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1 "A Cool Head" in the "Boom" of Big Data |
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1 | (48) |
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1 | (9) |
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1.1.1 Development of Big Data Technology |
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2 | (2) |
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1.1.2 Big Data Mark the Ascent Stage for Promoting Research Mode Reform |
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4 | (2) |
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1.1.3 Application of Big Data Technology in the Transportation Field |
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6 | (4) |
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1.2 Reflections Over Development of Basic Theories of Transportation Engineering |
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10 | (9) |
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1.2.1 Foundational Role of Network Traffic Flow Analysis Theory |
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10 | (2) |
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1.2.2 Development of Transportation Behavior Analysis Theories |
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12 | (3) |
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1.2.3 Expectations for Future Development |
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15 | (4) |
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1.3 Pursuit and Confusion in the Context of Big Data |
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19 | (9) |
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1.3.1 Close Attention to New Research Paradigms |
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19 | (3) |
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1.3.2 Information Environment for Incomplete Big Data |
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22 | (3) |
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1.3.3 Cause Analysis of Troubles |
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25 | (3) |
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1.4 The Value of Big Data in Urban Transportation Analysis |
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28 | (9) |
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1.4.1 Advantages Furnished by Large Sample |
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28 | (2) |
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1.4.2 Information Obtained from Continuous Tracking |
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30 | (1) |
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1.4.3 Observation and Exploration Under Different Measures |
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31 | (3) |
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1.4.4 Complementation Under Multi-view Observation |
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34 | (3) |
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1.5 Reflections Over Big Data Analysis of Urban Transportation |
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37 | (12) |
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1.5.1 Problem-Oriented Technological Demands |
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38 | (2) |
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1.5.2 "Perception, Cognition and Insight" Based on Big Data |
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40 | (2) |
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1.5.3 Problem Representation of "Multi-dimensional Integration" |
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42 | (2) |
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44 | (5) |
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2 Urban Transportation Monitoring Based on Concept of Complex Adaptive System |
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49 | (52) |
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2.1 Correct Understanding for Probability of Urban Transportation Evolution |
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49 | (8) |
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2.1.1 Significances in Nonlinearity of Urban Transportation |
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50 | (2) |
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2.1.2 Strategic Regulation of Urban Transportation |
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52 | (2) |
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2.1.3 Timely Response Countermeasure Modes in the Face of Probability |
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54 | (3) |
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2.2 Integrating Monitoring into New Technological Conceptual Framework |
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57 | (5) |
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2.2.1 Basic Concept of Complex Adaptive System |
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57 | (3) |
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2.2.2 System Monitoring Tasks After Changes in Technological Concepts |
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60 | (2) |
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2.3 Representations of Behavioral Agent Patterns Based on Clustering and Classification |
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62 | (14) |
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2.3.1 Representations of Differences in Subjective Attributes of Behavioral Agent |
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63 | (3) |
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2.3.2 Identification and Characterization of Differences in Individual Behavior Representations |
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66 | (3) |
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2.3.3 User Response to New Transportation Service Modes |
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69 | (4) |
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2.3.4 Behavior Detection of Transportation Service Providers |
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73 | (3) |
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2.4 Macrostate Monitoring of Urban Transportation System |
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76 | (5) |
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2.4.1 Reduced Data Through Mode Classification |
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78 | (2) |
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2.4.2 Measurement of Urban Spatial Connection Conditions |
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80 | (1) |
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2.5 Reflections Over Emergence |
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81 | (12) |
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2.5.1 Discovery of Coupling Characteristics of Road Network Traffic State |
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88 | (3) |
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2.5.2 Characteristics of Settlement Patches in the Process of Urban Space Expansion |
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91 | (2) |
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2.6 Changes in Spatial Connection Structure of Urban Agglomerations |
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93 | (8) |
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99 | (2) |
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3 Inheritance and Revolution of Technology |
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101 | (36) |
