Predicting short-term traffic crashes is challenging due to an imbalanced data set characterized by excessive zeros in noncrash counts, random crash occurrences, spatiotemporal correlation in crash counts, and inherent heterogeneity. Existing models struggle to effectively address these distinct characteristics in crash data. This paper proposes a new joint model by combining the time-series generalized regression neural network (TGRNN) model and the binomially weighted convolutional neural network (BWCNN) model. The joint model aims to capture all these characteristics in short-term crash prediction. The model was trained and tested using real-world, highly disaggregated traffic data collected with inductive loop detectors on the M1 motorway in the UK in 2019, along with crash data extracted from the UK National Accident Database for the same year. The short-term is defined as a 30-min interval, providing sufficient time for a traffic control center to implement interventions and mitigate potential hazards. The year was segmented into 30-min intervals, resulting in a highly imbalanced data set with over 99.99% noncrash samples. The joint model was applied to predict the probability of a crash occurrence by updating both the crash and traffic data every 30 min. The findings revealed that 75.3% of crashes and 81.6% of noncrash events were correctly predicted in the southbound direction. In the northbound direction, 78.1% of crashes and 80.2% of noncrash events were accurately captured. Causal analysis and model-based interpretation were used to analyze the relative importance of explanatory variables regarding their contribution to crashes. The results reveal that speed variance and speed are the most influential factors contributing to crash occurrence.

Construction site layout planning (CSLP) involves strategically placing various facilities to optimize a project. However, real construction sites are complex, making it challenging to consider all construction activities and facilities comprehensively. Addressing multi-objective layout optimization is crucial for CSLP. Previous optimization results often lacked precision, imposed stringent boundary constraints, and had limited applications in prefabricated construction. Traditional heuristic algorithms still require improvements in region search strategies and computational efficiency when tackling multi-objective optimization problems. This paper optimizes the prefabricated component construction site layout planning (PCCSLP) by treating construction efficiency and safety risk as objectives within a multi-objective CSLP model. A novel heuristic algorithm, the Hybrid Multi-Strategy Improvement Dung Beetle Optimizer (HMSIDBO), was applied to solve the model due to its balanced capabilities in global exploration and local development. The practicality and effectiveness of this approach were validated through a case study in prefabricated residential construction. The research findings indicate that the HMSIDBO-PCCSLP optimization scheme improved each objective by 18% to 75% compared to the original layout. Compared to Genetic Algorithm (GA), the HMSIDBO demonstrates significantly faster computational speed and higher resolution accuracy. Additionally, in comparison with the Dung Beetle Optimizer (DBO), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA), HMSIDBO exhibits superior iterative speed and an enhanced ability for global exploration. This paper completes the framework from data collection to multi-objective optimization in-site layout, laying the foundation for implementing intelligent construction site layout practices.

The COVID-19 pandemic caused severe and enduring effects globally, impacting public health, normalcy, and productivity significantly. In response, government-led food supplies became crucial in many countries to counter the adverse effects of pandemic control measures on daily activities. Focusing on government-led food supply chain during the COVID-19 pandemic, this study employed simulations across different pandemic phases to identify and confirm effective recovery strategies. Our analysis pinpointed insufficient transportation capacity, uneven distribution of district warehouses, and production-demand mismatches as the main factors contributing to food shortages. Strategies such as enhancing transportation capacity, establishing new district warehouses, and increasing production capacity proved to significantly bolster supply chain resilience, stabilize supplies, and meet escalating demands. Opening municipal emergency warehouses ahead of potential disruptions also showed a positive recovery effect. However, while food aid from other provinces and more frequent inventory checks generally enhanced resilience, they occasionally led to unintended negative consequences. Surprisingly, reallocating food between district warehouses negatively impacted the supply chain. This research advances the understanding of government-led food supply chain vulnerabilities during significant public health crises and proposes targeted recovery strategies for different pandemic phases, aiding policymakers in better managing future emergencies.

This paper investigates whether e-hailing performs better than on-street searching for taxi services. By adopting the Poission point process to model the temporal-spatial distributions of idle vehicles, passengers’ waiting time distributions of on-street searching and e-hailing are explicitly modeled, and closed-form results of their expected waiting time are given. It is proved that whether e-hailing performs better than on-street searching mainly depends on the density of idle vehicles within the matching area and the matching period. It is proved that given the advantage of e-hailing in rapidly pairing passengers and idle vehicles, the expected waiting time for on-street searching is always longer than that of e-hailing as long as the number of idle vehicles within a passenger’s dominant temporal-spatial area is lower than 4/π. Moreover, we extend our analysis to explore the market equilibria for both e-hailing and on-street searching, and present the equilibrium conditions for a taxi market operating under e-hailing versus on-street searching. With a special reciprocal passenger demand function, it is shown that the performance difference between e-hailing and on-street searching is mainly determined by the ratio of fleet size to maximum potential passenger demand. It suggests that e-hailing can achieve a higher capacity utilization rate of vehicles than on-street searching when vehicle density is relatively low. Furthermore, it is shown that an extended average trip duration improves the chance that e-hailing performs better than on-street searching. The optimal vehicle fleet sizes to maximize the total social welfare/profit are then analyzed, and the corresponding maximization problems are formulated.

