2 research outputs found
Interpretable Machine Learningμ νμ©ν ꡬκ°λ¨μμμ€ν μ€μΉμ λ°λ₯Έ μΈλͺ νΌν΄μ¬κ³ κ°μ ν¨κ³Ό μ°κ΅¬
νμλ
Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν 건μ€ν경곡νλΆ, 2020. 8. κΉλκ·.In this study, a prediction model for casualty crash occurrence was developed considering whether to install SSES and the effect of SSES installation was quantified by dividing it into direct and indirect effects through the analysis of mediation effect. Also, it was recommended what needs to be considered in selecting the candidate sites for SSES installation. For this, crash prediction model was developed by using the machine learning for binary classification based on whether or not casualty crash occurred and the effects of SSES installation were analyzed based on crashes and speed-related variables. Especially, the IML methodology was applied that considered the predictive performance as well as the interpretability of the forecast results as important. When developing the IML which consisted of black-box and interpretable model, KNN, RF, and SVM were reviewed as black-box model, and DT and BLR were reviewed as interpretable model. In the model development, the hyper-parameters that could be set in each methodology were optimized through k-fold cross validation. The SVM with a polynomial kernel trick was selected as black-box model and the BLR was selected as interpretable model to predict the probability of casualty crash occurrence.
For the developed IML model, the evaluation was conducted through comparison with the typical BLR from the perspective of the PDR framework. The evaluation confirmed that the results of the IML were more excellent than the typical BLR in terms of predictive accuracy, descriptive accuracy, and relevancy from a human in the loop.
Using the result of IML's model development, the effect on SSES installation were quantified based on the probability equation of casualty crash occurrence. The equation is the logistic function that consists of SSES, SOR, SV, TVL, HVR, and CR. The result of analysis confirmed that the SSES installation reduced the probability of casualty crash occurrence by about 28%. In addition, the analysis of mediation effects on the variables affected by installing SSES was conducted to quantify the direct and indirect effects on the probability of reducing the casualty crashes caused by the SSES installation. The proportion of indirect effects through reducing the ratio of exceeding the speed limit (SOR) was about 30% and the proportion of indirect effects through reduction of speed variance (SV) was not statistically significant at the 95% confidence level.
Finally, the probability equation of casualty crash occurrence developed in this study was applied to the sections of Yeongdong Expressway to compare the crash risk section with the actual crash data to examine the applicability of the development model. The analysis result verified that the equation was reasonable. Therefore, it may be considered to select dangerous sites based on casualty crash and speeding firstly, and then to install SSES at the section where traffic volume (TVL), heavy vehicle ratio (HVR), and curve ratio (CR) are higher than the other sections.λ³Έ μ°κ΅¬μμλ ꡬκ°λ¨μμμ€ν
(Section Speed Enforcement System, SSES) μ€μΉ ν¨κ³Όλ₯Ό μ λννκΈ° μν΄ μΈλͺ
νΌν΄μ¬κ³ μμΈ‘λͺ¨νμ κ°λ°νκ³ , 맀κ°ν¨κ³Ό λΆμμ ν΅ν΄ SSES μ€μΉμ λν μ§μ ν¨κ³Όμ κ°μ ν¨κ³Όλ₯Ό ꡬλΆνμ¬ μ λννμλ€. λν, κ°λ°ν μμΈ‘λͺ¨νμ λν κ³ μλλ‘μμμ μ μ© κ°λ₯μ±μ κ²ν νκ³ , SSES μ€μΉ λμμ§ μ μ μ κ³ λ €ν΄μΌν μ¬νμ μ μνμλ€. λͺ¨ν κ°λ°μλ μΈλͺ
νΌν΄μ¬κ³ λ°μ μ¬λΆλ₯Ό μ’
μλ³μλ‘ νλ μ΄μ§λΆλ₯ν κΈ°κ³νμ΅μ νμ©νμμΌλ©°, κΈ°κ³νμ΅ μ€μμλ λͺ¨νμ μμΈ‘ μ±λ₯κ³Ό λλΆμ΄ μμΈ‘ κ²°κ³Όμ λν ν΄μλ ₯μ μ€μνκ² κ³ λ €νλ μΈν°ν리ν°λΈ λ¨Έμ λ¬λ(Interpretable Machine Learning, IML) λ°©λ²λ‘ μ μ μ©νμλ€.
IMLμ λΈλλ°μ€ λͺ¨λΈκ³Ό μΈν°ν리ν°λΈ λͺ¨λΈλ‘ ꡬμ±λλ©°, λ³Έ μ°κ΅¬μμλ λΈλλ°μ€ λͺ¨λΈλ‘ KNN, RF λ° SVMμ, μΈν°ν리ν°λΈ λͺ¨λΈλ‘ DTμ BLRμ κ²ν νμλ€. λͺ¨ν κ°λ° μμλ κ° κΈ°λ²μμ νλμ΄ κ°λ₯ν νμ΄νΌ νλΌλ―Έν°μ λνμ¬ κ΅μ°¨κ²μ¦ κ³Όμ μ κ±°μ³ μ΅μ ννμλ€. λΈλλ°μ€ λͺ¨λΈμ ν΄λ¦¬λ
Έλ―ΈμΌ 컀λ νΈλ¦μ νμ©ν SVMμ, μΈν°ν리ν°λΈ λͺ¨λΈμ BLRμ μ μ©νμ¬ μΈλͺ
νΌν΄μ¬κ³ λ°μ νλ₯ μ μμΈ‘νλ λͺ¨νμ κ°λ°νμλ€. κ°λ°λ IML λͺ¨λΈμ λν΄μλ PDR(Predictive accuracy, Descriptive accuracy and Relevancy) νλ μμν¬ κ΄μ μμ (typical) BLR λͺ¨λΈκ³Ό λΉκ΅ νκ°λ₯Ό μ§ννμλ€. νκ° κ²°κ³Ό μμΈ‘ μ νλ, ν΄μ μ νλ λ° μΈκ°μ μ΄ν΄κ΄μ μμμ μ ν©μ± λ±μμ λͺ¨λ IML λͺ¨λΈμ΄ μ°μν¨μ νμΈνμλ€.
