簡易檢索 / 詳目顯示

回結果列表
研究生： 陳柏瑞
Chen, Po-Jui
論文名稱： 自行車與行人共用道上之自行車行為特性─以哈瑪星鐵道文化園區為例
The Characteristics of Bicycle Behaviour in Cyclist-Pedestrian Mixed Flow on Shared Paths: A Case Study of Hamasen Railway Cultural Park
指導教授： 李子璋
Lee, Tzu-Chang
學位類別： 碩士
Master
系所名稱： 規劃與設計學院 - 都市計劃學系
Department of Urban Planning
論文出版年： 2016
畢業學年度： 104
語文別： 英文
論文頁數： 45
中文關鍵詞： 自行車與行人共用道 、空拍調查 、軌跡擷取 、行人與自行車混合流 、超車行為
外文關鍵詞： shared path, aerial videography, trajectory extraction, pedestrians and cyclists mixed traffic, overtaking behaviour
相關次數： 點閱：102  下載：8 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 在環保意識逐漸抬頭下，自行車與步行作為永續交通的一環，近年來逐漸受到世界各國重視且積極推廣。然而國內目前的運輸規劃和相關配套設施尚未完善，自行車騎士為了不與速度差較大的汽、機車混流，經常和行人共用人行道。雖然共用道上嚴重事故的發生機率較低，卻仍舊避免不了兩者之間互相干擾碰撞的風險。為降低不同使用者之間的衝突，了解其互動行為並應用在都市設計中來提供安全通行設施是亟需被探討的課題。
    本研究係運用無人空拍機攝影及軌跡擷取技術，探討自行車與行人共用道上自行車之運動特性。於高雄市哈瑪星鐵道文化園區進行實地拍攝，並建置軌跡資料庫。無人空拍機具有飛行成本低、攝影視野廣、資料精度高等優點，可解決以往交通調查研究多受限於影片資料取得困難或成本過高之問題。最後，以資料庫中數據分析行人與自行車混合流時之運動特性，包含速度、加（減）速度、自行車進行超越行為時與其他用路人所保持之橫向間距、縱向間距以及碰撞時間，並發展個體選擇模型來描述自行車的方向選擇。本研究成果除了可以提供交通模擬發展的相關資訊，亦可供行人與自行車共用道設計及相關政策參考。

    The aim of this paper is to investigate the kinematic features and to model the interaction between cyclists and pedestrians on shared paths. Walking and cycling are linked to sustainable transport because both pedestrians and cyclists are non-motorized road users who contribute to healthy, low-carbon lifestyles. To encourage these two traffic modes, it is common to construct shared paths to provide safer environments that protect non-motorized users from general traffic. Although a shared space might be more efficient in terms of saving money and space, it can cause conflicts between cyclists and pedestrians. Hence, it is critical to understand the interaction between bicycles and pedestrians in order to design safe shared paths, particularly from a kinematics perspective.
    The data for this analysis were collected with a novel approach using aerial videography. Hamasen Railway Cultural Park in Kaohsiung City, Taiwan was chosen as the survey site. All road users’ trajectories as well as their interaction could be observed and then extracted with a semi-automatic trajectory extraction system to establish the database. The basic kinematic parameters of each object, such as locations, speeds, steering angles, accelerations and decelerations, were stored in a database for further analysis. Some macroscopic and microscopic mixed traffic characteristics were investigated and reported, such as speed differences, lateral distances, longitudinal distances and time-to-collision of cyclist-pedestrian mixed flow. To depict cyclist overtaking behaviour, models based on a discrete choice model were also established.
    This paper introduces a new method for cyclist-pedestrian mixed flow surveys. The results also contribute information for urban design, infrastructure management and policy implementation of shared path plans to encourage a non-motorized, eco-friendly traffic mode. In addition, the information obtained is of great help to the calibration of microscopic models and simulation software describing pedestrian and bicycle movements for further application both in academia and in practice.

    1. INTRODUCTION 1 1.1. Background and motivation 1 1.2. Research objectives 2 1.3. Structure of the study 3 2. LITERATURE REVIEW 5 2.1. Design of bicycle riding space 5 2.1.1. Bicycle lanes 5 2.1.2. Shared paths 5 2.1.3. Mixed traffic lanes 6 2.1.4. Summary 7 2.2. Traffic flow models 7 2.2.1. Macroscopic model 7 2.2.2. Microscopic model 8 2.2.3. Summary 11 3. METHODOLOGY 12 3.1. Data collection 12 3.1.1. Times and sites of the data survey 12 3.1.2. Procedure of aerial photography and post-production 13 3.1.3. Trajectory extraction technique 15 3.2. Analysis 17 3.2.1. Definition of bicycle behaviour 17 3.2.2. Analysis procedure 18 3.3. Discrete Choice Model 19 3.3.1. Multinomial Logit Model 20 3.3.2. Nested Logit Model 21 4. CHARACTERISTICS OF CYCLIST-PEDESTRIAN MIXED FLOW 22 4.1. Speed 22 4.2. Acceleration/ Deceleration 24 4.3. Lateral distance 26 4.4. Longitudinal distance 28 4.5. Time to collision 29 5. LOGIT MODELLING 30 5.1. Direction Choice Model 30 5.1.1. Parameters and alternatives 30 5.1.2. Model calibration 32 5.2. Cyclist Overtaking model 33 5.2.1. Parameters and alternatives 33 5.2.2. MNL Model calibration 35 5.2.3. NL Model calibration 36 5.3. Simulation 38 5.4. Summary 39 6. CONCLUSIONS 40 7. DISCUSSION AND FUTURE WORKS 42 REFERENCES 43

