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Road traffic accidents occur frequently and seriously in the accident-prone spots. In order to improve the safety and efficiency of road traffic, it is necessary to find the location of accident-prone points. The existing density clustering algorithm needed to set the number of center points and was easy to expand the clustering range when identifying traffic accident-prone points, this paper proposed the limit cluster expansion and adaptive search clustering by fast search and find of density peaks (LA-CFDP) algorithm to solve these problems. LA-CFDP algorithm automatically determined the number of center points by increasing the restriction condition of center points, and introduced the parameter expansion factor to limit the cluster expansion range, so as to improve the adaptability and accuracy of the algorithm for accident-prone point identification. The case analysis on the 2019 traffic accident data set of four cities in United Kingdom shows that the Sihouette coefficient of the clustering results of LA-CFDP algorithm reaches 0.72～0.92, and the Davies-Bouldin index (DBI) are all reduced to below 0.37. The clustering results accord with the definition and characteristics of accident-prone spots, and can provide reliable basis for the management of accident-prone spots. [ABSTRACT FROM AUTHOR]

Since the first launch of the new engineering contract (NEC) in 1993, early warning of problems has been widely recognized as an important approach of proactive management during a construction or engineering project. Is early warning really effective for the improvement of problem solving and project performance? This is a research question that still lacks a good answer. For this reason, an empirical investigation was made in the United Kingdom (U.K.) to answer the question. This study adopts a combination of literature review, expert interview, and questionnaire survey. Nearly 100 questionnaire responses were collected from the U.K. construction industry, based on which the use of early warning under different forms of contract is compared in this paper. Problem solving and project performance are further compared between the projects using early warning and the projects not using early warning. The comparison provides clear evidence for the significant effect of early warning on problem solving and project performance in terms of time, cost, and quality. Subsequently, an input-process-output model is developed in this paper to explore the relationship among early warning, problem solving, and project performance. All these help construction researchers and practitioners to better understand the role of early warning in ensuring project success. [ABSTRACT FROM AUTHOR]