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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許聿廷 | zh_TW |
| dc.contributor.advisor | Yu-Ting Hsu | en |
| dc.contributor.author | 陳勝文 | zh_TW |
| dc.contributor.author | Sheng-Wen Chen | en |
| dc.date.accessioned | 2024-08-16T17:09:39Z | - |
| dc.date.available | 2024-08-17 | - |
| dc.date.copyright | 2024-08-16 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-07 | - |
| dc.identifier.citation | Ali, M., Yusof, K. M., Wilson, B., & Ziegelmueller, C. （2022）. Traffic speed prediction of high‐frequency time series using additively decomposed components as features. IET Smart Cities, 4（2）, 92–109. https://doi.org/10.1049/smc2.12027
Ali, M., Yusof, K. M., Wilson, B., & Ziegelmueller, C. （2023）. Traffic speed prediction using GARCH‐GRU hybrid model. IET Intelligent Transport Systems, 17（11）, 2300–2312. https://doi.org/10.1049/itr2.12411 Asif, M. T., Dauwels, J., Chong Yang Goh, Oran, A., Fathi, E., Muye Xu, Dhanya, M. M., Mitrovic, N., & Jaillet, P. （2014）. Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction. IEEE Transactions on Intelligent Transportation Systems, 15（2）, 794–804. https://doi.org/10.1109/TITS.2013.2290285 Ben-Akiva, M., Cuneo, D., Hasan, M., Jha, M., & Yang, Q. （2003）. Evaluation of freeway control using a microscopic simulation laboratory. Transportation Research Part C: Emerging Technologies, 11（1）, 29–50. https://doi.org/10.1016/S0968-090X（02）00020-7 Dumoulin, V., & Visin, F. （2018）. A guide to convolution arithmetic for deep learning （arXiv:1603.07285）. arXiv. http://arxiv.org/abs/1603.07285 Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. （2014）. Generative Adversarial Networks. https://doi.org/10.48550/ARXIV.1406.2661 Haar, L. V., Elvira, T., & Ochoa, O. （2023）. An analysis of explainability methods for convolutional neural networks. Engineering Applications of Artificial Intelligence, 117, 105606. https://doi.org/10.1016/j.engappai.2022.105606 Inoue, R., & Miyashita, A. （2021）. Short-Term Traffic Speed Prediction Based on Fundamental and Cointegration Relationship of Speed–Density in Non-Congested and Congested States. IEEE Open Journal of Intelligent Transportation Systems, 2, 470–481. https://doi.org/10.1109/OJITS.2021.3133573 Jha, M., Cuneo, D., & Ben-Akiva, M. （1999）. Evaluation of Freeway Lane Control for Incident Management. Journal of Transportation Engineering, 125（6）, 495–501. https://doi.org/10.1061/（ASCE）0733-947X（1999）125:6（495） Jia, Y., Wu, J., Ben‐Akiva, M., Seshadri, R., & Du, Y. （2017）. Rainfall‐integrated traffic speed prediction using deep learning method. IET Intelligent Transport Systems, 11（9）, 531–536. https://doi.org/10.1049/iet-its.2016.0257 K, H. Priya., Shankar, K. V. R. R., Prasad, C. S. R. K., & Reddy, T. S. （2013）. Evaluation of Area Traffic Management Measures Using Microscopic Simulation Model. Procedia - Social and Behavioral Sciences, 104, 815–824. https://doi.org/10.1016/j.sbspro.2013.11.176 Ke, R., Li, W., Cui, Z., & Wang, Y. （2020）. Two-Stream Multi-Channel Convolutional Neural Network for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact. Transportation Research Record: Journal of the Transportation Research Board, 2674（4）, 459–470. https://doi.org/10.1177/0361198120911052 Li, Y., & Bai, Y. （2009）. Effectiveness of temporary traffic control measures in highway work zones. Safety Science, 47（3）, 453–458. https://doi.org/10.1016/j.ssci.2008.06.006 Liu, D., Tang, L., Shen, G., & Han, X. （2019）. Traffic Speed Prediction: An Attention-Based Method. Sensors, 19（18）, 3836. https://doi.org/10.3390/s19183836 Liu, D., Xu, X., Xu, W., & Zhu, B. （2021）. Graph Convolutional Network: Traffic Speed Prediction Fused with Traffic Flow Data. Sensors, 21（19）, 6402. https://doi.org/10.3390/s21196402 Lu, W., Yi, Z., Wu, R., Rui, Y., & Ran, B. （2022）. Traffic speed forecasting for urban roads: A deep ensemble neural network model. Physica A: Statistical Mechanics and Its Applications, 593, 126988. https://doi.org/10.1016/j.physa.2022.126988 Ma, T., Antoniou, C., & Toledo, T. （2020）. Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast. Transportation Research Part C: Emerging Technologies, 111, 352–372. https://doi.org/10.1016/j.trc.2019.12.022 Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., & Wang, Y. （2017）. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors, 17（4）, 818. https://doi.org/10.3390/s17040818 Nissan, A., & Koutsopoulosb, H. N. （2011）. Evaluation of the Impact of Advisory Variable Speed Limits on Motorway Capacity and Level of Service. Procedia - Social and Behavioral Sciences, 16, 100–109. https://doi.org/10.1016/j.sbspro.2011.04.433 Perraki, G., Roncoli, C., Papamichail, I., & Papageorgiou, M. （2018）. Evaluation of a model predictive control framework for motorway traffic involving conventional and automated vehicles. Transportation Research Part C: Emerging Technologies, 92, 456–471. https://doi.org/10.1016/j.trc.2018.05.002 Radford, A., Metz, L., & Chintala, S. （2015）. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. https://doi.org/10.48550/ARXIV.1511.06434 Ranjan, N., Bhandari, S., Zhao, H. P., Kim, H., & Khan, P. （2020）. City-Wide Traffic Congestion Prediction Based on CNN, LSTM and Transpose CNN. IEEE Access, 8, 81606–81620. https://doi.org/10.1109/ACCESS.2020.2991462 Sousa Santos, G., Sundvor, I., Vogt, M., Grythe, H., Haug, T. W., Høiskar, B. A., & Tarrason, L. （2020）. Evaluation of traffic control measures in Oslo region and its effect on current air quality policies in Norway. Transport Policy, 99, 251–261. https://doi.org/10.1016/j.tranpol.2020.08.025 Tang, J., Liu, F., Zou, Y., Zhang, W., & Wang, Y. （2017）. An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic. IEEE Transactions on Intelligent Transportation Systems, 18（9）, 2340–2350. https://doi.org/10.1109/TITS.2016.2643005 Wang, F., Lu, Y., Dai, H., & Han, H. （2021）. Evaluation of Freeway Traffic Management and Control Measures Based on SUMO. Journal of Physics: Conference Series, 1910（1）, 012044.https://doi.org/10.1088/1742-6596/1910/1/012044 Xin, W., Hourdos, J., & Michalopoulos, P. G. （2006）. Comprehensive Evaluation of New Integrated Freeway Ramp Control Strategy. Transportation Research Record: Journal of the Transportation Research Board, 1959（1）, 46–54. https://doi.org/10.1177/0361198106195900106 Xu, X., Zhang, T., Xu, C., Cui, Z., & Yang, J. （2023）. Spatial–Temporal Tensor Graph Convolutional Network for Traffic Speed Prediction. IEEE Transactions on Intelligent Transportation Systems, 24（1）, 92–103. https://doi.org/10.1109/TITS.2022.3215613 Yang, X., Yuan, Y., & Liu, Z. （2020）. Short-Term Traffic Speed Prediction of Urban Road With Multi-Source Data. IEEE Access, 8, 87541–87551. https://doi.org/10.1109/ACCESS.2020.2992507 Yao, B., Chen, C., Cao, Q., Jin, L., Zhang, M., Zhu, H., & Yu, B. （2017）. Short‐Term Traffic Speed Prediction for an Urban Corridor. Computer-Aided Civil and Infrastructure Engineering, 32（2）, 154–169. https://doi.org/10.1111/mice.12221 Yu, B., Lee, Y., & Sohn, K. （2020）. Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network （GCN）. Transportation Research Part C: Emerging Technologies, 114, 189–204. https://doi.org/10.1016/j.trc.2020.02.013 Yu, B., Song, X., Guan, F., Yang, Z., & Yao, B. （2016）. K-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition. Journal of Transportation Engineering, 142（6）, 04016018. https://doi.org/10.1061/（ASCE）TE.1943-5436.0000816 Zhang, Z., Li, M., Lin, X., Wang, Y., & He, F. （2019）. Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies. Transportation Research Part C: Emerging Technologies, 105, 297–322. https://doi.org/10.1016/j.trc.2019.05.039 Zheng, G., Chai, W. K., & Katos, V. （2022）. A dynamic spatial–temporal deep learning framework for traffic speed prediction on large-scale road networks. Expert Systems with Applications, 195, 116585. https://doi.org/10.1016/j.eswa.2022.116585 Zhou, Z., Yang, Z., Zhang, Y., Huang, Y., Chen, H., & Yu, Z. （2022）. A comprehensive study of speed prediction in transportation system: From vehicle to traffic. iScience, 25（3）, 103909. https://doi.org/10.1016/j.isci.2022.103909 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94624 | - |
| dc.description.abstract | 國道為聯絡縣市間重要之公路系統，其中國道1號、國道3號、國道5號等縱向國道尤其為南北往來的重要公路。連假期間大量車流湧上國道，造成國道多路段、長時間的壅塞，高速公路局於國道1號、國道3號及國道5號實施多項交通管理措施，包含入口匝道封閉、入口高乘載管制及收費措施調整等措施。國道交管措施會產生國道上多路段長時間的車流影響，然而其帶來的影響難以評估，主要係因為國道交管措施實施的時間、空間、形式皆不相同，對於用路人產生的效果亦不相同。
本研究提出以深度學習方法建構之圖片基礎速度預測模型，模型根據所欲預測日期本身的屬性、預定交管措施以及百萬延車公里（Million Vehicle-Kilometers, MVK） 便能夠預測出該日的時空圖，實際以真實資料訓練，並且針對驗證資料進行預測後，各模型絕對百分誤差（Mean Absolute Perntage Error, MAPE） 介於4~7%，顯示出模型優良的預測能力。此外，為了實際應用於交管措施評估，本研究提出擾動基礎的模型解釋方法，透過改變輸入觀察輸出的方式檢視不同交管措施帶來的影響，結果顯示多項交管措施符合預期的變化，模型能夠合理學習並輸出交管措施帶來的影響，可作為管理機關用以評估交管措施之工具。 | zh_TW |
| dc.description.abstract | National freeways are important highway systems connecting counties and cities, and the vertical freeways such as Freeway 1, Freeway 3, and Freeway 5 are especially important national freeways for north-south travel. During the vacation, lots of vehicles poured onto the national freeways, causing long-term congestion in many sections of the national freeways. The freeway bureau implemented several traffic control measures on Freeway 1, Freeway 3, and Freeway 5, including closing entrance ramps, HOV control of entrance ramps, and adjusting toll collection measures. Traffic control measures on national freeways will have a long-term impact on traffic flow in multiple sections of national freeways, but the impact is difficult to assess, mainly because the time, space and form of implementation of traffic control measures on national freeways are all different, and the effects on road users are also different.
This study proposes an image-based speed prediction model using a deep learning approach. The model can predict the time-space diagram of the day based on the properties of the predicted date itself, the predetermined traffic control measures, and the Million Vehicle-Kilometers（MVK）. After training with real data and predictions on validation data, the Mean Absolute Percentage Error（MAPE）of each model is between 4% and 7%, showing the excellent prediction ability of the model. In addition, in order to apply the assessment of traffic control measures, this study proposes a perturbation-based model explanation method, which examines the impact of different traffic control measures by changing the input and observing the output. The results show that multiple traffic control measures are in line with the expected changes, and the model can reasonably learn and output the impact of traffic control measures. This model can be a supporting tool for the authority to evaluate traffic control measures. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-16T17:09:39Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-16T17:09:39Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii 英文摘要 iii 目 次 v 圖 次 vii 表 次 xiii 第一章 緒論 1 1.1 背景介紹 1 1.2 研究動機 1 1.3 研究目的 3 第二章 文獻回顧 5 2.1 交管措施評估 5 2.1.1 非高速公路 5 2.1.2 高速公路 6 2.1.3 交管措施評估小結 7 2.2 速度預測 7 2.2.1 路段基礎 8 2.2.2 路網基礎 11 2.2.3 圖片基礎 11 2.2.4 速度預測小結 12 2.3 卷積層架構及可解釋性 14 2.4 小結 15 第三章 研究方法 17 3.1 資料及變數 17 3.1.1 輸入變數 17 3.1.2 輸出變數 19 3.2 研究範圍 22 3.3 圖片基礎速度預測模型 23 3.3.1 轉置卷積層 23 3.3.2 模型建構 25 第四章 模型分析結果 30 4.1 預測績效 30 4.2 時空圖比較 32 4.2.1 國道1號 32 4.2.2 國道3號 47 4.2.3 國道5號 62 4.3 小結 78 第五章 交管措施評估 84 5.1 交管措施評估方法 84 5.2 交管措施影響 85 5.2.1 入口匝道封閉 85 5.2.2 入口高乘載管制 100 5.2.3 收費措施調整 105 5.3 小結 117 第六章 結論與建議 121 參考文獻 125 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 車流速度預測 | zh_TW |
| dc.subject | 轉置卷積網路 | zh_TW |
| dc.subject | 國道交管措施評估 | zh_TW |
| dc.subject | 擾動基礎模型解釋 | zh_TW |
| dc.subject | Perturbation-Based Model Explanation | en |
| dc.subject | Deep Learning | en |
| dc.subject | Traffic Speed Prediction | en |
| dc.subject | Transposed Convolution Network | en |
| dc.subject | Assessment of Freeway Traffic Control Measures | en |
| dc.title | 以深度學習方法評估國道交管措施時空影響 | zh_TW |
| dc.title | Assessing Time-Space Influence of Traffic Control Measures on National Freeways Using Deep Learning Approaches | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳柏華;胡守任;劉瀚聰 | zh_TW |
| dc.contributor.oralexamcommittee | Albert Y. Chen;Shou-Ren Hu;Han-Tsung Liou | en |
| dc.subject.keyword | 深度學習,車流速度預測,轉置卷積網路,國道交管措施評估,擾動基礎模型解釋, | zh_TW |
| dc.subject.keyword | Deep Learning,Traffic Speed Prediction,Transposed Convolution Network,Assessment of Freeway Traffic Control Measures,Perturbation-Based Model Explanation, | en |
| dc.relation.page | 129 | - |
| dc.identifier.doi | 10.6342/NTU202401973 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-11 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| 顯示於系所單位： | 土木工程學系 | |
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