2 research outputs found
μ¬μ μ§νλ₯ κ³Ό μ¬μ μ±κ³Όλͺ©ν λ¬μ±λ₯ μ μ€μ¬μΌλ‘
νμλ
Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : νμ λνμ νμ νκ³Ό(μ μ±
νμ 곡), 2023. 2. κ³ κΈΈκ³€.곡곡λΆλ¬Έμ μμ° λΆμ©μ λ°λΌλ³΄λ κ·Έκ°μ μ£Όμ λ
Όμμ λ°λΌ, μμ° λΆμ©μ μ¬μ λ―Όμ£Όμ£Όμμ νΌμμ΄μ, λ°°λΆμ Β·κΈ°μ μ μΈ‘λ©΄μμ λΉν¨μ¨μ μΈ μ¬μ μ΄μ©μ κ²°κ³Όλ‘ μΈμλμ΄ μλ€. κ·Έ κ²°κ³Ό μμ° μ‘°κΈ°μ§νμ μ±
, μμ° λΆμ©μ λ°λ₯Έ μμ° μκ° λ± μμ° λΆμ©μ μ΅μννκΈ° μν μ¬λ¬κ°μ§ μ λλ€μ΄ μκ²Όκ³ , μ¬μ
μ€λ¬΄μλ€μ μμ° λΆμ©μ λν λΆλ΄μ λμ± μ»€μ Έκ°λ€.
μμ° μ‘°κΈ°μ§ν λͺ©ν λ¬μ±μ μν΄, λ μμ° λΆμ©μ λ°λ₯Έ μ¬λ¬ μ λμ λΆμ΄μ΅μ ννΌνκΈ° μν΄ μ¬μ
μ€λ¬΄μμκ²λ λΉν¨μ¨μ Β·λΉν¨κ³Όμ μμ°μ§νμ λν μ μΈμ΄ λ°μνκ² λλ€. λ§μ½ κ·Έλ λ€λ©΄, μμ° λΆμ©μ κΈ°μ‘΄μ μ°κ΅¬μ κ°μ΄ μ¬μ μ΄μ© λΉν¨μ¨μ±μ μ§νλ‘ μΌλ κ²μ΄ μ μ νμ§, λ λ§μ½ λΉν¨μ¨μ Β·λΉν¨κ³Όμ μμ° μ§νμ΄ λ°μνλ€λ©΄, μ€νλ € μ¬μμ΄ λ³΄μ λλ μμ° λΆμ©μ΄ μλμ μΌλ‘ 건μ ν μμ° νμκ° μλμ§ μλ¬Έμ΄ λ λ€.
λ³Έ λ
Όλ¬Έμμλ μμ° λΆμ©μ μ¬μ μ΄μ©μ λΉν¨μ¨μ±μ μ§νλ‘ μΌλ κ²μ΄ μ μ νμ§ μ΄ν΄λ³΄κΈ° μν΄ μ°λλ³ μμ°μ§νλ₯ μ΄ μ¬μ μ±κ³Όλͺ©ν λ¬μ±λ₯ μ λΌμΉλ μν₯μ νμ
ν΄λ³΄μλ€. λν 1λΆκΈ°μ 4λΆκΈ°μ μμ°μ§νλ₯ μ΄ κ°κ° μ¬μ μ±κ³Όλͺ©ν λ¬μ±λ₯ μ μ΄λ ν μν₯μ λΌμΉλμ§ νμΈνμ¬, λΉν¨μ¨μ Β·λΉν¨κ³Όμ μμ°μ§νμ΄ λ°μνμ§λ μλμ§ νμΈν΄λ³΄μλ€.
μ°λλ³ μμ°μ§νλ₯ κ³Ό 1λΆκΈ°, 4λΆκΈ° μμ°μ§νλ₯ μ κ°κ° λ
립λ³μλ‘ μ€μ νμ¬ λΆμν κ²°κ³Ό, λ¨Όμ μ°λλ³ μμ°μ§νλ₯ μ μ¬μ μ±κ³Όλͺ©ν λ¬μ±λ₯ μ μ μν μν₯μ λΌμΉμ§ μλ κ²μΌλ‘ λνλ¬λ€. μ΄λ¬ν κ²°κ³Όλ μμ°λΆμ©μ μ¬μ μ΄μ© λΉν¨μ¨μ±μ μΈ‘μ νκΈ° μν μ§νλ‘ μΌλ κ²μ΄ μ μ νμ§ μμ μ μμμ 보μ¬μ€λ€.
