A New Method Based on Association Rules Mining and Geo-filter for Mining Spatial Association Knowledge

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.

Driving rule extraction based on cognitive behavior analysis

In order to make full use of the driver’s long-term driving experience in the process of perception, interaction and vehicle control of road traffic information, a driving behavior rule extraction algorithm based on artificial neural network interface(ANNI) and its integration is proposed. Firstly, based on the cognitive learning theory, the cognitive driving behavior model is established, and then the cognitive driving behavior is described and analyzed. Next, based on ANNI, the model and the rule extraction algorithm(ANNI-REA) are designed to explain not only the driving behavior but also the non-sequence. Rules have high fidelity and safety during driving without discretizing continuous input variables. The experimental results on the UCI standard data set and on the self-built driving behavior data set, show that the method is about 0.4% more accurate and about 10% less complex than the common C4.5-REA, Neuro-Rule and REFNE. Further, simulation experiments verify the correctness of the extracted driving rules and the effectiveness of the extraction based on cognitive driving behavior rules. In general, the several driving rules extracted fully reflect the execution mechanism of sequential activity of driving comprehensive cognition, which is of great significance for the traffic of mixed traffic flow under the network of vehicles and future research on unmanned driving.

An overview of solutions to the bus bunching problem in urban bus systems

Bus bunching has been a persistent issue in urban bus system since it first appeared,and it remains a challenge not fully resolved.This phenomenon may reduce the operational efficiency of the urban bus system,which is detrimental to the operation of fast-paced public transport in cities.Fortunately,extensive research has been undertaken in the long development and optimization of the urban bus system,and many solutions have emerged so far.The purpose of this paper is to summarize the existing solutions and serve as a guide for subsequent research in this area.Upon careful examination of current findings,it is found that,based on the different optimization objects,existing solutions to the bus bunching problem can be divided into five directions,i.e.,operational strategy improvement,traffic control improvement,driver driving rules improvement,passenger habit improvement,and others.While numerous solutions to bus bunching are available,there remains a gap in research exploring the integrated application of methods from diverse directions.Furthermore,with the development of autonomous driving,it is expected that the use of modular autonomous vehicles could be the most potential solution to the issue of bus bunching in the future.