The traffic modes in metropolis is interwoven to form an interconnected passenger flow network, and the spatio-temporal relationship between cross-transportation modes is complicated, which requires the joint prediction of passenger flow to analyze the overall travel law. For the cross-transportation passenger flow network, the supergraph correlation matrix of cross-transportation modes is introduced to describe the correlation between the passenger flow supergraph network of bus and subway, and a joint prediction model based on dual-mode spatial-temporal supergraph convolution network (BSTHCN) is proposed. Specifically, the model consists of three parts: an input module, a spatio-temporal convolution module (including temporal and spatial convolutions), and an output module, which can simultaneously capture the passenger flow network characteristics of both buses’and metros’stations and routes, as well as the transfer passenger flow characteristics between the two different passenger flow networks. The proposed model can identify and extract important information features, and perform feature aggregation and allocation. The experimental results show that the proposed model has better prediction accuracy compared to classical prediction models. The proposed model reduces the mean absolute error (MAE) by 8.93% and 8.10% on the bus and metro datasets, respectively, while the RMSE decreased by 10.64% and 7.47%. Moreover, the parameter volume and model runtime of proposed model are within a reasonable range. Compared to S-TGCN and DCRNN, proposed model achieves more accurate predictions with only a 4.82% increase in runtime. On the whole, proposed model demonstrates strong competitiveness. The ablation experimental results further demonstrate that after incorporating hypergraphs and considering multi-modal transportation correlations, the proposed model can better reflect both local and global characteristics in passenger flow networks, thereby improving the accuracy of passenger flow prediction.

Differentiated highway toll strategy can promote carbon emission reduction in the road field and boost the realization of the “double-carbon” goal. In this study, the carbon reduction benefits are considered as a part of the highway’s benefits. With the goal of simultaneously considering highway benefits and logistics enterprise costs, the study constructed a differentiated toll pricing model based on dual-layer programming. Firstly, the factors influencing truck route choices were analyzed; then Logit model was used to construct transfer traffic flow measurement method based on differentiated highway charging and carbon emission measurement method based on transferred traffic flow, and it proposed carbon trading pricing model under shadow price; finally, considering the partial maximization of carbon emission reduction benefits and the cost minimization of logistics enterprises, it proposed the dual-layer programming model of differentiated charging, and at the same time, the traffic flow data of highway A and parallel national and provincial roads in Shandong province were used for example analysis. The results indicate that, through the adjustment of differentiated highway toll strategy, the dual-layer programming model ensures optimal benefits for highway management while minimizing logistics enterprise costs. Furthermore, adjusting toll standards around the optimal toll rate has minimal impact on the benefits of Highway A, with benefit fluctuations remaining below 1.5%, demonstrating good revenue stability. The dual-layer programming model for differentiated toll pricing model proposed in this study can maximize the increase in highway carbon reduction benefits and minimize logistics enterprise costs.