The acceleration of economic globalization and integration has led to a dramatic increase in the flow of goods worldwide and changes in the spatial location of logistics facilities. The location of logistics facilities affects not only the cost and efficiency of cargo transportation activities, but also the rational allocation of logistics resources. Recently, the two major perspectives of logistics space research—cluster (the concentration of logistics facilities and functions in geography) and sprawl (movement of facilities from the urban core to peripheral places) have received extensive attention from academia and policy makers. The evolution of logistics space is influenced by land prices, traffic accessibility, market demand, agglomeration advantages and government policies. The purpose of this study is to present a literature review of logistics space, including data sources, research methods as well as research theories, and to study the impact of logistics space from the perspective of sustainable development. The research results provide some reference for logistics space researchers and logistics facility planners, and play a role in formulating new logistics development strategies and promoting the sustainable development of logistics.

Under the background of the prompt development of the global economy and continuous improvement of environmental protection awareness, end-of-life vehicles (ELVs), as an essential part of “urban mineral”, have the substantial economic, resource, and environmental value. The research on reverse logistics of ELVs has developed rapidly, but the existing relevant reviews are based on unique research perspectives and do not fully understand the whole field. This work aims to help comprehend the research status of reverse logistics of ELVs, excavate and understand the critical publications, and reveal the main research topics in the past 20 years. Based on 299 articles published in ISI Web of Science Core Collection (WOSCC) database from 2000 to 2019, this paper uses the methodologies of literature bibliometrics and content analysis, combined with VOS viewer, CiteSpace, and Bibexcel software. Besides, the literature quantity and cited situation, core journals, distribution of countries and regions, institutions, core authors, subject categories, and keywords information are analyzed to determine the primary trends and future research hot spots focus on reverse logistics of ELVs.

With the deepening of economic globalization, the global freight volume has been constantly on the rise; and urban logistics space is gradually changing as well. Reorganization of urban logistics space is closely related to sustainable development. It has great influence on rational distribution of social resources, improvement of urban ecological environment, and balance of urban economic structure. This paper takes A-level logistics enterprises in the Yangtze River Delta as the object of study, aiming at revealing the spatial and temporal evolution of A-level logistics enterprises in Shanghai and Yangtze River Delta in 2005–2015 from the metropolitan and regional levels, respectively, and at providing reference for the rational planning of logistics space. The analysis result shows that the logistics sprawl occurs in various degrees in Shanghai and the Yangtze River Delta, and in the process of logistics enterprises moving from urban centers to the suburbs, the characteristics of logistics enterprises cluster keep emerging and gradually form a specific status. Then, we analyze the reasons underlying the formation of the spatial and temporal distribution pattern of logistics enterprises in light of relevant policies, geography and the sustainable development of economy, thus providing relevant suggestions for the government and logistics enterprises.

With the increasing energy use and carbon emissions in the transportation industry, its impact on the greenhouse effect is gradually being recognized. Therefore, this study aims to explore the achievement of carbon emission peak and carbon neutrality in transportation through prediction. The research employs a deep learning model, the CNN-GRU-Attention model, to predict carbon emissions in the transportation industry in Jiangsu, China. We select influencing factors through an extended STIRPAT model coupled with Lasso regression, and construct the CNN-GRU-Attention traffic carbon emission prediction model according to data indicators from 1995 to 2021. The model predicts carbon emissions from the transportation industry in Jiangsu Province between 2022 and 2035 under six distinct scenarios and proposes corresponding emission reduction strategies. The results show that the model in this study has higher prediction accuracy compared with other models, with a mean absolute error (MAE) of 0.061582, root mean square error (RMSE) of 0.085025, and R² of 0.91609 on the test set. Scenario-based predictions reveal that emission peak in the transportation industry in Jiangsu Province can be achieved under the clean development and comprehensive low-carbon scenarios, with technological innovation being the primary driver of low-carbon emission reductions. This study provides a novel approach for forecasting carbon emissions from the transportation industry and explores the implementation path of emission peak through this method.

With the acceleration of urbanisation and the rapid increase in road traffic volume, the scientific prediction of traffic accidents has become crucial for improving road safety and enhancing traffic efficiency. However, traffic accident prediction is a complex and multifaceted problem that requires the comprehensive consideration of multiple factors, including people, vehicles, roads and the environment. This paper provides a detailed analysis of traffic accident prediction based on multi-source data. By thoroughly considering data sources, data processing and prediction methods, this paper introduces the various aspects of traffic accident prediction from different perspectives. It helps readers understand the characteristics of different data and methods, the process of accident prediction and the key technologies involved. At the end of the paper, the main challenges and future directions in road crash prediction research are summarised. For example, the lack of efficient data sharing between different departments and fields poses significant challenges to the integration of multi-source data. In the future, combining deep learning models with time-sensitive data, such as social media and vehicle network data, could effectively improve the accuracy of real-time accident prediction.