1λΆκΈ° μμ°μ§νλ₯ μμ μ¬μ μ±κ³Όλͺ©ν λ¬μ±λ₯ μ μ μν μν₯μ λΌμΉμ§ μλ κ²μΌλ‘ λνλ λ°λ©΄, 4λΆκΈ° μμ°μ§νλ₯ μ μ¬μ μ±κ³Όλͺ©ν λ¬μ±λ₯ μ μ μν μ(+)μ μν₯μ λΌμΉλ κ²μΌλ‘ λνλ¬λ€. μ΄λ₯Ό ν΅ν΄ μμ° μ‘°κΈ°μ§νμ μ±
μΌλ‘ μΈν΄ μΌλΆ λΉν¨μ¨μ Β·λΉν¨κ³Όμ μμ° μ§νμ΄ λ°μνκ³ μμμ μ μΆν μ μλ€.
μ΄λ¬ν μ°κ΅¬μ κ²°κ³Όλ μμ° λΆμ©μ λ°λΌλ³΄λ κΈ°μ‘΄μ μκ°μ λ€μ νλ² μ΄ν΄λ³Ό νμμ±μ μ μν΄μ€λ€. μ¬μ μ‘°μ¬ λ° μ§ν κ³ν λΆμ¬, μΆ©μ€νμ§ λͺ»ν μμ° μ§ν λ±μ λΉν¨μ¨μ μΈ μ¬μ μ΄μ©μΌλ‘ μΈν΄ μμ° λΆμ©μ΄ λ°μν μ μλ€. νμ§λ§ λλΉμ μμ° μ§ν λ±μ λΉν¨μ¨μ μ¬μ μ΄μ©μΌλ‘ μΈν΄ μ€νλ € μμ° λΆμ©μ΄ κ°μν μλ μκΈ° λλ¬Έμ μ¬μ μ΄μ©μ ν¨μ¨μ±μ νλ¨νλ μ§νλ‘ μμ° λΆμ©μ μ ννλ κ²μ μ μ νμ§ μμ μ μλ€. μ€μ λ‘ μ°λλ³ μμ°μ§νλ₯ κ³Ό μ¬μ μ±κ³Όλͺ©ν λ¬μ±λ₯ μ΄ λΉλ‘νμ§ μλ λΆμ κ²°κ³Όλ₯Ό λ³Ό λ μ΄λ¬ν μ£Όμ₯μ νλΉμ±μ΄ λν΄μ§λ€.
μμ μΈκΈν λλΉμ μμ°μ§νμ΄ μ€μ λ‘ λ°μνλ κ² λν λΆμμ ν΅ν΄ νμΈν μ μμλ€. μ΄μ κ°μ λλΉμ μμ°μ§νμ κΈ°μ μ ν¨μ¨μ± μΈ‘λ©΄μμ λΉν¨μ¨μ μΈ μ¬μ μ΄μ©μ κ²°κ³Όμ΄μ, μ¬μ λ―Όμ£Όμ£Όμμ λ ν° νΌμμ λΆλ¬μΌμΌν¨λ€. μ€μ λ‘ μμ° λΆμ©μ μΈκ³μμ¬κΈμ ννλ‘ λ€λ₯Έ μ©λμ μ¬μ©λκ±°λ μ°¨κΈ° μ¬μμ μ΄μ
λλ€. νμ§λ§ λλΉμ μμ°μ§νμ μ§νμ λ°λ₯Έ μ±κ³Όλ₯Ό μ»μ§ λͺ»ν μ± μ¬μμ΄ μλ©Έλλ€λ μ μμ μμ° λΆμ©λ³΄λ€ λμ± λΉν¨μ¨μ μΈ μμ° νμμ΄μ, μ¬μ λ―Όμ£Όμ£Όμμ νΌμμ΄λΌκ³ λ³Ό μ μμΌλ©°, μ€νλ € μμ° λΆμ©μΌλ‘ λ¨κΈ°λ κ²μ΄ μλμ μΌλ‘ 건μ ν μμ° νμλΌκ³ λ³Ό μ μλ€.
