Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. RULE EXTRACTION BASED TECHNIQUES <s> Rough Set Theory (RST) is an induction based decision-making technique, which can extract useful information from attribute-value (decision) table. This study introduces RST into pavement management system (PMS) for maintenance and rehabilitation (M&R) strategy induction. An empirical study is conducted by using the pavement distress data collected from 7 county roads by experienced pavement engineers of Taiwan Highway Bureau (THB). For each road section, the severity and coverage of existing distresses and required M&R treatment were separately recorded. The analytical database consisting of 2,348 records (2,000 records for rule induction, and 348 records for rule testing) are established to induce M&R strategies. On the basis of the testing results, total accuracy and total coverage for the induced strategies are as high as 88.7% and 84.2% respectively, which illustrates that RST certainly can reduce distress types and remove redundant records to induce the proper M&R strategies. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. RULE EXTRACTION BASED TECHNIQUES <s> This paper presents a novel non-parametric methodology - rough set theory - for accident occurrence exploration. The rough set theory allows researchers to analyze accidents in multiple dimensions and to model accident occurrence as factor chains. Factor chains are composed of driver characteristics, trip characteristics, driver behavior and environment factors that imply typical accident occurrence. A real-world database (2003 Taiwan single auto-vehicle accidents) is used as an example to demonstrate the proposed approach. The results show that although most accident patterns are unique, some accident patterns are significant and worth noting. Student drivers who are young and less experienced exhibit a relatively high possibility of being involved in off-road accidents on roads with a speed limit between 51 and 79 km/h under normal driving circumstances. Notably, for bump-into-facility accidents, wet surface is a distinctive environmental factor. <s> BIB002 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. RULE EXTRACTION BASED TECHNIQUES <s> This paper describes the application of data mining methods in the database of the DORIS transportation information system, currently used by the Prague Public Transit Company. The goal is to create knowledge about the behavior of objects within this information system. Data is analyzed partly with the help of descriptive statistical methods, and partly with the help of association rules, which may discover common combinations of attributes that occur most frequently within a given data set. Two types of quantifiers were used when creating the association rules; namely “founded implication” and “above average”. The results of the analysis are presented in the form of graphs and hypotheses. <s> BIB003 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> C. RULE EXTRACTION BASED TECHNIQUES <s> Over the past decades, Intelligent Transportation Systems (ITS) have developed and deployed in order to improve trans-portation safety and mobility, reduces environmental impact, promotes sustainable transportation development and enhances productivity. ITS combines high technology and improvements in information systems, communication, sensors, controllers and advanced mathematical methods with the conventional world of transportation infrastructure. As an inter-disciplinary research field, it is difficult to have a clear picture about the whole system for a novice researcher. This paper 1) proposes an ITS architecture, which is based on the requirement and technology at the present stage, 2) sort out the bottleneck issue that lie behind intelligent transportation research, 3) explores the future prospect of the ITS and research focus as new technologies become available. <s> BIB004
One of the important features for learning-driven moving object models is to understand the pattern, association, and correlation among various datasets acquired through different sources. The objective is typically achieved using association rule mining (ARM). Lin et al. BIB004 utilize the association analysis with ARM for the prediction of network flow. Similarly, ARM is used for the classification of network flow anomalies in  . Another technique for rule extraction is based on rough set theory (RST) BIB001 , BIB002 , BIB003 . VOLUME 6, 2018 RST extracts useful data from decision table using induction based decision-making techniques. BD is analyzed using statistical methods and association rule in order to identify the common attributes that appear with high frequency. One of the advantages of using RST is to extract important attributes without having any information outside the dataset.