Bus bunching has been a persistent issue in urban bus system since it first appeared, and it remains a challenge not fully resolved. This phenomenon may reduce the operational efficiency of the urban bus system, which is detrimental to the operation of fast-paced public transport in cities. Fortunately, extensive research has been undertaken in the long development and optimization of the urban bus system, and many solutions have emerged so far. The purpose of this paper is to summarize the existing solutions and serve as a guide for subsequent research in this area. Upon careful examination of current findings, it is found that, based on the different optimization objects, existing solutions to the bus bunching problem can be divided into five directions, i.e., operational strategy improvement, traffic control improvement, driver driving rules improvement, passenger habit improvement, and others. While numerous solutions to bus bunching are available, there remains a gap in research exploring the integrated application of methods from diverse directions. Furthermore, with the development of autonomous driving, it is expected that the use of modular autonomous vehicles could be the most potential solution to the issue of bus bunching in the future.

To assess the effectiveness of China’s emissions trading scheme (ETS) in facilitating energy structure optimization, we constructed a fuel-switching model utilizing data from 1067 generating units under the Chinese ETS framework. The model simulates the fuel-switching price in China’s thermal power sector, taking into account various allowance allocation strategies. The results show the following: 1) Thermal power plants will transition from coal to gas if the current ETS auction rate surpasses 26%. 2) Furthermore, in scenarios where the ETS operates independently, a transition will occur if the carbon allowance market is entirely auction-based and the carbon price attains 119.50 USD/tCO₂. 3) In a collaborative scenario involving both the ETS and a gas feed-in tariff subsidy, a carbon price of 9.39 USD/tCO₂ will effect a transition from coal to gas, provided both the auction ratio and subsidy price are maximized.

In urban settings, fluctuating traffic conditions and closely spaced signalized intersections lead to frequent emergency acceleration, deceleration, and idling in vehicles. These maneuvers contribute to elevated energy use and emissions. Advances in vehicle-to-vehicle and vehicle-to-infrastructure communication technologies allow autonomous vehicles (AVs) to perceive signals over long distances and coordinate with other vehicles, thereby mitigating environmentally harmful maneuvers. This paper introduces a data-driven algorithm for rolling eco-speed optimization in AVs aimed at enhancing vehicle operation. The algorithm integrates a deep belief network with a back propagation neural network to formulate a traffic state perception mechanism for predicting feasible speed ranges. Fuel consumption data from the Argonne National Laboratory in the United States serves as the basis for establishing the quantitative correlation between the fuel consumption rate and speed. A spatiotemporal network is subsequently developed to achieve eco-speed optimization for AVs within the projected speed limits. The proposed algorithm results in a 12.2% reduction in energy consumption relative to standard driving practices, without a significant extension in travel time.

Cloud systems, which are typical cyber–physical systems, consist of physical nodes and virtualized facilities that collaborate to fulfill cloud computing services. The advent of virtualization technology engenders resource sharing and service parallelism in cloud services, introducing novel challenges to system modeling. In this study, we construct a systematic model that concurrently evaluates system reliability, performance, and power consumption (PC) while delineating cloud service disruptions arising from random hardware and software failures. Initially, we depict system states using a birth–death process that accommodates resource sharing and service parallelism. Given the relatively concise service duration and regular failure distributions, we employ transient-state transition probabilities instead of steady-state analysis. The birth–death process effectively links system reliability, performance, and PC through service durations governed by service assignment decisions and failure/repair distributions. Subsequently, we devise a multistage sample path randomization method to estimate system metrics and other factors related to service availability. The findings highlight that the trade-off between performance and PC, under the umbrella of reliability guarantees, hinges on the equilibrium between service duration and unit power. To further delve into the subject, we formulate optimization models for service assignment and juxtapose optimal decisions under varying availability scenarios, workload levels, and service attributes. Numerical results indicate that service parallelism can improve performance and conserve energy when the workload remains moderate. However, as the workload escalates, the repercussions of resource sharing-induced performance loss become more pronounced due to resource capacity limitations. In cases where system availability is constrained, resource sharing should be approached cautiously to ensure adherence to deadline requirements. This study theoretically analyzes the interrelations among system reliability, performance, and PC, offering valuable insights for making informed decisions in cloud service assignments.

With the burgeoning emphasis on sustainable construction practices in China, the demand for green building assessment has significantly escalated. The overall evaluation process comprises two key components: The acquisition of evaluation data and the evaluation of green scores, both of which entail considerable time and effort. Previous research predominantly concentrated on automating the latter process, often neglecting the exploration of automating the former in accordance with the Chinese green building assessment system. Furthermore, there is a pressing requirement for more streamlined management of structured standard knowledge to facilitate broader dissemination. In response to these challenges, this paper presents a conceptual framework that integrates building information modeling, ontology, and web map services to augment the efficiency of the overall evaluation process and the management of standard knowledge. More specifically, in accordance with the Assessment Standard for Green Building (GB/T 50378-2019) in China, this study innovatively employs visual programming software, Dynamo in Autodesk Revit, and the application programming interface of web map services to expedite the acquisition of essential architectural data and geographic information for green building assessment. Subsequently, ontology technology is harnessed to visualize the management of standard knowledge related to green building assessment and to enable the derivation of green scores through logical reasoning. Ultimately, a residential building is employed as a case study to validate the theoretical and technical feasibility of the developed automated evaluation conceptual framework for green buildings. The research findings hold valuable utility in providing a self-assessment method for applicants in the field.

Reaching consensus within larger social network groups has emerged as a pivotal concern in the digital age of connectivity. This article redefines group consensus as the emergence of collective intelligence resulting from self-organizing actions and interactions of individuals within a social network group. In our exploration of extant research on group consensus, we illuminate two frequently underestimated, yet noteworthy facets: Dynamism and emergence. In contrast to the conventional perspective of consensus as a mere outcome, we perceive it as an ongoing, dynamic process. This process encompasses self-organized communication and interaction among group members, collectively guiding the group towards cognitive convergence and viewpoint integration. Consequently, it is imperative to redirect our focus from the outcomes of group interactions to an examination of the relationships and processes underpinning consensus formation, thus elucidating the mechanisms responsible for the generation of group consensus. The amalgamation of cognitive contexts and accurate simplification of real-world scenarios for simulation and experimental analysis offers a pragmatic operational approach. This study contributes novel theoretical underpinnings and quantitative insights for establishing and sustaining group consensus within the realm of engineering management practices. Concurrently, it holds substantial importance for advancing the broader research landscape pertaining to social consensus.

Using the Hotelling model and evolutionary game theory, this paper studies the optimal production strategy of duopoly auto manufacturers and explores the impacts of two government policies (manufacturer and consumer subsidies) on strategies related to the production of electric vehicles (EVs) or fuel vehicles (FVs). The study finds that consumers’ environmental preferences have direct effects on manufacturers’ market shares and profits, which in turn, affect the manufacturers’ production strategy selection. Specifically, when consumer environmental preference is sufficiently high, both auto manufacturers will eventually choose to produce EVs; when it is moderate, only one with a cost advantage will choose to produce EVs. Finally, when it is low, neither auto manufacturer will produce EVs. The findings further reveal that the more significant the difference in EV production costs is, the more inclined auto manufacturers are to choose a different final stable strategy. Regardless of whether the government subsidizes manufacturers or consumers, the policy only works if subsidies reach a certain threshold. The study also identifies the conditions under which government subsidies are considered more cost-effective.