improve the capacity of approach roads, and prevent the intersection capacity from being restricted by the inefficient traffic at the approach road. The conflict-free coordination method of mixed platoons optimizes the throughput of intersections and broadens the usage conditions of unsignalized intersections. A series of simulation experiments verify the effectiveness of the strategy, and the applicable boundary conditions are obtained.

With the advent of the data-driven era, deep learning approaches have been gradually introduced to short-term traffic flow prediction, which plays a vital role in the Intelligent Transportation System (ITS). A hybrid predicting model based on deep learning is proposed in this paper, including three steps. Firstly, an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method is applied to decompose the nonlinear time series of highway traffic flow to obtain the intrinsic mode function (IMF). The fuzzy entropy (FE) is then calculated to recombine subsequences, highlighting traffic flow dynamics in different frequencies and improving prediction efficiency. Finally, the Temporal Convolutional Network (TCN) is adopted to predict the recombined subsequences, and the final prediction result is reconstructed. Two sensors of US101-S on the main road and on-ramp were selected to measure the prediction effect. The results show that the prediction error of the proposed model on two sensors is notably decreased on single-step and multistep prediction, compared with the original TCN model. Furthermore, the proposed improved CEEMDAN-FE-X framework can be combined with prevailing prediction methods to increase the prediction accuracy, among which the improved CEEMDAN-FE-TCN model has the best performance and strong robustness.

Autonomous vehicles (AVs) have the potential to improve safety, traffic capacity, and energy efficiency, but these advantages can only be realized when the AV market penetration is sufficiently high. To promote the adoption of AVs, it would be crucial for the government to take policy measures. This paper develops a two-stage model to explore the effects of subsidy and AV lanes’ policies on AV adoption. In the first stage, given the subsidy policy, the vehicle manufacturer sets AV price to pursue maximum profit while anticipating the choice of consumers for AVs or conventional vehicles (CVs), from which the AV market penetration can be accessed. Subsequently, based on the AV market penetration acquired from the first stage model, an optimization model integrating the mixed traffic assignment is developed in the second stage to determine the time-dependent progressive AV lanes’ deployment plan. The first and second stage models are solved using the simulation-optimization and genetic-algorithm-based approaches, respectively. Due to the mutual influence of the two models, an iterative optimization approach is applied to solve the whole model. Two numerical experiments are conducted, and the results demonstrate the positive effects of subsidy and AV lane policies on increasing AV market penetration. The analysis provides significant managerial insights for policymakers to promote the development of AVs.

The work zone on the urban road network will affect the surrounding road traffic. To represent the influence area of the work zone, the concept of a subnetwork is proposed in this paper. Delineating a suitable subnetwork quantitatively is a challenging problem. To address this issue, the node synthesized indexes (NSI) are deployed as a variability measure that captures both the change of link flow and origin-destination (OD) demand. The inertia-based stochastic user equilibrium with the elastic demand (ISUEED) model is proposed to accurately provide the data of link flow and OD demand for the network with the work zone. Correspondingly, the data of the network without a work zone can be obtained by the stochastic user equilibrium (SUE) model. According to the value of NSI, the initial range of the subnetwork is determined. Finally, the connectivity and compactness can be guaranteed by the modified L-shell algorithm. To demonstrate the performance of the method, two case studies and sensitivity analyses are conducted based on the Braess network and the local road network in Changchun, China. The proposed method is beneficial to reduce the complexity of the traffic model by substituting the entire network with a subnetwork.