The utilization of rooftop space offers various benefits to cities and their residents, such as urban heat island mitigation, energy saving, and water management. However, a comprehensive understanding of these benefits and their regional differences is still lacking. We reviewed 97 articles published between 2000 and 2022 to evaluate the efficiency of various rooftop engineering approaches, including green roofs, white roofs, solar roofs, blue roofs, and wind turbine roofs. The main findings are as follows: (I) As of 2020, there are ~245 billion m² of rooftop space worldwide, equivalent to the land area of the UK. About 29%–50% of these rooftops are suitable for utilization. (II) Effective use of rooftop space can cool cities by ~0.60°C, meet ~44% of city energy demand, reduce runoff by ~17%, and save ~23% of building water demand. (III) Climate and building types influence the efficiency of rooftop engineering, with mediterranean climates and low-rise buildings offering the most favorable conditions. This review provides a comprehensive evaluation of global rooftop resources and their potential benefits, offering valuable guidance for cities to adopt differentiated rooftop strategies.

Since the implementation of the transportation power strategy, China’s transportation industry has developed rapidly, yet the number of road traffic accidents has remained high in recent years. Many scholars have investigated the factors influencing traffic accidents to find the underlying mechanisms, thereby enhancing road traffic safety. Compared to general accidents, the factors influencing major road traffic accidents are more complex. This study focuses on examining the relationships between factors affecting major road traffic accidents. Data on 968 major road traffic accidents from 2012 to 2018 in China were collected and organized. The accident information fields were analyzed to identify seven attributes: accident province, accident region, accident quarter, accident time, accident form, accident vehicle, and weather condition. The Apriori association rule algorithm was employed to mine and solve the strong association rules between accident attribute values. The associations between different influencing factors and the form of accident results were analyzed, with a deeper exploration of three-factor and four-factor rules. The results indicate that certain causal factors jointly contribute to major accidents, particularly in the western region, represented by Guangxi. These accidents mainly involved trucks and occurred in rainy and snowy weather during the first quarter. The conclusions of this research can provide the transportation management department with measures to improve urban road traffic safety and reduce the occurrence of traffic accidents.

With ongoing global industrialization, the demand for refined oil products, particularly in developing countries, is increasing significantly. Shipping companies typically transport refined oil from a primary refinery to multiple oil depots, addressing various demand tasks. To manage uncertain refined oil demand, shipping companies use both self-owned tankers and outsourced tankers, including time-chartered and voyage-chartered tankers. A time charter is a contract where the shipping company pays charter money for a specific period, while a voyage charter involves payments based on voyage frequency. This paper develops a nonlinear programming model to optimize fleet deployment, considering transportation costs and penalty costs for capacity loss during a planning period. Additionally, the model is extended to allow flexible charter types, meaning that time-chartered and voyage-chartered tankers are interchangeable based on shipping demands. A heuristic algorithm based on tabu search is designed to solve the proposed models, and four search operators are incorporated to enhance algorithm efficiency. The models and algorithms are validated using a real tanker fleet. Numerical experiments demonstrate the efficiency of the improved tabu search algorithm in obtaining exact solutions for small-scale instances. The case study indicates that the shipping company prefers waiting for tasks to avoid ship delay penalties and provide high-quality services. Moreover, the flexible charter strategy can reduce shipping costs by 16.34%. These findings offer management insights for determining charter contracts for ship fleets.

This study investigates the use of autonomous vehicles in bus rapid transit lanes during the initial phases of autonomous driving development. The aim is to accelerate the advancement of autonomous driving technologies and enhance the efficiency of bus lane usage. We first develop a dynamic joint optimization model that adjusts autonomous vehicle speeds and bus timetables to minimize vehicle travel times while reducing bus passenger waiting times. We account for random variables such as stochastic passenger arrivals at bus stations and variable demand for autonomous vehicle travel by constructing a stochastic dynamic model. To address the computational challenges of large-scale scenarios, we implement a simulation-based heuristic algorithm framework. This framework is designed to efficiently produce high-quality solutions within feasible time limits. Our numerical studies on an actual bus line show that our approach significantly improves system throughput compared to existing benchmarks. Moreover, by strategically managing the entry of autonomous vehicles into the lane and modifying bus timetables, we further enhance the operational efficiency of the system.

Blockchain technology (BCT) has significantly affected various core challenges in distributed systems, particularly traceability. Integrating BCT into supply chain management offers stakeholders enhanced security, traceability, and reliability. A comprehensive traceability system covering the complete process flow and product tracking is essential for meeting specific quality standards and making informed decisions during supply chain operations. An intelligent software-agent-oriented system with blockchain implementation could be a viable solution to address the need for operational traceability in a decentralized supply chain environment. This study presents a framework for supply chain traceability that includes comprehensive workflow tracking and control, considering both internal and external traceability perspectives. The effectiveness of the proposed framework has been evaluated in the context of a gasoline manufacturing and distribution supply chain. It demonstrates how the proposed framework helps to establish a resilient supply chain that ensures the accurate execution of activities throughout the entire supply chain lifecycle.

Artificial intelligence (AI) has emerged as a promising technological solution for addressing critical infrastructure construction challenges, such as elevated accident rates, suboptimal productivity, and persistent labor shortages. This review aims to thoroughly analyze the contemporary landscape of AI applications in the infrastructure construction sector. We conducted both quantitative and qualitative analyses based on 594 and 91 selected papers, respectively. The results reveal that the primary focus of current AI research in this field centers on safety monitoring and control, as well as process management. Key technologies such as machine learning, computer vision, and natural language processing are prominent, with significant attention given to the development of smart construction sites. Our review also highlights several areas for future research, including broadening the scope of AI applications, exploring the potential of diverse AI technologies, and improving AI applications through standardized data sets and generative AI models. These directions are promising for further advancements in infrastructure construction, offering potential solutions to its significant challenges.