透過數據分析來提昇道路駕駛的安全與降低交通事故發生的機率是現今關
注的重要議題，近年來亦有許多學者透過資料探勘方法進行行車軌跡、車道變換、
駕駛行為分類等研究。行車軌跡或駕駛行為往往透過時間序列資料呈現，相近時
點的行為之間存在特定的相依性，然考量動態時間下的駕駛行為變化之分析相對
匱乏。本研究將利用軌跡數據以時間序列分群法判別駕駛員的風險行為模式並進
行軌跡預測。本研究透過車輛軌跡數據，建構駕駛行為與駕駛風險指標之多維度
時間序列資料，據以進行分群，並利用駕駛行為及風險的分群結果來強化軌跡預
測，發展出可以將駕駛風格分類的參考模型，並討論風險行為分類加入軌跡預測
模型後的結果。研究之結果亦可用於識別駕駛員的風險等級，提供未來自動駕駛
以及保險相關定價策略的決策依據 。

Improving road driving safety and reducing the probability of traffic accidents
through data analysis have become an important issue of concern today. In recent years, many scholars have used data exploration methods to conduct research on driving trajectories, lane changes, and driving behavior classification. Driving trajectories or driving behaviors are often presented through time series data. There are specific dependencies between behaviors at similar points in time. However, analysis of driving behavior changes under dynamic time is relatively scarce. This research will use the trajectory data to identify the driver’s risk behavior pattern and predict the trajectory by time series clustering. This study uses vehicle trajectory data to construct multidimensional time series data of driving behavior and driving risk indicators, group them, and by using the results of driving behavior and risk grouping, develop a reference model that can categorize driving styles and strengthen trajectory prediction. The results of the research can also be used to identify the driver’s risk level and provide a
basis for decision-making on future autonomous driving and insurance-related pricing strategies.

參考文獻
Abou Elassad, Z. E., Mousannif, H., Al Moatassime, H., & Karkouch, A. (2020). The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review. Engineering Applications of Artificial Intelligence, 87, 103312.
Aghabozorgi, S., Shirkhorshidi, A. S., & Wah, T. Y. (2015). Time-series clustering–a decade review. Information Systems, 53, 16-38.
Altché, F., & de La Fortelle, A. (2017, Oct 16-19). An LSTM network for highway trajectory prediction. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.
Archer, J. (2005). Indicators for traffic safety assessment and prediction and their application in micro-simulation modelling: A study of urban and suburban intersections KTH].
Barrios, C., & Motai, Y. (2011). Improving estimation of vehicle's trajectory using the latest global positioning system with Kalman filtering. IEEE Transactions on Instrumentation and Measurement, 60(12), 3747-3755.
Chen, H.-K., Chou, H.-W., Su, J.-W., & Wen, F.-H. (2019). Structural interrelationships of safety climate, stress, inattention and aberrant driving behavior for bus drivers in Taiwan. Transportation research part A: policy and practice, 130, 118-133.
Chen, T., Shi, X., & Wong, Y. D. (2021). A lane-changing risk profile analysis method based on time-series clustering. Physica A: Statistical Mechanics and its Applications, 565, 125567.
Fan, J., Li, Y., Liu, Y., Zhang, Y., & Ma, C. (2019, June 9-12). Analysis of taxi driving behavior and driving risk based on trajectory data. 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.
Fernández, S., Graves, A., & Schmidhuber, J. (2007, Sept 9-13). An application of recurrent neural networks to discriminative keyword spotting. In A. L. A. de Sá J.M., Duch W., Mandic D, Lecture Notes in Computer Science International Conference on Artificial Neural Networks, Porto, Portugal.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning book. MIT Press, 521(7553), 800.
Guido, G., Saccomanno, F., Vitale, A., Astarita, V., & Festa, D. (2011). Comparing safety performance measures obtained from video capture data. Journal of transportation engineering, 137(7), 481-491.
Guo, F., & Fang, Y. (2013). Individual driver risk assessment using naturalistic driving data. Accident Analysis & Prevention, 61, 3-9.
Han, J., Kamber, M., & Pei, J. (2011). Data mining concepts and techniques third edition. The Morgan Kaufmann Series in Data Management Systems, 5(4), 83-124.
He, T., & Droppo, J. (2016, Mar 20-25). Exploiting LSTM structure in deep neural networks for speech recognition. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.
Higgs, B., & Abbas, M. (2013, Oct 6-9). A two-step segmentation algorithm for behavioral clustering of naturalistic driving styles. 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, Netherlands.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
Houenou, A., Bonnifait, P., Cherfaoui, V., & Yao, W. (2013, Nov 3-7). Vehicle trajectory prediction based on motion model and maneuver recognition. 2013 IEEE/RSJ international conference on intelligent robots and systems, Tokyo, Japan.
Hsu, W.-N., Zhang, Y., Lee, A., & Glass, J. (2016). Exploiting depth and highway connections in convolutional recurrent deep neural networks for speech recognition. cell, 50, 1. (INTERSPEECH)
Keogh, E., & Ratanamahatana, C. A. (2005). Exact indexing of dynamic time warping. Knowledge and information systems, 7(3), 358-386.
Kim, B., Kang, C. M., Kim, J., Lee, S. H., Chung, C. C., & Choi, J. W. (2017, Oct 16-19). Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.
Kumar, P., Perrollaz, M., Lefevre, S., & Laugier, C. (2013, June 23-26). Learning-based approach for online lane change intention prediction. 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, Australia.
Liao, T. W. (2005). Clustering of time series data—a survey. Pattern Recognition, 38(11), 1857-1874.
Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.
Mahmud, S. S., Ferreira, L., Hoque, M. S., & Tavassoli, A. (2017). Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs. IATSS research, 41(4), 153-163.
Mandalia, H. M., & Salvucci, M. D. D. (2005, Sept 1). Using support vector machines for lane-change detection. Proceedings of the human factors and ergonomics society annual meeting, Los Angeles, CA.
Mavrogiannis, A., & Liu, C. (2020). Human Driver Behavior Classification from Partial Trajectory Observation.
Nai, W., Chen, Y., Yu, Y., Zhang, F., Dong, D., & Zheng, W. (2016, Mar 12-14). Fuzzy risk mode and effect analyasis based on raw driving data for pay-how-you-drive vehicle insurance. 2016 IEEE International Conference on Big Data Analysis (ICBDA), Hangzhou, China.
Naji, H. A., Xue, Q., Zheng, K., & Lyu, N. (2020). Investigating the significant individual historical factors of driving risk using hierarchical clustering analysis and quasi-poisson regression model. Sensors, 20(8), 2331.
Palangi, H., Deng, L., Shen, Y., Gao, J., He, X., Chen, J., Song, X., & Ward, R. (2016). Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(4), 694-707.
Park, S. H., Kim, B., Kang, C. M., Chung, C. C., & Choi, J. W. (2018, June 30-July 1). Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture. 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, Suzhou, China.
Qu, Z., Haghani, P., Weinstein, E., & Moreno, P. (2017, Dec 16-20). Syllable-based acoustic modeling with CTC-SMBR-LSTM. 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Okinawa, Japan.
Sak, H., Senior, A., & Beaufays, F. (2014). Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv preprint arXiv:1402.1128.
Sak, H., Senior, A. W., & Beaufays, F. (2014). Long short-term memory recurrent neural network architectures for large scale acoustic modeling.
Shi, X., Wong, Y., Li, M., & Chai, C. (2018). Key risk indicators for accident assessment conditioned on pre-crash vehicle trajectory. Accident Analysis & Prevention, 117, 346-356.
Shi, X., Wong, Y. D., Li, M. Z.-F., Palanisamy, C., & Chai, C. (2019). A feature learning approach based on XGBoost for driving assessment and risk prediction. Accident Analysis & Prevention, 129, 170-179.
U.S. Department of Transportation Federal Highway Administration. (2016, June 23, 2020). Next Generation Simulation (NGSIM) Vehicle Trajectories and Supporting Data. Retrieved Sept 1 from https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Vehicle-Trajector/8ect-6jqj
Uno, N., Iida, Y., Yasuhara, S., & SUGANUMA, M. (2003). Objective analysis of traffic conflict and modeling of vehicular speed adjustment at weaving section. Infrastructure Planning Review, 20, 989-996.
Van der Horst, A. R. A. (1991). A time-based analysis of road user behaviour in normal and critical encounters.
Wiki Commons. (2018, Oct 23, 2020). File:Euclidean vs. DTW.jpg. Retrieved Sept 18 from https://commons.wikimedia.org/wiki/File:Euclidean_vs_DTW.jpg#/media/File:Euclidean_vs_DTW.jpg
Woo, H., Ji, Y., Kono, H., Tamura, Y., Kuroda, Y., Sugano, T., Yamamoto, Y., Yamashita, A., & Asama, H. (2017). Lane-change detection based on vehicle-trajectory prediction. IEEE Robotics and Automation Letters, 2(2), 1109-1116.
World Health Organization. (2018). Global status report on road safety 2018: summary.
Yang, L., Ma, R., Zhang, H. M., Guan, W., & Jiang, S. (2018). Driving behavior recognition using EEG data from a simulated car-following experiment. Accident Analysis & Prevention, 116, 30-40.
Zaremba, W., Sutskever, I., & Vinyals, O. (2014). Recurrent neural network regularization. arXiv preprint arXiv:1409.2329.