有效的評估交通的韌性是交通管理與都市計畫領域中相當重要的一環。隨著都市的擴張、區域間的發展與日常的旅行行為，車流量的提升以及號誌的等待時間等導致日常的交通壅塞的現象，使都市交通變得複雜且脆弱，進而產生經濟、環境和安全等問題，同時也是臺灣交通面臨的重要課題之一。以往的研究往往是針對自然災害、工程施工等突發性難以預測與控制的交通外部干擾，而日常的壅塞屬於經常性的干擾，雖與生活息息相關卻少有研究進行評估與衡量。再者，研究交通壅塞需要透過大量的數據分析，然而臺灣取得交通領域的相關研究資料時面臨許多困難，如交通密度、交通流量等，所以在測試新型的電子地圖等大數據資料蒐集工具對交通的韌性研究有一定的影響性。
面對這種習以為常的壅塞以及資料取得的困難，本研究的突破口有二：首先交通壅塞是一種交通動態的臨界現象，當交通系統達到臨界點時，系統就會開始癱瘓，所以本研究認為日常的交通壅塞為影響交通韌性的一場事件。而交通滲流理論（Percolation Theory）是一種描述傳遞交通流的數學理論工具，透過這項理論工具研究這種日常的交通壅塞，能夠深入理解和評估都市路網中交通壅塞的形成、傳遞和關鍵時間。其二，交通的相關數據常受限於資料的普及性，本研究利用Python3.9爬取Google Maps API的旅行時間數據，透過Google大量的蒐集手機導航的資訊，能夠捕捉精確的路段旅行時間資料。
故本研究以滲流理論為基礎並蒐集交通數據研究日常壅塞時路網的擴散過程以及路段的恢復時間，並測試與實證這個方法的合宜性。因造成交通壅塞的可能性很多，並且在城郊區之路段壅塞程度不同，若僅用全面且單一的分析是相對不足的，故本研究為全面性以客觀的角度評估，將研究設計為以都市的結構以及交通工程兩者面向綜合性的評估研究交通的韌性。並訂定臺南市市中心為實證的範圍約764公頃（289個節點與760條路段所組成）。利用Google Maps API的技術爬取一週七天7點至23點以15分鐘為間隔的路段旅行速度作為本研究之關鍵指標。另外為了解實證範圍在都會區中的影響或意義或地位，故以接近都會區擴大約5,080公頃（由4,439個節點組成）資料分析範圍，以免造成偏誤。都市結構面向的指標是利用Dijkstra’s Algorithm取得研究範圍之最短路徑及距離，取得實證範圍內中心性與空間型構法則之指標。而交通工程面向則是以2022年發布之「臺灣公路容量手冊」，參考交通部運輸研究所探討之「臺灣地區多車道郊區公路容量及特性研究」所提出之路邊停車及車道寬度調整因素和方法。綜合兩種面向以多維度的綜合性指標評估路段的性能P(t)，以滲流理論宏觀的評估整體路網以及微觀的分析單一路段。以性能P(t)和閾值 q_s 評估路網壅塞的擴散過程和路網崩潰的臨界點(滲流閾值q_c)，並以滲流閾值q_c定義路網之最低要求性能（MRP, minimum required performance），能夠評估單一路段的發生交通壅塞時的恢復速度，也就是交通韌性。研究結果顯示以都市的結構的面向評估交通韌性與真實狀況更相似，透過這樣的評估有效的提升在都市計畫與交通管理層面的應用。本研究比起傳統單一的方法更穩定且可信度更高，能夠有效提供都市的交通不同的視角理解管理需求。

Effectively assessing traffic resilience is a crucial aspect of traffic management and urban planning. Numerous factors and situations contribute to daily traffic congestion. This makes urban traffic complex and vulnerable. First, previous studies have often focused on sudden and unpredictable external traffic disruptions. Daily congestion is closely related to daily life but rarely studied for assessment. So, this research considers daily traffic congestion to be an event that impacts traffic resilience. Using percolation theory to describe traffic flow propagation as a theoretical tool, evaluate the formation, propagation, and critical moments of traffic congestion in urban road networks. Second, this kind of research data, such as traffic density and traffic flow, is difficult to obtain in Taiwan. Therefore, testing new geospatial databases has a significant impact on traffic resilience.
This research studies the critical times during everyday congestion using Percolation Theory. Meanwhile, it is necessary to conduct a comprehensive assessment of the urban network, designing the research to consider both urban structure and traffic engineering aspects. The experimental area covers 764.08 HA (comprising 289 nodes and 760 road segments). Using the Google Maps API, travel time data for these road segments was crawled every 15 minutes from 7:00 to 23:00 for a week. Additionally, to understand the impact of the experimental scope within the metropolitan area, the study will analyze an expanded area of approximately 5,080 HA (comprising 4,439 nodes) to collect centrality and space syntax indicators of the urban structure aspect. And refer to the "2022 Taiwan Highway Capacity Manual" for factors and methods of the traffic engineering aspect. The results analyze overall network congestion, key segments, diffusion, and dissipation using percolation theory. Compared to traditional single-method approaches, this study is more stable and reliable, providing effective insights into urban traffic management needs from various perspectives.

Afrin, T., & Yodo, N. (2020). A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System. Sustainability, 12(11).
Ahmad Hosseini, S., & Wadbro, E. (2016). Connectivity reliability in uncertain networks with stability analysis. Expert Systems with Applications, 57, 337-344. https://doi.org/https://doi.org/10.1016/j.eswa.2016.03.040
Ahmed Md, A., Sadri Arif, M., Mehrabi, A., & Azizinamini, A. (2022). Identifying Topological Credentials of Physical Infrastructure Components to Enhance Transportation Network Resilience: Case of Florida Bridges. Journal of Transportation Engineering, Part A: Systems, 148(9), 04022055. https://doi.org/10.1061/JTEPBS.0000712
Almoghathawi, Y., Barker, K., & Albert, L. A. (2019). Resilience-driven restoration model for interdependent infrastructure networks. Reliability Engineering & System Safety, 185, 12-23. https://doi.org/https://doi.org/10.1016/j.ress.2018.12.006
Alomari, A. H., Al-Omari, B. H., & Al-Hamdan, A. B. (2020). Validating trip travel time provided by smartphone navigation applications in Jordan. Jordan Journal of Civil Engineering, 14(4).
Alsobky, A., & Mousa, R. (2020). Estimating free flow speed using Google Maps API: accuracy, limitations, and applications [Article]. Advances in Transportation Studies, 50, 49-64. https://doi.org/10.4399/97888255317324
Amlan, H. A., Hassan, S. A., & Alias, N. E. (2023). Discovering the global landscape of vulnerability assessment method of transportation network studies: A bibliometric review. Physics and Chemistry of the Earth, Parts A/B/C, 129, 103336. https://doi.org/https://doi.org/10.1016/j.pce.2022.103336
Ansari Esfeh, M., Kattan, L., Lam, W. H. K., Salari, M., & Ansari Esfe, R. (2022). Road network vulnerability analysis considering the probability and consequence of disruptive events: A spatiotemporal incident impact approach. Transportation Research Part C: Emerging Technologies, 136, 103549. https://doi.org/https://doi.org/10.1016/j.trc.2021.103549
Arslan, T. (2009). A hybrid model of fuzzy and AHP for handling public assessments on transportation projects. Transportation, 36(1), 97-112. https://doi.org/10.1007/s11116-008-9181-9
Bayen, A. M. (2011). A hydrodynamic theory based statistical model of arterial traffic.
Bell, M. G. H. (2000). A game theory approach to measuring the performance reliability of transport networks. Transportation Research Part B: Methodological, 34(6), 533-545. https://doi.org/https://doi.org/10.1016/S0191-2615(99)00042-9
Berdica, K. (2002). An introduction to road vulnerability: what has been done, is done and should be done. Transport Policy, 9(2), 117-127. https://doi.org/https://doi.org/10.1016/S0967-070X(02)00011-2
Bigazzi, A. Y., & Figliozzi, M. A. (2012). Congestion and emissions mitigation: A comparison of capacity, demand, and vehicle based strategies. Transportation Research Part D: Transport and Environment, 17(7), 538-547. https://doi.org/https://doi.org/10.1016/j.trd.2012.06.008
Boeing, G. (2024). Modeling and Analyzing Urban Networks and Amenities with OSMnx. https://geoffboeing.com/publications/osmnx-paper/
Buisson, C., & Ladier, C. (2009). Exploring the Impact of Homogeneity of Traffic Measurements on the Existence of Macroscopic Fundamental Diagrams. Transportation Research Record, 2124(1), 127-136. https://doi.org/10.3141/2124-12
Calvert, S. C., & Snelder, M. (2018). A methodology for road traffic resilience analysis and review of related concepts. Transportmetrica A: transport science, 14(1-2), 130-154.
Chen, A., Yang, H., Lo, H. K., & Tang, W. H. (2002). Capacity reliability of a road network: an assessment methodology and numerical results. Transportation Research Part B: Methodological, 36(3), 225-252. https://doi.org/https://doi.org/10.1016/S0191-2615(00)00048-5
Chen, C., He, F., Yu, R., Wang, S., & Dai, Q. (2023). Resilience assessment model for urban public transportation systems based on structure and function. Journal of Safety Science and Resilience, 4(4), 380-388. https://doi.org/https://doi.org/10.1016/j.jnlssr.2023.10.001
Chen, H., Zhou, R., Chen, H., & Lau, A. (2022). A resilience-oriented evaluation and identification of critical thresholds for traffic congestion diffusion. Physica A: Statistical Mechanics and its Applications, 600, 127592. https://doi.org/https://doi.org/10.1016/j.physa.2022.127592
Chen, L., & Yang, H. (2012). Managing congestion and emissions in road networks with tolls and rebates. Transportation Research Part B: Methodological, 46(8), 933-948. https://doi.org/https://doi.org/10.1016/j.trb.2012.03.001
Chen, Z., & Rose, A. (2018). Economic resilience to transportation failure: a computable general equilibrium analysis. Transportation, 45(4), 1009-1027. https://doi.org/10.1007/s11116-017-9819-6
Daganzo, C. F. (1994). The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research Part B: Methodological, 28(4), 269-287. https://doi.org/https://doi.org/10.1016/0191-2615(94)90002-7
Daganzo, C. F., & Geroliminis, N. (2008). An analytical approximation for the macroscopic fundamental diagram of urban traffic. Transportation Research Part B: Methodological, 42(9), 771-781. https://doi.org/https://doi.org/10.1016/j.trb.2008.06.008
de Jong, G. C., & Bliemer, M. C. J. (2015). On including travel time reliability of road traffic in appraisal. Transportation Research Part A: Policy and Practice, 73, 80-95. https://doi.org/https://doi.org/10.1016/j.tra.2015.01.006
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269-271. https://doi.org/10.1007/BF01386390
Dong, S., Mostafizi, A., Wang, H., Gao, J., & Li, X. (2020). Measuring the Topological Robustness of Transportation Networks to Disaster-Induced Failures: A Percolation Approach. Journal of Infrastructure Systems, 26(2), 04020009. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000533
Faturechi, R., & Miller-Hooks, E. (2014). Travel time resilience of roadway networks under disaster. Transportation Research Part B: Methodological, 70, 47-64. https://doi.org/https://doi.org/10.1016/j.trb.2014.08.007
Faturechi, R., & Miller-Hooks, E. (2015). Measuring the Performance of Transportation Infrastructure Systems in Disasters: A Comprehensive Review. Journal of Infrastructure Systems, 21(1), 04014025. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000212
Françoise, J.-P., Naber, G. L., & Tsou, S. T. (2006). Encyclopedia of mathematical physics (Vol. 2). Elsevier Oxford, UK.
Fu, L., Sun, D., & Rilett, L. R. (2006). Heuristic shortest path algorithms for transportation applications: State of the art. Computers & Operations Research, 33(11), 3324-3343. https://doi.org/https://doi.org/10.1016/j.cor.2005.03.027
Geroliminis, N., & Daganzo, C. F. (2008). Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transportation Research Part B: Methodological, 42(9), 759-770. https://doi.org/https://doi.org/10.1016/j.trb.2008.02.002
Giannopoulou, M., Roukounis, Y., & Stefanis, V. (2012). Traffic Network and the Urban Environment: An Adapted Space Syntax Approach. Procedia - Social and Behavioral Sciences, 48, 1887-1896. https://doi.org/https://doi.org/10.1016/j.sbspro.2012.06.1163
Godfrey, J. (1969). The mechanism of a road network. Traffic Engineering & Control, 8(8).
Golbeck, J. (2013). Chapter 3 - Network Structure and Measures. In J. Golbeck (Ed.), Analyzing the Social Web (pp. 25-44). Morgan Kaufmann. https://doi.org/https://doi.org/10.1016/B978-0-12-405531-5.00003-1
Google. (2024). Google Directions API. Retrieved 2024.01.29 from https://developers.google.com/maps/documentation/directions/overview
Gu, Y., Fu, X., Liu, Z., Xu, X., & Chen, A. (2020). Performance of transportation network under perturbations: Reliability, vulnerability, and resilience. Transportation Research Part E: Logistics and Transportation Review, 133, 101809. https://doi.org/https://doi.org/10.1016/j.tre.2019.11.003
Guidotti, R., Gardoni, P., & Chen, Y. (2017). Network reliability analysis with link and nodal weights and auxiliary nodes. Structural Safety, 65, 12-26. https://doi.org/https://doi.org/10.1016/j.strusafe.2016.12.001
Hamedmoghadam, H., Zheng, N., Li, D., & Vu, H. L. (2022). Percolation-based dynamic perimeter control for mitigating congestion propagation in urban road networks. Transportation Research Part C: Emerging Technologies, 145, 103922. https://doi.org/https://doi.org/10.1016/j.trc.2022.103922
Hao, N., Feng, Y., Zhang, K., Tian, G., Zhang, L., & Jia, H. (2017). Evaluation of traffic congestion degree: An integrated approach. International Journal of Distributed Sensor Networks, 13(7), 1550147717723163. https://doi.org/10.1177/1550147717723163
Herman, R., & Prigogine, I. (1979). A two-fluid approach to town traffic. Science, 204(4389), 148-151.
Hoffman, K., Berardino, F., & Hunter, G. (2013). Congestion pricing applications to manage high temporal demand for public services and their relevance to air space management. Transport Policy, 28, 28-41. https://doi.org/https://doi.org/10.1016/j.tranpol.2012.02.004
Isa, N., Yusoff, M., & Mohamed, A. (2014, 3-5 Dec. 2014). A Review on Recent Traffic Congestion Relief Approaches. 2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology, Shah Alam, Selangor Malaysia.
Jenelius, E., Petersen, T., & Mattsson, L.-G. (2006). Importance and exposure in road network vulnerability analysis. Transportation Research Part A: Policy and Practice, 40(7), 537-560. https://doi.org/10.1016/j.tra.2005.11.003
Kadkhodaei, M., & Shad, R. (2018). Analysis and Evaluation of Traffic Congestion Control Methods in Touristic Metropolis Using Analytical Hierarchy Process (AHP). Civil Engineering Journal, 4(3), 602-608.
Klarqvist, B. (2015). A space syntax glossary. NA, 6(2).
Kondo, R., Shiomi, Y., & Uno, N. (2012, 2012//). Network Evaluation Based on Connectivity Reliability and Accessibility. Network Reliability in Practice, New York, NY.
Kurzhanskiy, A. A., & Varaiya, P. (2010). Active traffic management on road networks: a macroscopic approach. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1928), 4607-4626.
Leclercq, L., Chiabaut, N., & Trinquier, B. (2014). Macroscopic Fundamental Diagrams: A cross-comparison of estimation methods. Transportation Research Part B: Methodological, 62, 1-12. https://doi.org/https://doi.org/10.1016/j.trb.2014.01.007
Leung, I. X., Chan, S.-Y., Hui, P., & Lio, P. (2011). Intra-city urban network and traffic flow analysis from GPS mobility trace. arXiv preprint arXiv:1105.5839.
Li, D., Fu, B., Wang, Y., Lu, G., Berezin, Y., Stanley, H. E., & Havlin, S. (2015). Percolation transition in dynamical traffic network with evolving critical bottlenecks. Proceedings of the National Academy of Sciences, 112(3), 669-672. https://doi.org/10.1073/pnas.1419185112
Li, Z., Jin, C., Hu, P., & Wang, C. (2019). Resilience-based transportation network recovery strategy during emergency recovery phase under uncertainty. Reliability Engineering & System Safety, 188, 503-514. https://doi.org/https://doi.org/10.1016/j.ress.2019.03.052
Liu, X., Li, D., Ma, M., Szymanski, B. K., Stanley, H. E., & Gao, J. (2022). Network resilience. Physics Reports, 971, 1-108. https://doi.org/https://doi.org/10.1016/j.physrep.2022.04.002
Liu, X. H., Zhang, D. G., Yan, H. R., Cui, Y. y., & Chen, L. (2019). A New Algorithm of the Best Path Selection Based on Machine Learning. IEEE Access, 7, 126913-126928. https://doi.org/10.1109/ACCESS.2019.2939423
Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y. (2015). Traffic Flow Prediction With Big Data: A Deep Learning Approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865-873. https://doi.org/10.1109/TITS.2014.2345663
Ma, C., Zhou, J., Xu, X., & Xu, J. (2020). Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review. Journal of Advanced Transportation, 2020, 5261580. https://doi.org/10.1155/2020/5261580
Madkour, A., Aref, W. G., Rehman, F. U., Rahman, M. A., & Basalamah, S. (2017). A survey of shortest-path algorithms. arXiv preprint arXiv:1705.02044.
Magzhan, K., & Jani, H. M. (2013). A review and evaluations of shortest path algorithms. Int. J. Sci. Technol. Res, 2(6), 99-104.
Massobrio, R., & Cats, O. (2024). Topological assessment of recoverability in public transport networks. Communications Physics, 7(1), 108. https://doi.org/10.1038/s42005-024-01596-8
Mattsson, L.-G., & Jenelius, E. (2015). Vulnerability and resilience of transport systems – A discussion of recent research. Transportation Research Part A: Policy and Practice, 81, 16-34. https://doi.org/10.1016/j.tra.2015.06.002
Monokrousou, K., & Giannopoulou, M. (2016). Interpreting and Predicting Pedestrian Movement in Public Space through Space Syntax Analysis. Procedia - Social and Behavioral Sciences, 223, 509-514. https://doi.org/https://doi.org/10.1016/j.sbspro.2016.05.312
Murray-Tuite, P. M. (2006, 3-6 Dec. 2006). A Comparison of Transportation Network Resilience under Simulated System Optimum and User Equilibrium Conditions. Proceedings of the 2006 Winter Simulation Conference, Monterey, CA, USA.
Niu, S., Zhang, J.-s., Zhang, F., & Gao, J. (2020, 2020//). Resilience Analysis for Comprehensive Transportation Network. Green, Smart and Connected Transportation Systems, Singapore.
Pan, S., Yan, H., He, J., & He, Z. (2021). Vulnerability and resilience of transportation systems: A recent literature review. Physica A: Statistical Mechanics and its Applications, 581, 126235. https://doi.org/https://doi.org/10.1016/j.physa.2021.126235
Pan, X., Dang, Y., Wang, H., Hong, D., Li, Y., & Deng, H. (2022). Resilience model and recovery strategy of transportation network based on travel OD-grid analysis. Reliability Engineering & System Safety, 223, 108483. https://doi.org/https://doi.org/10.1016/j.ress.2022.108483
Qi, W., Wen, H., & Wu, Y. (2015). Method and application of grade division for road traffic congestion based on driver’s feeling. Advances in Mechanical Engineering, 7(11), 1687814015618860. https://doi.org/10.1177/1687814015618860
Razali, N. A. M., Shamsaimon, N., Ishak, K. K., Ramli, S., Amran, M. F. M., & Sukardi, S. (2021). Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning. Journal of Big Data, 8(1), 152. https://doi.org/10.1186/s40537-021-00542-7
Reggiani, A., Nijkamp, P., & Lanzi, D. (2015). Transport resilience and vulnerability: The role of connectivity. Transportation Research Part A: Policy and Practice, 81, 4-15. https://doi.org/https://doi.org/10.1016/j.tra.2014.12.012
Rismanchian, O., & Bell, S. (2010). The application of space Syntax in studying the structure of the cities. Journal of Fine Arts: Architecture & Urban Planning, 2(43), 49-56. https://jfaup.ut.ac.ir/article_23063_a26353bffc1b9744ed433cef1abc70d3.pdf
Schrank, D., Eisele, B., Lomax, T., & Bak, J. (2015). 2015 urban mobility scorecard.
Serdar, M. Z., Koç, M., & Al-Ghamdi, S. G. (2022). Urban Transportation Networks Resilience: Indicators, Disturbances, and Assessment Methods. Sustainable Cities and Society, 76, 103452. https://doi.org/https://doi.org/10.1016/j.scs.2021.103452
Simon, K., DI MARTINO, S., & Wolfgang, N. (2014). Predicting Traffic Congestion in Presence of Planned Special Events. Proceedings of the Twentieth International Conference on Distributed Multimedia Systems, Wyndham Pittsburgh University Center, Pittsburgh, USA.
Stauffer, D., & Aharony, A. (2018). Introduction to percolation theory. CRC press.
Sumalee, A., & Kurauchi, F. (2006). Network Capacity Reliability Analysis Considering Traffic Regulation after a Major Disaster. Networks and Spatial Economics, 6(3), 205-219. https://doi.org/10.1007/s11067-006-9280-0
Sun, D. J., Liu, X., Ni, A., & Peng, C. (2014). Traffic Congestion Evaluation Method for Urban Arterials: Case study of Changzhou, China. Transportation Research Record, 2461(1), 9-15. https://doi.org/10.3141/2461-02
Sun, Z., & Wang, Y. (2009). Traffic congestion identification by combining PCA with higher-order Boltzmann machine. Neural Computing and Applications, 18(5), 417-422.
Suryani, E., Hendrawan, R. A., Eadipraja, P. F., Wibisono, A., & Dewi, L. P. (2019). Modelling Reliability of Transportation Systems to Reduce Traffic Congestion. Journal of Physics: Conference Series, 1196(1), 012029. https://doi.org/10.1088/1742-6596/1196/1/012029
Talebpour, A., Mahmassani, H. S., & Hamdar, S. H. (2017). Effect of Information Availability on Stability of Traffic Flow: Percolation Theory Approach. Transportation Research Procedia, 23, 81-100. https://doi.org/https://doi.org/10.1016/j.trpro.2017.05.006
Tang, J., Liu, F., Zhang, W., Zhang, S., & Wang, Y. (2016). Exploring dynamic property of traffic flow time series in multi-states based on complex networks: Phase space reconstruction versus visibility graph. Physica A: Statistical Mechanics and its Applications, 450, 635-648. https://doi.org/https://doi.org/10.1016/j.physa.2016.01.012
Tang, S., & Wang, F. Y. (2006). A PCI-Based Evaluation Method for Level of Services for Traffic Operational Systems. IEEE Transactions on Intelligent Transportation Systems, 7(4), 494-499. https://doi.org/10.1109/TITS.2006.883935
Tao, Y., & Xinmiao, Y. (1998). Fuzzy comprehensive assessment, fuzzy clustering analysis and its application for urban traffic environment quality evaluation. Transportation Research Part D: Transport and Environment, 3(1), 51-57. https://doi.org/https://doi.org/10.1016/S1361-9209(97)00021-7
Tarjan, R. (1972). Depth-first search and linear graph algorithms. SIAM journal on computing, 1(2), 146-160.
Tavassoli Hojati, A., Ferreira, L., Washington, S., Charles, P., & Shobeirinejad, A. (2016). Reprint of: Modelling the impact of traffic incidents on travel time reliability. Transportation Research Part C: Emerging Technologies, 70, 86-97. https://doi.org/https://doi.org/10.1016/j.trc.2016.06.013
The Virtual and the Real in Planning and Urban Design. (2017). (C. Yamu, A. Poplin, O. Devisch, & G. D. Roo, Eds. 1 ed.). Routledge.
