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Engineering designs for mountainous highways emphasize compliance checking to ensure safety. However, relying solely on compliance checking may lead designers to minimize costs at the expense of high risk indicators, since the overall risk level of the highway design is unknown to the designers. This paper describes a method for the simultaneous consideration of traffic safety risks and the associated cost burden related to the appropriate planning and design of a mountainous highway. The method can be carried out in four steps: First, the highway design is represented by a new parametric framework to extract the key design variables that affect not only the life-cycle cost but also the operational safety. Second, the relationship between the life-cycle cost and the operational safety risk factors is established in the cost-estimation functions. Third, a fault tree analysis (FTA) is introduced to identify the traffic risk factors from the design variables. The safety performance of the design solutions is also assessed by the generalized linear-regression model. Fourth, a theory of acceptable risk analysis is introduced to the traffic safety assessment, and a computing algorithm is proposed to solve for a cost-efficient optimal solution within the range of acceptable risk, in order to help decision-makers. This approach was applied and examined in the Sichuan–Tibet Highway engineering project, which is located in a complex area with a large elevation gradient and a wide range of mountains. The experimental results show that the proposed approach significantly improved both the safety and cost performance of the project in the study area. Keywords: Highway planning and design, Cost–safety optimization, Acceptable risk assessment, Sichuan–Tibet Highway

Falling from height accidents are a major cause of fatalities on construction sites. Despite a lot of research conducted on the enhancement of safety training and removal of hazardous areas, falling accidents remain a major threat for steeplejacks. According to NOISH FACE reports, 75.1% of the fall from height decedents didn't use the Personal Fall Arrest Systems (PFAS), which shows insufficient supervision of the use of Personal Protective Equipment (PPE) by steeplejacks. Few scholars consider PFAS an important measure to prevent falls and the existing studies on PPE inspections showed that they were unsuitable for the scenarios faced by steeplejacks. This paper proposes an automated inspection method to check PPEs' usage by steeplejacks who are ready for aerial work beside exterior walls. An aerial operation scenario understanding method is proposed, which makes the inspection a preventative control measure and highly robust to noise. A deep-learning based occlusion mitigation method for PPE checking is introduced. We tested the performance of our method under various conditions and the experimental results demonstrate the reliability and robustness of our method to inspect falling prevention measures for steeplejacks and can help facilitate safety supervision.

Highway operations are marred with inherent risks of injury or death, making risk management critical for ensuring the adequate safety of the people involved. This paper investigates the interaction between various highway safety risk factors and effective risk mitigation strategies related to such interaction. The Covariate-Balanced Determinant Detector (CBDD) technique is used to estimate the quantity of both individual and combined risks, and their effect on highway operations safety. Through this technique, the most dangerous risk combinations have been identified and corresponding risk mitigation scenarios have been developed. The results illustrate that the most dangerous scenarios probably result from the interactive effect of risk factors rather than individual factors, and the effect of mitigation strategies should be evaluated in response to a risk scenario before it is implemented.

The Minimum Dominating Set (MinDS) problem is an NP-hard problem of great importance in both theories and applications. In this paper, we propose a new local search algorithm ScBppw (Score Checking and Best-picking with Probabilistic Walk) to solve the MinDS problem in large graphs. For diversifying the search, our algorithm exploits a tabu strategy, called Score Checking (SC), which forbids a vertex to be added into the current candidate solution if the vertex’s score has not been changed since the last time it was removed out of the candidate solution. Also, to keep a good balance between intensification and diversification during the search, we propose a strategy that combines, in a novel way, best-picking with probabilistic walk at removing stages. At this stage, the algorithm selects a vertex with the minimum loss, or other vertices in the candidate solution with a probability proportional to the their degrees, depending on how repeatedly the area has been visited. Experimental results show that our solver significantly outperforms state-of-the-art MinDS solvers. Also we conducted several experiments to show the individual impacts of our novelties.

Risk-based design is an essential strategy for enhancing the safety performance of road infrastructure during its operation stage. However, conventional risk-based design generally considers the risk of a single type of disaster, which is insufficient for assessing the multi-hazard risk of the road infrastructure in an extreme environment. In this study, a synthesis method, the risk-informed knowledge-based analytical method (RKAM) for multi-hazard risk assessment, is proposed for the road infrastructure. The RKAM uses risk mapping techniques and the as low as reasonably practicable (ALARP) principle. Expert knowledge is utilized in order to overcome the scarcity of historical data. The RKAM is applied to the Tuoba–Qamdo Highway project in the Qinghai–Tibet Plateau of China to assess the overall risks of causalities and structural damages caused by traffic accidents, fire accidents, and landslide disasters. The results of this case study show the capabilities of RKAM to generate a synthesis assessment of the expected loss by multi-hazard disasters, which provides valuable information for selecting the most appropriate design of road infrastructure.

