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Large-scale congestion can lead to traffic paralysis, which severely hampers the flow of vehicles and disrupts the normal functioning of urban traffic. Traffic optimization strategies can effectively improve the performance of road networks, but often ignore the impact of regional traffic conditions and equity. This paper presents a novel traffic strategy to solve regional traffic congestion in large cities, particularly focusing on mixed traffic scenarios of connected and non-connected vehicles. The proposed method involves monitoring the traffic condition of the congestion warning community and adjusting the internal access flow within each region. The problem is formulated as a Stackelberg game, with traffic policymakers and road users as the key players. The upper layer aims to control traffic access by issuing a community warning index, with the objective of minimizing congestion warning conditions within the community. This information is then disseminated to connected vehicles which utilize it to generate personalized route guidance, while non-connected vehicles remain unaffected. The lower-level objective is to allocate vehicles in the transportation network in a user-optimal manner. To solve the bi-level programming model, the paper introduces a variable neighborhood search approach based on graph theory. The Frank-Wolfe algorithm is used to solve the lower-level model, with a penalty function introduced to transform the constrained traffic assignment problem (TAP) into an unconstrained TAP. The proposed method is applied using the data of Beijing urban road network and a sensitivity analysis is conducted to examine the impacts of critical parameters, such as regional partitioning and mixed traffic proportion. The results show that the method exhibits improved optimization performance across different parameter settings, effectively utilizing idle links and contributing to a reduction in the occurrence of traffic warning regions. • The regional control strategy prevents the emergence of warning communities. • A bi-level model is developed to solve the traffic diversion of mixed traffic. • A variable neighborhood search algorithm is designed to solve the model. • The control strategies under different critical variables scenarios are analyzed. [ABSTRACT FROM AUTHOR]

With the development of modern cities, urban rail transit has become an indispensable part of residents' travelling mode, and accurate prediction of urban rail transit passenger flow is particularly important. However, due to the non-linearity and non-stability of passenger flow, the low quality of big data and the lack of data make it more and more difficult to predict the passenger flow of urban rail transit. In this paper, we propose a unique model structure that ingeniously integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN) with fused residual network connections, and a self-attention mechanism. The design of this model is primarily to effectively handle complex and low-quality data, which is often prevalent in passenger flow prediction scenarios. To validate the effectiveness of the proposed model, we used data from the Beijing urban rail transit in 2016 for prediction. In the experiments, we performed comparison study and ablation study. The experimental results show that the model proposed in this paper has significantly improved prediction accuracy in both horizontal and vertical comparisons. This outcome substantiates that the proposed model can not only effectively handle complex and low-quality data but also extract short-term features and long-term dependency features of passenger flow well, thereby achieving more accurate predictions. • Integration of GRU, CNN with fused residual connections, and self-attention mechanism constitutes a novel model. • The model is capable of handling complex and lower-quality data, significantly improving the accuracy of predictions. • The model can simultaneously extract short-term features and long-term dependency features from time series. [ABSTRACT FROM AUTHOR]