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With the widespread application of large-section quasi-rectangular pipe-jacking tunnels in urban road traffic engineering in China, higher requirements have been put forward to control the influence of their construction on the surrounding environment. To scientifically evaluate the stability of large-section quasi-rectangular pipe-jacking tunnels under-passing existing box culverts, we proposed a novel viscoelastic–plastic model coupling Biot consolidation with non-stationary parameter shear creep (NPSCBCVPM) to fully characterize the coupling effect of consolidation and rheology of saturated soft soil. NPSCCBVPM was developed in Fortran as an ABAQUS user material subroutine. In addition, the NPSCBCVPM was compared with the creep tests of undisturbed soft soil and the generalized Nishihara creep model (GNCM). Finally, the proposed model was applied to the large-section quasi-rectangular pipe-jacking tunnel under-passing existing box culverts of Songhu Road in Shanghai. The results show that NPSCBCVPM are in good agreement with the creep tests of soft soil, and NPSCBCVPM can better reflect the nonlinear rheological characteristics of soft soil than GNCM. Furthermore, the proposed model can scientifically evaluate the viscoelastic–plastic stability analysis of large-section quasi-rectangular pipe-jacking tunnel under-passing box culvert. Further research should focus on developing three-dimensional NPSCBCVPM to better evaluate the spatial response of the box culvert structure and surrounding soil to the entire construction process of large-section quasi-rectangular pipe-jacking tunnels. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China has contributed to traffic events, such as traffic accidents and delays. It is difficult to detect and resolve highway traffic congestion in a timely manner using traditional methods because they are slow, require a large number of workers, and require the installation of a large amount of monitoring equipment. Therefore, it is imperative to introduce advanced technology to address these challenges. Recently, deep learning technology has made significant breakthroughs and has been widely applied to various fields with satisfactory results. Deep learning is among the most important technologies for the detection and evaluation of traffic congestion, enabling the accurate detection of the state of the expressway network's traffic congestion, the evaluation of traffic congestion, and the prediction of possible traffic congestion. This enables management to formulate traffic dredging strategies in advance to prevent the negative impact of traffic congestion on normal traffic flow. This paper proposes a framework based on deep learning for next-generation highway traffic management. This framework selects traffic congestion indicators to construct an index model, and then constructs a deep learning model based on self-coding. It predicts and classifies highway traffic environment data and excavates sample data based on the characteristics of traffic parameters. As soon as traffic data were classified, a prediction model based on SoftMax was established to detect and predict highway traffic congestion. We conducted a traffic congestion analysis of the Shanghai expressway network based on the speed performance data obtained from the China Traffic Management Bureau. As a result of their research, we developed an index to measure highway traffic congestion. For traffic control and management organizations to function effectively, it is crucial to have an accurate and clear picture of traffic network operations. We evaluated the proposed framework using data gathered from highway monitoring scenes, and the results indicated that 98.6% of the data could be correctly detected. Using the prediction model based on SoftMax for expressway vehicles during peak hours, the accuracy was 92%, and the misjudgment rate was 8%. This study demonstrates that detecting and evaluating the state of highway traffic environments using deep learning has high accuracy, can be applied to actual highway traffic systems, and is extremely useful for detecting highway congestion. This framework is a promising solution for next-generation highway traffic management and provides accurate and timely traffic congestion detection and evaluation. [ABSTRACT FROM AUTHOR]

Through a review of previous studies, this paper analysed the epidemiological characteristics and attempts to determine the various trends of road traffic injuries (RTIs) in China before and after the coronavirus disease 2019 (COVID-19). This paper proposed effective measures and suggestions for responding to RTIs in China. Moreover, this paper aimed to provide some references for studies on RTIs in the future. According to a reference review, 50 articles related to RTIs were published and viewed in the China National Knowledge Infrastructure (CNKI), Wanfang database, Weipu (VIP) database and PubMed/MEDLINE database. Articles were selected according to the exclusion and inclusion criteria and then classified and summarized. Regarding cases, RTIs in China were highest in summer, autumn, and in rural areas and lowest in February. Men, elderly individuals and people living in rural areas were more susceptible to RTIs. In addition, thanks to effective and proactive policies and measures, the number of RTIs and casualties in China has substantially decreased, while there has been a growing number of traffic accidents along with the increase in nonmotor vehicles. However, it is worth noting that the number of RTIs obviously fell during the COVID-19 pandemic due to traffic lockdown orders and home quarantine policies. Nevertheless, accidents related to electric bicycles increased unsteadily because of the reduction in public transportation use at the same time. The factors that cause RTIs in China can be divided into four aspects: human behaviours, road conditions, vehicles and the environment. As a result, measures responding to RTIs should be accordingly proposed. Moreover, the road traffic safety situation in developing countries was more severe than that in developed countries. RTIs in China showed a downward trend attributed to road safety laws and various policies, and the downward trend was more significant during the COVID-19 pandemic owing to traffic lockdowns and home quarantine measures. It is urgent and necessary to promote road traffic safety, reduce injuries, and minimize the burden of injuries in developing countries. [ABSTRACT FROM AUTHOR]

The urban green wave traffic control system is one of the effective means to solve the problems of urban traffic jams and reduce vehicle delay. Aiming at the problems of traditional green wave traffic control in optimization computation, this paper introduces the adaptive mechanism and crossover, mutation operators to artificial fish swarm algorithm (AFSA) in order to adjust the evolution group. Through setting bulletin board measure and retention strategy, the individual status of the optimal artificial fish is recorded. In that way, the adaptive genetic AFSA is established. Finally, the paper applies this algorithm to green wave traffic control in five continuous intersections on Jianning Road, Lanzhou, China, gains one traffic control program better than traditional method through calculation, and verifies the feasibility and effectiveness of adaptive genetic-AFSA to optimize green wave traffic control system. [ABSTRACT FROM AUTHOR]