![]() In addition, Hangzhou, Shangyu and ChunAn have always been aggregation centres. The results show that in the past 3,000 years, the ancient water wells there have experienced an evolution from 'single-core' to 'multicore' aggregation, and the scope has gradually shifted from northern Zhejiang to southern Zhejiang. Therefore, we show that a spatiotemporal kernel density estimation (STKDE) model and the centre-of-gravity method are useful for studying the spatiotemporal evolution of ancient water wells over the past 3,000 years in Zhejiang Province of southern China. However, at present, there are few reports to quantitatively explore the spatiotemporal evolution of ancient water wells from the perspective of geography. The identification of spatiotemporal patterns of ancient water wells is a key to understanding the relationship between ancients and water, the evolution of ancient settlement patterns, and the history of population migration. Water wells are very important in the history of human development. Also, the prediction results show that the dynamic Bayesian model has better prediction effect than the static Bayesian model for the same sample data. The overall prediction accuracy of this model is 84.9%, the crash prediction accuracy is 60.8%, and the non-crash prediction accuracy is 92.3%. Finally, based on the three variables, the dynamic Bayesian network model for highway traffic crash risk prediction is proposed. Thereafter, the downstream mean speed (ASD1D2), the upstream mean occupancy (AOU1U2), and the speed difference (DSU1D1) on the nearest detector were determined as the explanatory variables of the crash risk prediction model. Then, the random forest model is used to screen several traffic flow variables that affect the highway crash risk. ![]() Secondly, the multiparameter fusion clustering analysis method is used to indicate that the sample data of different time series have different effects on the crash risk. Firstly, the case-control sample analysis method is used to extract 6 time series sample data composed of crash traffic flow data and corresponding non-crash traffic flow data for crash risk analysis and prediction. A highway traffic crash risk prediction method considering temporal correlation characteristics is proposed in this research. While the steady march of methodological innovation (including recent applications of random parameter and finite mixture models) has substantially improved our understanding of the factors that affect crash-frequencies, it is the prospect of combining evolving methodologies with far more detailed vehicle crash data that holds the greatest promise for the future.Ĭrash risk analysis and prediction are considered the premise of highway traffic safety control, which directly affects the accuracy and effectiveness of traffic safety decisions. This paper provides a detailed review of the key issues associated with crash-frequency data as well as the strengths and weaknesses of the various methodological approaches that researchers have used to address these problems. However, in the absence of detailed driving data that would help improve the identification of cause and effect relationships with individual vehicle crashes, most researchers have addressed this problem by framing it in terms of understanding the factors that affect the frequency of crashes – the number of crashes occurring in some geographical space (usually a roadway segment or intersection) over some specified time period. Gaining a better understanding of the factors that affect the likelihood of a vehicle crash has been an area of research focus for many decades.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |