This paper investigates the impact of ride-hailing services, particularly the integration of autonomous vehicles (AVs), on urban transportation systems. The paper discusses the challenges faced by ride-hailing platforms in managing a fleet of both AVs and conventional vehicles (CVs) within the spatial network of a city. It examines the approaches and methods used to manage demand allocation for AVs and CVs, considering the strategic behavior of human drivers and considerations for possible regulations. Using mean-field game theory, this paper proposes efficient strategies for managing fleet operations along with those of traffic optimization and service efficiency. The analysis highlights the complexities of integrating AVs into existing transportation systems and advocates for the development of robust theoretical traffic models for regulatory decisions and improved urban mobility.

This paper proposes an algorithm for train delay propagation on double-track railway lines under First-Come-First-Serve (FCFS) management. The objective is to handle the challenges faced by the dispatchers as they encounter train delays and their effects on the functioning of the railway system. We assume that the location and duration of disruptions are known, which are important inputs to the algorithm. This data enables calculation of delays experienced by each affected train. Our method analyzes factors such as train schedules, track capacities, and operation constraints to assess the manner in which delays would get propagated along railway lines. Key indicators of delay propagation, consisting of the number of delayed trains and stations, disruption settling time, and cumulative delays, are considered. Moreover, a numerical example is given to explain the practical application of this algorithm. Finally, we show that a tool like this would facilitate the dispatchers in managing and rescheduling trains in case of delays and will be improving resilience and efficiency of railway operations.

Mega infrastructure projects (MIPs) play crucial roles in promoting social development, regional growth, and disaster and crisis resilience. These complex projects frequently face challenges in stakeholder management, which might be a risk for their sustainability. Hence, this paper proposes affordance theory as a new theoretical framework, particularly on the basis of understanding and managing MIPs. This paper aims to achieve three main objectives: 1) conceptualizing MIP affordance, 2) documenting the influence of MIP affordance on stakeholder management and project sustainability, and 3) developing strategies for managing MIP affordance. It applies critical realism to conceptualize MIP affordance and its mechanism, and employs the expectation-confirmation theory to identify critical determinants for managing MIP affordance. The paper thus provides new knowledge and insights into the management of MIP stakeholders concerning project sustainability.

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.