Methodological framework of pedestrian demand estimation for green space in urban area = 도심부 그린스페이스 조성에 따른 보행수요예측

국문 초록 (Abstract)

과학기술의 지속적인 발전으로 사람들의 통행패턴이 더욱 다양해지고 있다. 그런데도 차량 이용객이 계속 급증하면서 교통 혼잡이 더 심해지고 있다. 이 때문에 많은 도시가 교통 문제를 겪으면서 오염과 사회적 비용이 증가하고 있다. 그(에 대한) 해결책으로 정책 입안자들은 특히 피크시간대에 자가용 이용 대신 걷거나 대중교통 수단을 이용하는 것과 같은 몇 가지를 권고했다. 최근 국내외 연구진이 보행자 중심의 도로계획을 통해 위에서 언급한 문제를 해결하려고 노력했다. 이들은 도시공간을 재설계하고 기존 도로를 지하터널로 대체하며 기존에 도로가 점유하던 터널 위 공간에 녹지공간, 공원, 체육시설 등의 사업을 조성해 목표를 달성했다.
서울의 사례도 이와 비슷하게, 교통 체증 문제가 상당히 증가했다. 도시계획가들은 교통혼잡 상황을 완화하기 위해 서울 서부간선도로에 도로의 폭을 줄이고 녹지공간을 설치하는 등 보행환경 개선을 도모했다. 이번 연구는 이 녹지공간을 사례연구로 활용함으로써 녹지공간을 이용할 보행자의 수를 추정하고 보행장려지수를 개발하는 데 목적을 두고 있다.
서울 서부간선도로에서 보행자 흐름, 건축면적, 토지이용 등의 데이터를 활용했고, 연구지역에 있는 녹지공간을 이용하는 보행자의 수를 예측하는 회귀모델도 함께 활용했다. 그리고 나서 이 새롭게 파생된 정보는 녹색 공간을 지나갈 보행자의 수를 추정하기 위해 시뮬레이션 프레임워크에 사용된다.
회귀 모형 결과에서 보도, 상업지역, 버스 교환 지점과 같은 요인들이 보행량에 긍정적인 영향을 미치지만 보행 거리는 부정적인 영향을 미친다는 것을 확인했다. 이 연구의 결과는 좋은 보행 환경이 걷기를 장려할 수 있고 정책 입안자들이 다양한 토지 이용 요인이 보행량에 미치는 영향을 이해하는 데 도움을 줄 수 있다는 것을 확인시켜 준다.

다국어 초록 (Multilingual Abstract)

With the continuous development of science and technology, people's trip modes are becoming more diversified. Nevertheless, the number of people using vehicles keep on soaring, causing traffic congestion to increase further. For this reason, many urban cities are experiencing traffic problems leading to increased pollution and social costs. As a remedy, policymakers have made several recommendations such as walking or using public transport modes instead of private cars, particularly during peak hours. Recently, researchers at home and abroad have tried to solve the problems mentioned above through pedestrian-oriented road planning. They achieved their goal by redesigning urban spaces, replacing preexisting roadways with underground tunnels, and creating projects such as green spaces, parks, and sports facilities on the spaces above the tunnels that were previously occupied by roadways.
Similar to the case of Seoul, the traffic congestion problem has increased considerably. To alleviate the traffic congestion situation, city planners sought to improve the pedestrian environment by reducing the width of roadway lanes and establishing a green space on Seoul's western arterial highway. By using this green space area as a case study, this research aims at estimating the number of pedestrians that would use the green space and developing a walking encouragement index.
We employed data such as pedestrian flow, building area, and land use from the western arterial highway in Seoul, together with a regression model to forecast the number of pedestrians that use the green space located in the study area. This newly derived information is then used in a simulation framework to estimate the number of pedestrians that will pass by the green space.
From the multiple linear regression model results, we identified that factors such as sidewalks, commercial areas and bus interchange points have a positive impact on pedestrian volume, whereas walking distance has a negative effect. The results of this study confirms that good pedestrian environments can encourage walking and can also help policymakers to understand the effect of different land use factors on pedestrian volume.