μμ° λΆμ©μ λν μ λμ λΆμ΄μ΅μ λΆμμ©μΌλ‘ λ°μνλ λλΉμ μμ°μ§νμ λ°©μ§μ μ¬μ μ΄μ©μ ν¨μ¨μ± κ°μ μ μν΄μλ μμ° μ¬μ κΈ°κ΄μ μ λ¬Έμ± νλ³΄κ° νμμ μ΄λ©°, μ² μ ν λΆμμ λ°νμΌλ‘ ν νμ€μ μΈ μ‘°κΈ°μ§νλͺ©ν μ€μ , νκ°κΈ°μ€μΌλ‘μμ μμ° μ‘°κΈ°μ§νλͺ©ν, μ¬μ
μμ°μ§νλ₯ λ° λΆμ©μ‘ λͺ©νμ μ¬κ²ν κ° λλ°λμ΄μΌ ν κ²μ΄λ€. μ΄μ ν¨κ» μμ°μ§νκΈ°κ°μ μ°μ₯, λ€λ
λ μμ°μ λ, μμ°μ§νμ λν μ¬μ
λ΄λΉμμ μμ μ¬λκΆ κ°ν λ° μμ°μ μ½ μΈμΌν°λΈ νλ‘κ·Έλ¨μ λμ
λ±μ ν΅ν΄ μμ° μ§νμ λν μ¬μ
μ€λ¬΄μμ μ¬λκΆμ κ°ννκ³ μμ° μ μ½μ λν μ μΈμ μ 곡νμ¬ μ¬μ μ΄μ©μ ν¨μ¨μ±μ μ κ³ ν μ μμ κ²μ΄λ€.Previous studies on unused budget in the public sector recognized unused budget as a result of inefficient fiscal management in terms of budget allocation and budget execution as well as a threat to fiscal democracy. As a result, various policies such as preemptive budget execution policy and next-year budget cut based on the amount of unused budget, were adopted to minimize unused budget.
In order to achieve the preemptive budget execution goal, and to avoid disadvantages due to unused budget, public officials are encountered with the incentives to execute budget ineffectively and efficiently. If so, is it appropriate to use unused budget as an indicator of fiscal management efficiency? And if ineffective and inefficient budget execution occurs, unused budget can be relatively sound budget behavior for its financial resources can be preserved.
This paper analyzes the effect of the annual budget execution rate on government performance achievement rate to see if its appropriate to use unused budget as an indicator of fiscal management efficiency. And it also examines the effect of the budget execution in the first and the fourth quarter on government performance achievement rate to see if any ineffective and inefficient budget execution occurs.
The analysis showed that the annual budget execution rate appeared not to have any effect on the performance achievement rate. This implies that it maybe inadequate to use annual budget execution rate as an indicator of fiscal management efficiency. And this could be a valid point considering that different forms of inefficient fiscal management like wasteful budget execution can decrease the amount of unused budget.
Also, it is revealed that the budget execution rate of the first quarter does not have any effect on the performance achievement rate, and the execution rate of the fourth quarter has positive impact on the achievement rate. And we can infer from this result that on some level, ineffective and inefficient budget execution are being made.
These results suggest the necessity to look over the existing mainstream perspective on unused budget again. Ineffective and inefficient budget execution is another form of inefficient fiscal management and a bigger threat to fiscal democracy for its resources cannot be preserved. Rather we can recognize unused budget as a relatively sound budget behavior.μ 1 μ₯ μ λ‘ 1
μ 1 μ μ°κ΅¬μ λͺ©μ λ° νμμ± 1
μ 2 μ μ°κ΅¬μ λμ λ° λ²μ 3
μ 2 μ₯ μ΄λ‘ μ λ
Όμ λ° μ νμ°κ΅¬ κ²ν 6
μ 1 μ μ±κ³Όμ κ΄ν μ΄λ‘ μ λ
Όμ λ° μ νμ°κ΅¬ κ²ν 6
1. μ±κ³Όμ κ΄ν μ΄λ‘ μ λ
Όμ 6
2. μ¬μ μ±κ³Όλͺ©νκ΄λ¦¬μ λ 10
3. μ±κ³Όνκ°μ λμ κ΄ν μ νμ°κ΅¬ κ²ν 17
4. μ±κ³Όμ κ²°μ μμΈμ κ΄ν μ νμ°κ΅¬ κ²ν 19