Road networks, especially those built in an extreme environment, are at risk of service failure after long-term exposure to various types of disruptive events. With growing costs of service failure, it has become increasingly important to evaluate the dynamic operability characteristics, i.e., the dynamic resilience, of road networks. However, existing studies in this field generally considered a one-time disaster event during a relatively short period, and thus became insufficient for assessing the resilience of road networks in an extreme environment with frequent disaster events. This paper aims to propose a Time-dependent Resilience Analysis Framework (TIDRAF) by modeling the dynamic resilience of a road network in an extreme environment (where there are frequent disruptive events) and sequential repair actions. The original resilience triangle diagram is extended into a long-term paradigm to fit the road network with its frequent-disaster environment. The road network in the Tibetan Plateau, which has for years severely suffered from frequent disaster events, was selected as the case to validate the proposed TIDRAF. Based on the results of the analysis, three optimization strategies are presented to improve the resilience of the network against failures.

In this paper, we develop a local search algorithm to solve the Maximum Weight Clique (MWC) problem. Firstly we design a novel scoring function to measure the benefits of a local move. Then we develop a Cycle Estimation based ReStart (CERS) strategy to resolve the cycling issue in the local search process. Experimental results show that our solver achieves state-of-the-art performances on the large sparse graphs as well as large dense graphs. Also we present a theorem which shows the necessity of the restart strategies in current state-of-the-art local search algorithms.

The maximum edge weight clique (MEWC) problem is important in both theories and applications. During last decades, there has been much interest in finding optimal or near-optimal solutions to this problem. Many existing heuristics focuses on academic benchmarks of relatively small size. However, very little attention has been paid to solving the MEWC problem in large sparse graphs. In this work, we exploit the so-called deterministic tournament selection (DTS) strategy to improve the local search MEWC algorithms. Experiments conducted on a broad range of large sparse graphs show that our algorithm outperforms state-of-the-art local search algorithms in this benchmark. Moreover it finds better solutions on a list of them.

The problem of finding a minimum vertex cover (MinVC) in a graph is a prominent NP-hard problem of great importance in both theory and application. During recent decades, there has been much interest in finding optimal or near-optimal solutions to this problem. Many existing heuristic algorithms for MinVC are based on local search strategies. Recently, an algorithm called FastVC takes a first step towards solving the MinVC problem for large real-world graphs. However, FastVC may be trapped by local minima during the local search stage due to the lack of suitable diversification mechanisms. In this work, we design a new random walk strategy to help FastVC escape from local minima. Experiments conducted on a broad range of large real-world graphs show that our algorithm outperforms state-of-the-art algorithms on most classes of the benchmark and finds smaller vertex covers on a considerable portion of the graphs.

The Maximum Vertex Weight Clique (MVWC) problem is a generalization of the Maximum Clique problem, which exists in many real-world applications. However, it is NP-hard and also very difficult to approximate. In this paper we developed a local search MVWC solver to deal with large sparse instances. We first introduce random walk into the multi-neighborhood greedy search, and then implement the algorithm with efficient data structures. Experimental results showed that our solver significantly outperformed state-of-the-art local search MVWC solvers. It attained all the best-known solutions, and found new best-known solutions on some instances.

Finding minimum vertex covers (MinVC) for simple undirected graphs is a well-known NP-hard problem. In the literature there have been many heuristics for obtaining good vertex covers. However, most of them focus on solving this problem in relatively small graphs. Recently, a local search solver called FastVC is designed to solve the MinVC problem on real-world massive graphs. Since the traditional best-picking heuristic was believed to be of high complexity, FastVC replaces it with an approximate best-picking strategy. However, since best-picking has been proved to be powerful for a wide range of problems, abandoning it may be a great sacrifice. In this paper we have developed a local search MinVC solver which utilizes best-picking with noise to remove vertices. Experiments conducted on a broad range of real-world massive graphs show that our proposed method finds better vertex covers than state-of-the-art local search algorithms on many graphs.

The Multi-Agent Programming Contest is to stimulate research in the area of multi-agent systems. In 2012, for the first time, a team from Sun Yat-sen University, Guangzhou, China, participated in the contest. The team is called AiWYX, and consists of a single member, who has just finished his undergraduate study. The system mainly exploits three strategies: strengthening action preconditions, task allocation optimization, and surrounding larger zones with shorter boundaries. With these strategies, our team is able to conquer large zones as early as possible, optimize collaboration, and ensure efficiency. The system was implemented in C++, and in this paper, we will introduce the design and architecture of AiWYX, and discuss the algorithms and implementations for these strategies.

The Multi-Agent Programming Contest is held every year to stimulate research in the area of multi-agent systems. Our system mainly exploits four strategies: prior state reasoning, task allocation optimization, dijkstra with recharge and surrounding several stable and valuable zones with shorter boundaries. With these strategies, our team is able to conquer several large zones as early as possible, optimize collaboration, and ensure efficiency. The system was implemented in C++, and in this paper, we will introduce the design and architecture of AiWXX, and discuss the algorithms and implementations for these strategies.