λν, λ³Έ μ°κ΅¬μμ κ°λ°λ IML λͺ¨λΈ κΈ°λ°μ μΈλͺ
νΌν΄μ¬κ³ λ°μ νλ₯ μμ SSES, SOR, SV, TVL, HVR λ° CRμ λ
립λ³μλ‘ κ΅¬μ±λμμΌλ©°, μ΄ νλ₯ μμ κΈ°λ°μΌλ‘ SSES μ€μΉμ λν ν¨κ³Όλ₯Ό μ λννμλ€. μ λν λΆμ κ²°κ³Ό, SSES μ€μΉλ‘ μΈν΄ μ½ 28% μ λμ μΈλͺ
νΌν΄μ¬κ³ λ°μ νλ₯ μ΄ κ°μν¨μ νμΈν μ μμλ€. λν, λͺ¨ν κ°λ°μ νμ©λ λ³μ μ€ SSES μ€μΉλ‘ μΈν΄ μν₯μ λ°λ λ³μλ€(SOR λ° SV)μ λν 맀κ°ν¨κ³Ό λΆμμ ν΅ν΄ SSES μ€μΉλ‘ μΈν μΈλͺ
νΌν΄μ¬κ³ κ°μ νλ₯ μ μ§μ ν¨κ³Όμ κ°μ ν¨κ³Όλ₯Ό ꡬλΆνμ¬ μ μνμλ€. λΆμ κ²°κ³Ό, SSESμ μ νμλ μ΄κ³ΌλΉμ¨(SOR)μ κ΄κ³μμ μμ΄μλ μ½ 30%κ° κ°μ ν¨κ³Όμ΄κ³ , SSESμ μλλΆμ°(SV)μ κ΄κ³μ μμ΄μλ 맀κ°ν¨κ³Όκ° ν΅κ³μ μΌλ‘ μ μνμ§ μμμ νμΈν μ μμλ€.
λ§μ§λ§μΌλ‘ μλκ³ μλλ‘λ₯Ό λμμΌλ‘ μΈλͺ
νΌν΄μ¬κ³ λ°μ νλ₯ μ κΈ°λ°μ μμΈ‘ μνꡬκ°κ³Ό μ€μ μΈλͺ
μ¬κ³ λ€λ° ꡬκ°μ λν λΉκ΅ λΆμμ ν΅ν΄ μ°κ΅¬ κ²°κ³Όμ νμ© κ°λ₯μ±μ νμΈνμλ€. λν, SSES μ€μΉ λμμ§ μ μ μμλ μ¬κ³ λ° μλ λΆμμ ν΅ν μνꡬκ°μ μ λ³ν ν κ΅ν΅λ(TVL)μ΄ λ§μ κ³³, ν΅κ³Όμ°¨λ μ€ μ€μ°¨λ λΉμ¨(HVR)μ΄ λμ κ³³ λ° κ΅¬κ° λ΄ κ³‘μ λΉμ¨(CR)μ΄ λμ κ³³μ μ°μ μ μΌλ‘ κ²ν νλ κ²μ μ μνμλ€.1. Introduction 1
1.1. Background of research 1
1.2. Objective of research 4
1.3. Research Flow 6
2. Literature Review 11
2.1. Research related to SSES 11
2.1.1. Effectiveness of SSES 11
2.1.2. Installation criteria of SSES 15
2.2. Machine learning about transportation 17
2.2.1. Machine learning algorithm 17
2.2.2. Machine learning algorithm about transportation 19
2.3. Crash prediction model 23
2.3.1. Frequency of crashes 23
2.3.2. Severity of crash 26
2.4. Interpretable Machine Learning (IML) 31
2.4.1. Introduction 31
2.4.2. Application of IML 33
3. Model Specification 37
3.1. Analysis of SSES effectiveness 37
3.1.1. Crashes analysis 37
3.1.2. Speed analysis 39
3.2. Data collection & pre-analysis 40
3.2.1. Data collection 40
3.2.2. Basic statistics of variables 42
3.3. Response variable selection 50
3.4. Model selection 52
3.4.1. Binary classification 52
3.4.2. Accuracy vs. Interpretability 53
3.4.3. Overview of IML 54
3.4.4. Process of model specification 57
4. Model development 59
4.1. Black-box and interpretable model 59
4.1.1. Consists of IML 59
4.1.2. Black-box model 60
4.1.3. Interpretable model 68
4.2. Model development 72
4.2.1. Procedure 72
4.2.2. Measures of effectiveness 74
4.2.3. K-fold cross validation 76
4.3. Result of model development 78
4.3.1. Result of black-box model 78
4.3.2. Result of interpretable model 85
5. Evaluation & Application 91
5.1. Evaluation 91
5.1.1. The PDR framework for IML 91
5.1.2. Predictive accuracy 93
5.1.3. Descriptive accuracy 94
5.1.4. Relevancy 99
5.2. Impact of Casualty Crash Reduction 102
5.2.1. Quantification of the effectiveness 102
5.2.2. Mediation effect analysis 106
5.3. Application for the Korean expressway 118
6. Conclusion 121
6.1. Summary and Findings 121
6.2. Further Research 125Docto
Utilization of carbohydrate reserves during tuber-sprouting and subsequent growth of Sagittaria trifolia L. and control of the growth
νμλ
Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :λνκ³Ό μλ¬Όνμ 곡,1998.Docto