To better understand the transportation situation in rapid urbanization areas and to improve social equity, this study constructed an approach to assess the spatial differentiation of public transportation resources based on deprivation theory and an accessibility analysis. Chenggong New District in Kunming, a typical rapid urbanization area in China, was analyzed as a case study. We introduced 6 indexes to establish a public transportation spatial deprivation evaluation system and applied SPSS to screen out two main factors that reflected the spatial deprivation associated with public transportation resources and services. Then, we adopted the accessibility model and spatial cluster model to embody residents’ opportunities to obtain access to public transportation and to judge whether public transportation resource allocation is appropriate. In addition, we used ArcGIS technology to better understand the spatial deprivation characteristics of public transportation. We found that the pattern of public transportation spatial deprivation in Chenggong could be summarized as “multicore and local radiation”: the spatial accessibility characteristics of public transportation take the form of a circular layer along with the metro lines and decline progressively toward the peripheral areas, where public transportation resource allocation is lacking. These findings show that the public transportation situation in rapid urbanization areas is consistent with the local land-use context and the suitability of established methods for extracting spatial public transportation characteristics.

As the transaction subject of contract farming, agricultural products are featured with a long production cycle and a short sales cycle, just like other perishable commodities. In the process of executing a contract, both the company and the farmer are running the risk of great uncertainty. This paper studies the coordination of agricultural supply chain in terms of the uncertainty of agricultural output and market demand. First of all, the random output volatility factor and the market demand volatility factor as two random variables are used to represent the uncertainty of the agricultural output and market demand, and revenue functions are set up, respectively, for the company and the farmer with the objective of maximizing expected returns. The theoretical derivation of these revenue functions proves that there is an optimal targeted yield in a centralized decision-making supply chain system and a single optimal solution that maximizes farmers’ revenue can be obtained in a decentralized one, but the centralized decision-making supply chain is superior to the decentralized and uncoordinated counterpart for overall benefit. Secondly, a revenue-sharing-plus-margin contract mechanism is proposed to coordinate income distribution between the two parties of the supply chain through the revenue-sharing coefficient and margin. Thirdly, calculation examples are given and solved by MATLAB based on the assumption that both the agricultural output volatility factor and the market demand volatility factor are uniformly distributed, and the theory and result are then verified consistently. Finally, the numerical analysis of the coordination mechanism of the revenue-sharing coefficient and the margin on both sides of the supply chain provides an optimal value range so that Pareto improvement on the company’s and the farmers’ income can be achieved.

In this paper, the bilevel programming model of the public transport network considering factors such as the per capita occupancy area and travel cost of different groups was established, to alleviate the urban transportation equity and optimize the urban public transport network under fairness constraints. The upper layer minimized the travel cost deprivation coefficient and the road area Gini coefficient as the objective function, to solve the optimization scheme of public transport network considering fairness constraints; the lower layer was a stochastic equilibrium traffic assignment model of multimode and multiuser, used to describe the complex selection behavior of different groups for different traffic modes in the bus optimization scheme given by the upper layer. The model in addition utilised the noninferior sorting genetic algorithm II to validate the model via a simple network. The results showed that (1) the travel cost deprivation coefficient of the three groups declined from 33.42 to 26.51, with a decrease of 20.68%; the Gini coefficient of the road area declined from 0.248 to 0.030, with a decrease of 87.76%; it could be seen that the transportation equity feeling of low-income groups and objective resource allocation improved significantly; (2) before the optimization of public transport network, the sharing rate of cars, buses, and bicycles was 42%, 47%, and 11%, respectively; after the optimization, the sharing rate of each mode was 7%, 82%, and 11%, respectively. Some of the high and middle income users who owned the car were transferred to the public transportation. It could be seen that the overall travel time of the optimized public transport network reduced, enhancing the attraction of the public transport network to various travel groups. The model improves the fairness of the urban public transport system effectively while ensuring the travel demand of the residents. It provides theoretical basis and model foundation for the optimization of public transit network, and it is a new attempt to improve the fairness of the traffic planning scheme.

The signal gaming model based on incomplete information is used to analyze the decisions of commercial banks and medium-sized and small enterprises (SMEs) in supply chain finance business. It is found that the returns of banks are closely relied on the probability of good SMEs join which is proportional to θ (the probability of “good” SMEs in the market) and p (the probability of “good” SMEs chosen to join the supply chain finance) in supply chain finance business, and the default cost is an important constrain for determining the strategies adopted by the SMEs and the banks. To achieve higher returns, SMEs and banks should make effects to create a better supply chain finance business environment to achieve the separation equilibrium.

To address the multi-objective conflicts among cost, time, and carbon emissions in Guangxi cross-border container multimodal transport—under the influence of factors such as tariffs, fluctuating customs clearance efficiency, and carbon emission policies—this paper develops a multi-objective optimization model that integrates mixed time windows and scenario analysis. The model incorporates multiple elements, including transportation cost, transshipment cost, tariff cost, time value of cargo, carbon tax cost, in-transit and customs clearance time, and transshipment-related carbon emissions, making it more aligned with real-world cross-border operational scenarios. To effectively solve this complex model, an improved NSGA-II algorithm (I-NSGA2) is designed, which introduces an adaptive crossover and mutation operator along with an elite retention strategy to enhance convergence speed and solution diversity, while embedding a scenario parameter response mechanism to accommodate dynamic fluctuations in key parameters. Subsequently, an evaluation framework is constructed using the entropy weight–TOPSIS method to select recommended routes with favorable cost–time–carbon trade-offs from the Pareto frontier. A case study based on the Nanning–Kuala Lumpur route is conducted for validation. Experimental results demonstrate that the I-NSGA2 algorithm significantly outperforms MOPSO, MOEAD, and the standard NSGA-II in terms of IGD and HV metrics; time-sensitive cargo tends to favor rail-dominated routes, while low-cost cargo prefers combined road–water–rail routes. This study effectively addresses the route selection problem for Guangxi cross-border container multimodal transport under varying key parameters, and also provides a research foundation for optimizing cross-border multimodal transport route selection in other regions.

Cross-border transportation networks show considerable complexity and vulnerability under the influence of geopolitical risks. To identify critical nodes and simulate the propagation paths of failure risk within cross-border transportation networks, this study develops a multilayer cross-border transportation network model that integrates four transport modes: waterway, road, rail, and air transport. First, composite edge weights are calculated using the Criteria Importance Through Intercriteria Correlation (CRITIC) method based on inter-node transport distance, transport time, and geopolitical risk (GPR) exposure score. These weights capture the joint effects of transport efficiency and risk exposure across different transport corridors. Second, an Improved Weighted K-shell (IWKS) model is proposed for critical-node identification by integrating three network topological indicators: degree centrality, weighted degree, and closeness centrality. Third, an edge-risk-based Susceptible–Infected–Recovered (SIR) propagation model is introduced. Monte Carlo simulations are then used to estimate the simulation-based propagation influence of each node. Finally, the identification performance of the IWKS model is compared with that of conventional centrality indicators. The IWKS scores are also significantly correlated with the simulation-based propagation influence under three types of correlation tests. The findings support the applicability of the proposed IWKS model in critical-node identification and system vulnerability research of multilayer cross-border integrated transportation networks.