목차 (Table of Contents)

• TABLE OF CONTENTS
• CHAPTER 1. INTRODUCTION 1
• 1.1 Research Background and Objectives 1
• 2.1 Research Scope and Process 5
• 2.1.1 Scope of the Study 5
• 2.1.2 Process of the Study 5
• CHAPTER 2. LITERATURE REVIEW 7
• 2.1 The Factors Influencing Pedestrian 7
• 2.2 Estimation Method of Pedestrian Demand 10
• 2.3 Pedestrian Environment 13
• 2.4 Limitation of Previous Studies 16
• CHAPTER 3. METHODOLOGY 18
• 3.1 Spatial unit of the Study 18
• 3.1.1 Influence Scope 18
• 3.1.2 PAZ(Pedestrian Analysis Zone) Division 19
• 3.2 Theory and Methodology 21
• CHAPTER 4. DATA COLLECTION 22
• 4.1 Personal Characteristics Data 22
• 4.1.1 Pedestrian Volume Investigation 22
• 4.1.2 Questionnaire Survey 22
• 4.2 Physical Characteristics Data 25
• 4.2.1 Physical Characteristics Variable 25
• 4.2.2 Walking Encouragement Index Construction 26
• 4.3 Study Area WEI 30
• CHAPTER 5. PEDESTRIAN DEMAND ESTIMATION MODEL 33
• 5.1 Induced Pedestrian Flows 33
• 5.1.1 Model Verification and Parameter Estimation 33
• 5.1.2 Pedestrian Demand Model 39
• 5.1.3 Pedestrian Inducement Flows 40
• 5.2 Transferred Pedestrian Flows 42
• 5.2.1 VISWALK Simulation Tool 42
• 5.2.2 Built Network 45
• 5.2.3 Validation of Micro-simulated Pedestrian Area 47
• 5.2.4 Micro-simulated Results 48
• CHAPTER 6. VERIFICATION AND RESULTS 52
• 6.1 Pedestrian Demand Flows Verification 52
• 6.1.1 Induced Pedestrian Flows Verification 52
• 6.1.2 Transferred Pedestrian Flows Verification 52
• 6.2 Results 55
• CHAPTER 7. CONCLUSION AND RECOMMENDATIONS 56
• 7.1 Direction for Future Research 57
• REFERENCES 59
• APPENDICES 63
• ABBREVIATIONS 71
• 초록 72
• ACKNOWLEDGEMENT 74
• LIST OF TABLES
• <Table 2-1> Differences from traditional four-step travel demand models 17
• <Table 4-1> Pedestrian influencing factors 24
• <Table 4-2> SP research results 24
• <Table 4-3> Physical variables 26
• <Table 5-4> Variable VIF results(1) 35
• <Table 5-5> Summary of collinearity verification(1) 35
• <Table 5-6> Analysis variable verification results(1) 36
• <Table 5-7> Variable VIF results(2) 37
• <Table 5-8> Analysis variable verification result(2) 38
• <Table 5-9> Summary of collinearity verification(2) 38
• <Table 5-10> Comparison of pedestrian volume models 42
• <Table 6-1> Comparison of similar research results 52
• Appendix B: WEI by PAZs 65
• Appendix C: Result of predicting pedestrian demand by PAZs 68
• LIST OF FIGURES
• [Figure 1-1] Time square before and after improvement 3
• [Figure 1-2] Jemulpo Road before and after improvement 4
• [Figure 1-3] Research Framework 6
• [Figure 2-1] The PEI of Chicago. Source: Peiravian & Sybil Derrible & Farukh Ijaz (2014) 14
• [Figure 2-2] Spatial distribution of the PEI in the GMA. Source: Lefebvre-Ropars, Gabriel, et al., 2017 15
• [Figure 3-1] Scope of influence 19
• [Figure 3-2] PAZs zonal structures 20
• [Figure 4-1] The survey location of pedestrian overpass 23
• [Figure 4-2] Spatial distribution of the WEI in the study area 32
• [Figure 5-4] Structure framework for simulation 43
• [Figure 5-5] Build the study area simulation network 45
• [Figure 5-6] 3D view of network in VISWALK 46
• [Figure 6-1] pedestrian volume observation position 54