5 research outputs found
Passenger Flows in the Metropolitan Seoul Public Transportation: Maximum Spanning Tree and Community Detection
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Έλλ€λ‘ ꡬλΆλ μ§μμ¬νλ₯Ό ꡬνμλ€. λ¨μΌ μ°κ²° ν©μΉκΈ° λ°©λ²κ³Ό λΆν λ°λ μ΅λνλ₯Ό ν΅ν΄ ꡬλΆλ λ§ν¬λ€μ μ§μμ¬νλ₯Ό ꡬνμ¬ μ€μ²©λ μ§μμ¬νλ₯Ό λ°κ²¬νμλ€. ν μ§μμ¬ν μμ λ
Έλ μμ λ§ν¬ μμ λΆν¬λ μμ κ±°λμ κ³± λ²μΉμ λ°λ₯Έλ€. μ΄ μ§μμ¬νλ€μ μ¬λλ€μ μ€μ μ΄λ μλ£λ₯Ό ν΅ν΄ μ°Ύμλ€λ μΈ‘λ©΄μμ μ€μ μμΈ μλκΆμ μ§μμ¬ν νΉμ±μ 보μ¬μ€λ€κ³ μκ°ν μ μλ€.In this thesis, passenger flow of Metropolitan Seoul public transportation system is examined through maximum spanning tree and community detection. The public transportation system, consisting bus and subway, provides major transportation modes to the people. The characteristic of movement of people can be analyzed by the passenger flow data of public transportation system. We divide one station by departure station and arrival station and construct maximum spanning tree of the passenger flow. The degree distribution of the departure stations and arrival stations in the maximum spanning tree follows power law with different exponent according to the time zones. We also investigate the community structure of Metropolitan Seoul. The bus and subway stations are coarse grained by square grid and the modularity maximization method using simulated annealing is employed first to find disjoint node(square area) communities. The disjoint link community, using single-linkage agglomerate method and partition density maximization, is also found to reveal overlapped communities. The distribution of number of links and nodes per community in the disjoint link community also follows power law distribution. These communities can be regarded showing real community character of Metropolitan Seoul in the sense of the lexical meaning of community.1 Introduction 1
2 Maximum Spanning Trees of Passenger Flow 4
2.1 Introduction 4
2.2 Method 5
2.3 Results 6
3 Community Detection 14
3.1 Introduction 14
3.2 Method 16
3.2.1 Modularity maximization by simulated annealing 16
3.2.2 Overlapped community detection: Disjoint link community 17
3.3 Results 18
3.3.1 Modularity maximization by simulated annealing 18
3.3.2 Overlapped community detection: Disjoint link community 19
4 Summary 27Maste
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