Jin Qin

1 In recent years, the rapid advancement of industrialization has led to increasingly severe issues of energy consumption and greenhouse gas emissions (Jiang, Zhang, and Li 2023; Wang, Zhu, and Ge ...

Recurrent hazardous-material shipments may concentrate traffic on a limited number of corridors when transportation cost dominates route choice, yet absolute flow does not provide a consistent basis for comparing heterogeneous road segments. This study develops a stochastic path-flow allocation framework for controlling localized hazardous-material flow concentration across a road network under uncertain OD demand while accounting for heterogeneity in arc-specific safety-informed reference capacities. Hazardous-material flows are expressed as normalized flow burdens, and a common normalized-flow-burden criterion is used to coordinate path-flow allocation across arcs and demand scenarios. The model jointly determines scenario-specific path-flow allocations and a normalized-burden threshold shared across all arcs and modeled demand scenarios, balancing maximum normalized flow burden against expected transportation cost according to the decision maker’s preference. A Lagrangian relaxation decomposition algorithm is developed to solve the model. Numerical experiments show that, relative to the cost-minimizing benchmark, the proposed framework reallocates flows, lowers the maximum normalized flow burden, and mitigates excessive concentration of hazardous-material flows on a limited number of arcs, albeit at additional transportation cost. The framework provides regulators with a quantitative basis for coordinating network-wide path-flow allocation and evaluating the trade-off between localized-burden control and transportation efficiency.

In Sub-Saharan Africa, the emergence of e-commerce and the rising population of cities is placing novel strain on logistics systems. The efficiency of last-mile delivery (LMD) is an important factor for improving the economic output and sustainable development of cities, particularly in fast urban development areas. This study investigates LMD efficiency in urbanizing Accra, Ghana, and Ouagadougou, Burkina Faso for the effects of urbanisation. It also explores the various urban problems such as persistent mass traffic jams, woefully inadequate infrastructure, and a growing number of informal settlements characterized by a lack of addressing systems that collectively impede the operation of LMD. The mixed methods approach integrates Geographic Information System (GIS) analysis with stakeholder surveys and statistical modelling to uncover that unmanaged traffic conditions, coupled with poor road quality, are leading contributors to inefficiency. It increases delivery times, increases operating costs due to fuel use and vehicle degradation, and aggravates environmental damage through the emission of greenhouse gases. Furthermore, informal economies and a lack of formal addressing aggravate these logistical problems within the research, the researchers highlight. An important contribution of this research is the development and empirical validation of a contextualised Automatic Machine Learn ing (AutoML) framework. The results revealed in a systematic case study simulation showed that routes optimized with AutoML achieved a 28% reduction in total delivery time, an estimated 22% reduction in travel distance, and a predicted reduction in fuel consumption by 22.1% compared to routing using regular routing approaches. This paper suggests specific policy recommendations to cities in Sub-Saharan Africa.

Massive relief supplies inter-regional lateral transshipment (MRSIRLT) can significantly enhance the efficiency of disaster response, meet the needs of affected areas (AAs), and reduce deprivation costs. This paper develops an integrated allocation and intermodality optimization model (AIOM) to address the MRSIRLT challenge. A phased interactive framework incorporating adaptive differential evolution (JADE) and improved adaptive large neighborhood search (IALNS) is designed. Specifically, JADE is employed in the first stage to allocate the volume of massive relief supplies, aiming to minimize deprivation costs, while IALNS optimizes intermodal routing in the second stage to minimize the weighted sum of transportation time and cost. A case study based on a typhoon disaster in the Chinese region of Bohai Rim demonstrates and verifies the effectiveness and applicability of the proposed model and algorithm. The results and sensitivity analysis indicate that reducing loading and unloading times and improving transshipment efficiency can effectively decrease transfer time. Additionally, the weights assigned to total transfer time and costs can be balanced depending on demand satisfaction levels.

In recent decades, railway passenger transport enterprises have been exploring numerous operation and management strategies to improve service quality and market competitiveness of railway passenger transport so as to ensure that the interests of railway passenger transport enterprises are maximized when taking social welfare into account. However, there are still shortcomings in the current research with respect to determining the pricing mechanism and formulating a reasonable price. This paper systematically reviews the scientific literature related to railway pricing, focusing on the application of basic price methods, mathematical programming methods, and data-driven methods in railway pricing, with the hope of proposing an innovative direction to solve existing problems. The main subjects involved in the formulation of railway pricing are passenger groups and transportation companies. The research can be conducted from four broad aspects: passenger demand, passenger time value, market segmentation, and the equilibrium relationship between rail service supply and passenger demand. On the basis of absorbing and summarizing the strengths and weaknesses of previous studies, this paper puts forward suggestions for improvement and innovative directions which will help promote railway passenger transport services from the perspective of pricing, thereby enhancing the sustainability of railway transport.

The quality of talent has increased across all fields due to the constant growth of different industries and the growing job saturation. Real‐time job information on recruitment platforms can, therefore, accurately reflect the demand for talent from businesses, serving as a basis for the creation of training policies in schools. In international competition, the development of talents, especially top‐level talents, will become more and more crucial. Growing in importance is China’s economy and social development. The evaluation of higher vocational and technical talents, however, should also be assessed from a variety of angles, given the diversification of talent training objectives and teaching methods, as well as the expansion of teaching functions. An emerging machine learning technology called deep learning (DL) has been developed to bring machine learning closer to the goals of artificial intelligence. This essay offers a thorough evaluation of the depth of deep learning as it relates to the development of innovative talent in schools. The entire school must be strengthened. It is demonstrated that the average execution time is slashed by 0.0024 s, and the learning sample size error of the DL model is reduced by 0.05276 when compared to the Apriori method. As a result, implementing and researching the DL model can significantly improve both the overall teaching quality of schools and their capacity for innovation.