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3.1 Transportation Decision-Making Support Data Resources Generated from Informatization |
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101 | (10) |
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3.1.1 Progressively-Improved Transportation System State Monitoring Network |
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101 | (5) |
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3.1.2 "Electronic Footprints" Collected by Information Service Systems |
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106 | (3) |
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3.1.3 Semantic Information Utilization in the Internet |
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109 | (2) |
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3.2 Blend of New Data Resources and Traditional Technology Concepts |
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111 | (12) |
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3.2.1 Application Practices of Urban Transportation Big Data in China |
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112 | (6) |
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3.2.2 Outreaching of Detection Methods Based on Traditional Technological Concepts |
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118 | (2) |
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3.2.3 Using Correlation as a Bridge for Traditional Technology Concepts |
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120 | (3) |
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3.3 Technological Concept Revolution in Echo with Data Environment |
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123 | (14) |
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3.3.1 Evidence-Based Decision-Making Analysis Technology Under Big Data Environment |
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123 | (3) |
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3.3.2 A Technical Framework of Nested Analysis at Macro and Micro Levels |
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126 | (2) |
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3.3.3 Finding More Suitable Ways of Expression |
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128 | (2) |
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3.3.4 Grasping Differences with the Help of Clustering |
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130 | (2) |
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3.3.5 Blazing the Path to Cognition Through Association |
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132 | (1) |
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3.3.6 Exploring Causality Through Comparative Studies |
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133 | (2) |
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135 | (2) |
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4 Feature Extraction, Cluster Analysis and Object Representation |
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137 | (50) |
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4.1 "Regularity" in "Numerous and Complicated Appearance" |
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137 | (14) |
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4.1.1 Group Characteristics Hidden in Individual Diversity |
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138 | (6) |
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4.1.2 Classification and Identification in the Context of Incomplete Information |
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144 | (4) |
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4.1.3 Revealing Underlying Law in Time Changes |
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148 | (3) |
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4.2 Multidimensional Characteristic Attributes of Behavioral Agents |
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151 | (11) |
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4.2.1 Activity Characterization in Different Forms |
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153 | (6) |
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4.2.2 Activity Pattern Classification Based on Trip Chain |
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159 | (3) |
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4.3 Clustering Analysis Based on Attribute Characteristics |
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162 | (11) |
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4.3.1 Subdivision of Research Objects Through Clustering |
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164 | (2) |
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4.3.2 Cluster Analysis of Habitual Behavior Patterns |
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166 | (3) |
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4.3.3 Clustering Analysis Based on Association Attributes |
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169 | (4) |
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4.4 Comparative Studies Based on Categorization |
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173 | (8) |
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4.4.1 Characteristics Comparison Between Categories |
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173 | (3) |
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4.4.2 Comparison for Location Point Distribution Based on Categorization |
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176 | (5) |
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4.5 Transforming Attribute Characteristics into Data Language for Crossover Communication |
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181 | (6) |
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4.5.1 Data Model as a Bridge for Crossover Communication- |
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181 | (2) |
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4.5.2 Work Collaboration with the Help of Multi-source Stream Mode Framework |
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183 | (3) |
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186 | (1) |
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5 Association and Correlation Analysis |
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187 | (42) |
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5.1 Understanding of Connections Through Association Analysis |
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188 | (9) |
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5.1.1 Relationship Between Spatial Features |
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188 | (2) |
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5.1.2 In-Depth Thinking Through Connections |
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190 | (3) |
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5.1.3 Spatial Association of Traffic Zoning |
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193 | (4) |
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5.2 Individual Attribute-Spatial Association Analysis Based on Big Data |
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197 | (7) |
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5.2.1 Classification of Vehicle Usage Categories Based on License Plate Data |
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198 | (3) |
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5.2.2 Discussion About Spatial Distribution of Cluster Structure |
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201 | (3) |
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5.3 Problem Conversion with the Help of Association Analysis |
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204 | (7) |
