A scientifically accurate assessment of tunnel health is the prerequisite for the safe maintenance and sustainability of the in-service road tunnel. The changing trend of tunnel health is not considered in existing research. Most evaluation indicators are static and the ambiguities or randomness at the boundary of the health level intervals is neglected in most evaluation methods. In this paper, the evaluation index system combined with dynamic, and static is set to solve these problems. The changing trend of the health state of tunnels is analyzed through the cubic b-spline function. The weights of evaluation indicators are calculated based on the AHP-improved entropy method. The health evaluation method is proposed based on combing the extension theory and the cloud model improved by the cloud entropy optimization algorithm. Finally, the evaluation results of the proposed method are compared with the detection data of the Beilongmen Tunnel of Zhangzhuo Expressway. The results demonstrate that 80% of the evaluation results in the sample tunnel data are consistent with the standard results, and the remaining 20% show a lower level of health than the standard results. This reflects the evaluation results are impacted by the trend of tunnel health status changes. The health state can be more accurately evaluated by dynamic indicators. The improved extension cloud model is feasible and applicable in the health assessment of tunnels. This work provides innovative ideas for the evaluation of the health state of tunnels and theoretical support for the formulation of reasonable maintenance measures.

Multistate information networks (MINs) are the basis of many modern communication networks, and the reliability evaluation of MINs not only contributes to the structural design of networks but also facilitates the development of modern communication technologies. However, the evaluation of the reliability of MINs in studies so far always requires massive calculations. To improve the calculation efficiency of MIN reliability evaluation, in this article, a new algorithm with lower time complexity than traditional algorithms is proposed. The comparative experiments show that the proposed algorithm outperforms the existing MIN reliability algorithms, and the running time of the proposed algorithm is at least 31.96% less than that of the node-based binary-addition tree algorithm.

In order to adapt to the healthy development of Chinas road network and the prosperity of the rural economy, rural roads are facing the need to continue to promote the construction of roads that reach deeper nodes. It is urgent to conduct in-depth and systematic research on the planning methods of Chinas rural networked roads. A road network model oriented to rural road network planning is proposed. First, the traffic demand is predicted, and then remote sensing technology and computer technology are used to evaluate the technical performance of the rural road network. The experimental results show that the comprehensive evaluation index value is 0.8 by combining the weight of each index, and the planning scheme is comprehensively evaluated. The evaluation results show that the program better supports the local social and economic development.

Urban traffic congestion has emerged as a global challenge constraining sustainable development. Estimating the traffic congestion probability is crucial since it presents valuable information for formulating congestion mitigation strategies and improving traffic management. The existing studies employ deterministic models to predict congestion; however, they do not consider the dynamic coupling between intrinsic traffic flow randomness (e.g. spatiotemporal heterogeneity in vehicle arrivals) and congestion formation mechanisms, causing prediction biases under high-uncertainty scenarios. In this study, we propose a probability-based congestion estimation framework that employs the stochastic traffic flow theory. The traffic arrival process is described using discrete probability distributions owing to the stochastic nature of traffic flows. To prevent the misclassification of transient traffic surges as congestion, we adopt a spatiotemporal persistence criterion with dual thresholds (vehicle accumulation exceeding a critical level and duration surpassing a minimum time) for congestion identification. Additionally, we perform empirical validation using traffic datasets from Portland, USA, which demonstrates that there is no statistically significant deviation from the measured data at the 95% confidence level in the calculated congestion probabilities. The proposed method facilitates the development of targeted congestion mitigation countermeasures and presents novel insights for future transportation planning.

With the development of technology, shared autonomous vehicles may become one of the main traffic modes in the future. Especially, shared autonomous vehicle reservation system, commuting, and other trips with fixed departure time mostly submit their travel requests in advance. Therefore, it is important to reasonably match shared autonomous vehicles and reservation demands. In this paper, reservation requests are divided into short-term and long-term requests by inputting requests in a more realistic way. An integer linear programming model considering operator scheduling cost and system service level is established. A detailed scheme considering rolling horizon continuity and ridesharing is used to improve the dispatching result. Based on traffic data in Delft, the Netherlands, 164 scenarios are tested in which the parking cost, fuel cost, ridesharing effect, service level, and network size are analyzed. The results show that a better relocation and ridesharing matching scheme can be obtained when the rolling horizon is small, while the overall effect is better when the rolling horizon is large. Moreover, the buffer time, distance, and travel time limit for vehicle relocation should be selected according to the request quantity and the calculation time requirement. The result can provide a suggestion for the dispatching of shared autonomous vehicle reservation system with ridesharing.