[1] Lee, K., Jung, W. S., Park, J. S., & Choi, M. Y. (2008). Statistical analysis of the Metropolitan Seoul Subway System: Network structure and passenger flows. Physica A: Statistical Mechanics and its Applications, 387(24), 6231-6234.
- Descriptive analysis of the network regarding to network distance
- Descriptive analysis of the network regarding to physical distance
[2] Zhang, J., Xu, X., Hong, L., Wang, S., & Fei, Q. (2011). Networked analysis of the Shanghai subway network, in China. Physica A: Statistical Mechanics and its Applications, 390(23-24), 4562-4570.
- Descriptive analysis of the network regarding to network distance
[3] Chen, S., Claramunt, C., & Ray, C. (2014). A spatio-temporal modelling approach for the study of the connectivity and accessibility of the Guangzhou metropolitan network. Journal of Transport Geography, 36, 12-23.
- Descriptive analysis of the network regarding to network distance
- Descriptive analysis of the network regarding to travel time
- Potential accessibility model using passenger data
[4] Stewart, J. Q. (1941). An inverse distance variation for certain social influences. Science, 93(2404), 89-90.
- Potential accessibility model first suggested here
[5] Salze, P., Banos, A., Oppert, J. M., Charreire, H., Casey, R., Simon, C., ... & Weber, C. (2011). Estimating spatial accessibility to facilities on the regional scale: an extended commuting-based interaction potential model. International journal of health geographics, 10(1), 1-16.
- Potential accessibility model of whcih parameters are estimated via regression
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Boosted Tree와 SVM을 이용한 승차인원 분석. 강남역의 특징, 사당역의 특징 등을 간략히 제시하고, 월별로 데이터를 뽑아내 학생들이 등교하는 월에 이용자 수가 많으며 출퇴근 시간에 이용자수가 많은 것을 정리함.
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"지하철 한 량"에 타고 있는 승객 수를 통해 Network Flow의 분석을 시도한 듯하나 뭔가 문제가 있다고만 나오고 결론이 없음
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"비가 오니 차가 막히겠다", 날씨와 대중교통 이용률간의 관계 제시