Wan, W., & Kang, J. (1994). Highway transportation comprehensive evaluation. Computers & Industrial Engineering, 27(1), 257-259. https://doi.org/https://doi.org/10.1016/0360-8352(94)90284-4
Wang, F., Li, D., Xu, X., Wu, R., & Havlin, S. (2015). Percolation properties in a traffic model. Europhysics Letters, 112(3), 38001.
Wang, F., & Xu, Y. (2011). Estimating O–D travel time matrix by Google Maps API: implementation, advantages, and implications. Annals of GIS, 17(4), 199-209. https://doi.org/10.1080/19475683.2011.625977
Wang, M., & Debbage, N. (2021). Urban morphology and traffic congestion: Longitudinal evidence from US cities. Computers, Environment and Urban Systems, 89, 101676. https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2021.101676
Wang, W.-X., Guo, R.-J., & Yu, J. (2018). Research on road traffic congestion index based on comprehensive parameters: Taking Dalian city as an example. Advances in Mechanical Engineering, 10(6), 1687814018781482. https://doi.org/10.1177/1687814018781482
Wang, Y., Papageorgiou, M., Gaffney, J., Papamichail, I., & Guo, J. (2010, 19-22 Sept. 2010). Local ramp metering in the presence of random-location bottlenecks downstream of a metered on-ramp. 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal.
Wu, Y., Tan, H., Qin, L., Ran, B., & Jiang, Z. (2018). A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies, 90, 166-180. https://doi.org/https://doi.org/10.1016/j.trc.2018.03.001
Xia, N., Cheng, L., Chen, S., Wei, X., Zong, W., & Li, M. (2018). Accessibility based on Gravity-Radiation model and Google Maps API: A case study in Australia. Journal of Transport Geography, 72, 178-190. https://doi.org/https://doi.org/10.1016/j.jtrangeo.2018.09.009
Yan, G., Zhou, T., Hu, B., Fu, Z.-Q., & Wang, B.-H. (2006). Efficient routing on complex networks. Physical Review E, 73(4), 046108. https://doi.org/10.1103/PhysRevE.73.046108
Zeng, G., Li, D., Guo, S., Gao, L., Gao, Z., Stanley, H. E., & Havlin, S. (2019). Switch between critical percolation modes in city traffic dynamics. Proceedings of the National Academy of Sciences, 116(1), 23-28. https://doi.org/10.1073/pnas.1801545116
Zhang, W., Lu, J., & Zhang, Y. (2016). Comprehensive Evaluation Index System of Low Carbon Road Transport Based on Fuzzy Evaluation Method. Procedia Engineering, 137, 659-668. https://doi.org/https://doi.org/10.1016/j.proeng.2016.01.303
Zhang, X., Miller-Hooks, E., & Denny, K. (2015). Assessing the role of network topology in transportation network resilience. Journal of Transport Geography, 46, 35-45. https://doi.org/https://doi.org/10.1016/j.jtrangeo.2015.05.006
Zhao, J., Li, D., Sanhedrai, H., Cohen, R., & Havlin, S. (2016). Spatio-temporal propagation of cascading overload failures in spatially embedded networks. Nature Communications, 7(1), 10094. https://doi.org/10.1038/ncomms10094
Zhao, S., Zhao, P., & Cui, Y. (2017). A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China. Physica A: Statistical Mechanics and its Applications, 478, 143-157. https://doi.org/https://doi.org/10.1016/j.physa.2017.02.069
Zhao, X., Hu, L., Wang, X., & Wu, J. (2022). Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems. Sustainability, 14(24).
交通部運輸研究所. (2022). 2022年臺灣公路容量手冊. 交通部運輸研究所.
吴若乾, 周勇, & 陈振武. (2019). 基于渗流理论的城市交通网络瓶颈识别研究. 城市交通, 17(1), 96-101.
研擬運輸系統因應氣候變遷調適策略. (2023). https://www.iot.gov.tw/zh_tw/research/construct/c1/?wiki=5480591
鄭屹翔, & 李子璋. (2023). 應用空間型構指標判斷道路層級之可行性 [The Feasibility of Applying Space Syntax to Measuring Road Hierarchy]. 都市與計劃, 50(3), 417-441. https://doi.org/10.6128/CP.202309_50(3).0003