μ 2 μ μμ°μ κ΄ν μ΄λ‘ μ λ
Όμ λ° μ νμ°κ΅¬ κ²ν 23
1. μ¬μ μ΄μ© κ³Όμ λ° μμ° κ΅¬μ‘° 23
2. μμ°μ κ΄ν μ΄λ‘ μ λ
Όμ 26
3. λΆμ©μ κ΄ν μ΄λ‘ μ λ
Όμ 28
4. λΉν¨μ¨μ μΈ μμ° μ§νμ κ΄ν μ νμ°κ΅¬ κ²ν 32
5. λΆμ©μ μν₯μμΈμ κ΄ν μ νμ°κ΅¬ κ²ν 34
μ 3 μ μ νμ°κ΅¬μ νκ³ 38
μ 3 μ₯ μ°κ΅¬μ€κ³ λ° λΆμλ°©λ² 39
μ 1 μ μ°κ΅¬λ¬Έμ λ° μ°κ΅¬κ°μ€ μ€μ 39
1. μ°κ΅¬λ¬Έμ 39
2. μ°κ΅¬κ°μ€ μ€μ 39
μ 2 μ μ°κ΅¬μ€κ³ 42
1. μ’
μλ³μ 42
2. λ
립λ³μ 43
3. ν΅μ λ³μ 45
4. λΆμλ°©λ² 47
μ 4 μ₯ μ€μ¦λΆμ κ²°κ³Ό 49
μ 1 μ κΈ°μ ν΅κ³λ λ° μκ΄κ΄κ³λΆμ κ²°κ³Ό 49
1. κΈ°μ ν΅κ³λ 49
2. μκ΄κ΄κ³λΆμ κ²°κ³Ό 51
μ 2 μ λ€μ€νκ·λΆμ κ²°κ³Ό 55
μ 5 μ₯ κ²°λ‘ 58
μ 1 μ μ°κ΅¬κ²°κ³Ό μμ½ λ° μμ¬μ 58
μ 2 μ μ°κ΅¬μ νκ³ 61
[λΆλ‘] κΈ°ν μκ΄κ΄κ³λΆμ κ²°κ³Ό 63
μ°Έκ³ λ¬Έν 68
Abstract 74μ
ν΅νμκ° μ λ’°λμ κ°λ³ 리μ€ν¬ μ νΈλλ₯Ό κ³ λ €ν κ²½λ‘μ ννν λͺ¨ν
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : 곡과λν 건μ€ν경곡νλΆ, 2018. 2. κ³ μΉμ.The route choice problem is an important factor in traffic operation and transportation planning. There have been many studies to analyze the route choice behavior using travel data. There was a limit in constructing a route choice model by generating an appropriate choice set due to the limitations of the data.
In this study, we construct a choice set generation model and a stochastic route choice model using the observed data. This study estimates the parameters incorporating travelers heterogeneity according to the choice set from the choice set generation model and the route choice model.
We define the individual confidence level according to perceived travel time distribution to reflect travelers heterogeneity on choice set generation model. In addition, the parameters were estimated using the mixed path-size correction logit model (MPSCL) considering the path overlapping and individual risk preference in the route choice model.
We compare the experienced paths and the derived choice set to construct choice set generation model. In addition, it is possible to estimate better parameters incorporating travelers heterogeneity for choice set generation model and route choice model. We compare the choice set from the developed model with that of the conventional choice set generation model. This study shows the superior prediction result in route choice model reflecting the individual behaviors of the route choice in the urban area on the transportation demand forecasting and traffic operation.1. Introduction 1
1.1 Backgrounds 1
1.2 Research Purpose 4
1.3 Main contents 5
1.4 Research Scope 9
2. Literature Review 11
2.1 Choice Set Generation 13
2.2 Route Choice Model 30
2.3 Review result and limitation 58
2.4 Research Contributions 62
3. Modelling Framework 66
3.1 Overview 66
3.2 Terminology 68
3.3 Choice set generation model 76
3.4 Route choice model 92
4. Revealed Preference Routing Data 100
4.1 Data Characteristics 100
4.2 Data Collection & Description 106
4.3 Data Processing 109
4.4 Missing Data Correction 113
5. Model Estimation & Validation 120
5.1 Overview 120
5.2 Choice set generation 121
5.3 Model estimation & Validation 124
5.4 Model verification 135
5.5 Discussion 138
6. Conclusion 140
6.1 Conclusion 140
6.2 Further research 142
REFERENCES 145
APPENDIX 158Docto