    1. ADOBE. 2013. ADOBE® AFTER EFFECTS® Help and tutorials [Online]. Available: https://helpx.adobe.com/after-effects/archive.html [Accessed Aug. 15, 2016].
    2. AKAR, G. & CLIFTON, K. 2009. Influence of individual perceptions and bicycle infrastructure on decision to bike. Transportation Research Record: Journal of the Transportation Research Board, 165-172.
    3. ANTONINI, G., BIERLAIRE, M. & WEBER, M. 2006. Discrete choice models of pedestrian walking behavior. Transportation Research Part B: Methodological, 40, 667-687.
    4. ANVARI, B., BELL, M. G., SIVAKUMAR, A. & OCHIENG, W. Y. 2015. Modelling shared space users via rule-based social force model. Transportation Research Part C: Emerging Technologies, 51, 83-103.
    5. ASADI-SHEKARI, Z., MOEINADDINI, M. & ZALY SHAH, M. 2013. Non-motorised level of service: addressing challenges in pedestrian and bicycle level of service. Transport reviews, 33, 166-194.
    6. BEN-AKIVA, M. E. & LERMAN, S. R. 1985. Discrete choice analysis: theory and application to travel demand, MIT press.
    7. BIERLAIRE, M. BIOGEME: a free package for the estimation of discrete choice models. Swiss Transport Research Conference, 2003.
    8. BLUE, V. J. & ADLER, J. L. 2001. Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transportation Research Part B: Methodological, 35, 293-312.
    9. BRACKSTONE, M. & MCDONALD, M. 1999. Car-following: a historical review. Transportation Research Part F: Traffic Psychology and Behaviour, 2, 181-196.
    10. CHEN, J.-H. & LEE, T.-C. 2014. The density-velocity diagrams of heterogeneous traffic containing pedestrians, bicycles and powered two-wheelers in shared spaces. 19th International Conference of Hong Kong Society for Transportation Studies (HKSTS 19). Hong Kong.
    11. CHEN, J.-H. & LEE, T.-C. 2015. The factors affecting the speeds of pedestrians in shared spaces containing pedestrians, bicycles and powered two-wheelers. 8th International Conference on Planning and Design (ICPD). Tainan, Taiwan.
    12. CHRISTOPOULOU, P. & PITSIAVA-LATINOPOULOU, M. 2012. Development of a model for the estimation of pedestrian level of service in Greek urban areas. Procedia-Social and Behavioral Sciences, 48, 1691-1701.
    13. DEMAIO, P. 2009. Bike-sharing: History, impacts, models of provision, and future. Journal of Public Transportation, 12, 3.
    14. DJI. 2014. DJI Phantom 2 Vision+ - Easy to Fly Aerial Filmmaking System [Online]. Available: http://www.dji.com/product/phantom-2-vision-plus [Accessed Aug. 15, 2016].
    15. GIPPS, P. G. 1981. A behavioural car-following model for computer simulation. Transportation Research Part B: Methodological, 15, 105-111.
    16. GIPPS, P. G. 1986. A model for the structure of lane-changing decisions. Transportation Research Part B: Methodological, 20, 403-414.
    17. GIPPS, P. G. & MARKSJ , B. 1985. A micro-simulation model for pedestrian flows. Mathematics and computers in simulation, 27, 95-105.
    18. HELBING, D., FARKAS, I. & VICSEK, T. 2000. Simulating dynamical features of escape panic. Nature, 407, 487-490.
    19. HELBING, D. & MOLNAR, P. 1995. Social force model for pedestrian dynamics. Physical review E, 51, 4282.
    20. JIA, B., LI, X.-G., JIANG, R. & GAO, Z.-Y. 2007. Multi-value cellular automata model for mixed bicycle flow. The European Physical Journal B, 56, 247-252.
    21. KANG, L., XIONG, Y. & MANNERING, F. L. 2013. Statistical analysis of pedestrian perceptions of sidewalk level of service in the presence of bicycles. Transportation Research Part A: Policy and Practice, 53, 10-21.
    22. LEE, T.-C. 2007. An agent-based model to simulate motorcycle behaviour in mixed traffic flow. Imperial College London (University of London).
    23. LEE, T.-C., POLAK, J. & BELL, M. 2009. New approach to modeling mixed traffic containing motorcycles in urban areas. Transportation Research Record: Journal of the Transportation Research Board, 195-205.
    24. LEE, T.-C., POLAK, J. W. & BELL, M. G. H. 2008. Trajectory Extractor user manual version 1.0. Centre for Transport Studies, Imperial College London, UK.
    25. LIANG, X., BAOHUA, M. & QI, X. 2012. Psychological-Physical Force Model for Bicycle Dynamics. Journal of Transportation Systems Engineering and Information Technology, 12, 91-97.
    26. LUO, J. T., HUANG, Y. C. & WONG, K. I. 2014. The feasibility of aerial videography using multicopter for traffic surveys. 19th International Conference of Hong Kong Society for Transportation Studies (HKSTS 19). Hong Kong.
    27. MCFADDEN, D. 1973. Conditional logit analysis of qualitative choice behavior.
    28. PIPES, L. A. 1953. An operational analysis of traffic dynamics. Journal of applied physics, 24, 274-281.
    29. PIPES, L. A. 1967. Car following models and the fundamental diagram of road traffic. Transportation Research, 1, 21-29.
    30. PUCHER, J., DILL, J. & HANDY, S. 2010. Infrastructure, programs, and policies to increase bicycling: an international review. Preventive medicine, 50, S106-S125.
    31. REGION OF PEEL 2014. Pedestrian and Bicycle Facility Design Guidance. In: PEEL, R. O. (ed.).
    32. ROBIN, T., ANTONINI, G., BIERLAIRE, M. & CRUZ, J. 2009. Specification, estimation and validation of a pedestrian walking behavior model. Transportation Research Part B: Methodological, 43, 36-56.
    33. TRANSPORTATION RESEARCH BOARD 2000. Highway Capacity Manual. Washington, DC: National Research Council.
    34. TSENG, J. C. H., HSU, H.-H., CHANG, S.-H., CHEN, W.-H., TAO, C.-C. & CHEN, Y.-W. 2013. A Study on Promotional Strategies for Bicycle Usage (in chinese). the Ministry of Tansportation and Communications.
    35. VASIC, J. & RUSKIN, H. J. 2012. Cellular automata simulation of traffic including cars and bicycles. Physica A: Statistical Mechanics and its Applications, 391, 2720-2729.
    36. WENG, W., SHEN, S., YUAN, H. & FAN, W. 2007. A behavior-based model for pedestrian counter flow. Physica A: Statistical Mechanics and its Applications, 375, 668-678.

    下載圖示 校內：2021-08-15公開
    校外：2021-08-15公開
    QR CODE