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5.3.1 Extenics Mindset and Problem Conversion |
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204 | (3) |
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5.3.2 Identifying Abnormal Events with the Help of Associated Attributes |
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207 | (4) |
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5.4 Generalization and Summarization of Problems Based on Association Analysis |
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211 | (18) |
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5.4.1 Asking Questions and Defining Analysis Tasks Based on Data |
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211 | (5) |
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5.4.2 Improving Data Resolution and Creating Conditions for Correlation Analysis |
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216 | (4) |
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5.4.3 Problem Classification Based on Associated Features |
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220 | (6) |
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226 | (3) |
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6 Information Fusion and Construction of Evidence Collection |
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229 | (40) |
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6.1 Technological Integration of Information Fusion and Evidence Theory |
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229 | (6) |
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6.1.1 Judgment Based on Indirect Evidence |
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229 | (2) |
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6.1.2 Information Fusion Within the Framework of Evidence System |
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231 | (2) |
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6.1.3 Judgment Synthesis Based on Evidence Theory |
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233 | (2) |
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6.2 Judging Credibility of Information Through Complementary Data Resources |
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235 | (11) |
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6.2.1 Credibility Test of Metro Usage Information Extracted from Mobile Phone Data |
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235 | (2) |
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6.2.2 Improving Quality of Bus Ride Location Information Through Multi-Source Data |
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237 | (4) |
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6.2.3 Bus Commuter Identification with the Help of Data Fusion |
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241 | (5) |
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6.3 Information Fusion in the Process of Intelligence Decision-Making |
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246 | (23) |
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6.3.1 Intelligence Decision-Making in the Process of Evidence Extraction |
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247 | (1) |
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6.3.2 Authenticity Assessment Based on Multi-Source Intelligence Comparison |
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248 | (8) |
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6.3.3 Association Analysis Based on Multi-Source Data |
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256 | (10) |
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266 | (3) |
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7 Nested Analysis of Big Data and Small Sample Data |
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269 | (38) |
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7.1 Exploratory Research for Construction of Task Framework |
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269 | (8) |
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7.1.1 Clarifying Topics Through Core Concepts |
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270 | (1) |
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7.1.2 Advancing Understanding Through Small Sample Analysis |
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271 | (5) |
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7.1.3 Macro and Micro Nested Bus Customer Management Analysis Framework |
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276 | (1) |
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7.2 Analysis of Macro Structure of Bus Passengers Based on Smart Card Data |
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277 | (4) |
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7.2.1 Characteristic Indexes Used for the Identification of Bus Usage Behavior Patterns |
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277 | (2) |
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7.2.2 Group Division of Smart Card Users |
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279 | (2) |
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7.3 Micro-Mechanism Analysis Based on Questionnaire Survey |
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281 | (15) |
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7.3.1 Mechanism Analysis Framework Based on User Loyalty |
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282 | (1) |
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7.3.2 Setting the Relationship Between Variables in Measurement Model |
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283 | (2) |
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7.3.3 Questionnaire Survey for Bus Commuters in Xiamen City |
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285 | (4) |
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7.3.4 Exploring Micro-Mechanism Through Structural Equation Model |
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289 | (7) |
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7.4 Integrating Macro and Micro Data to Make Clear the Priorities for Improvement |
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296 | (11) |
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7.4.1 Establishing Bonds Between Smart Card Data and Questionnaire Survey Data |
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297 | (3) |
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7.4.2 Group Division Based on Integration of Macro and Micro Data |
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300 | (2) |
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7.4.3 Pinpointing Key Objects of Restructuring Work According to Time-space Distribution of Groups |
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302 | (3) |
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305 | (2) |
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8 Conclusions and Reflections |
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307 | |
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8.1 Fruit--Profounder Understandings |
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307 | (4) |
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8.2 Outcome--Ever-Increasingly Mature Technologies |
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311 | (2) |
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8.3 Recognition--Proven Conclusions |
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313 | (2) |
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8.4 Reflections--The Road Ahead |
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315 | |
Lisainfo e-raamatute kohta