進階搜尋


下載電子全文  
系統識別號 U0026-2107201900481500
論文名稱(中文) 客運車隊駕駛員偏差駕駛行為分析及駕駛風險等級評估之研究
論文名稱(英文) Aberrant Driving Behavior Analysis and Driving Risk Level Assessment for Inter-city Bus Fleet
校院名稱 成功大學
系所名稱(中) 交通管理科學系
系所名稱(英) Department of Transportation & Communication Management Science
學年度 107
學期 2
出版年 108
研究生(中文) 賴家偉
研究生(英文) Jia-Wei Lai
電子信箱 david64321@gmail.com
學號 r56061130
學位類別 碩士
語文別 英文
論文頁數 134頁
口試委員 指導教授-魏健宏
口試委員-林佐鼎
口試委員-陳宥欽
中文關鍵字 客運駕駛員  人因特性  偏差駕駛行為  類神經網路  駕駛風險分級 
英文關鍵字 Bus drivers  Human factors  Aberrant driving behavior  Artificial neural network  Driving risk level 
學科別分類
中文摘要 客運業近年來因為擁有逐漸提升的服務品質與便宜的價格而佔有一席之地。然而,與客運車輛有關之交通事故常會伴隨著嚴重的人員傷亡與未知的經濟損失,現已成為不可忽視的安全問題,且發生事故亦會降低消費者對客運公司的聲譽與信賴。客運業者近年除了關注於車輛操作影響經濟油耗的問題外,亦逐漸重視駕駛人的駕駛行為對交通安全所造成的影響。超過九成的交通事故是由人為因素所引起,且駕駛行為亦會受到人因特性所影響,理應探討人因特性對駕駛行為之影響,進而降低發生交通事故的潛在因素。
本研究配合個案客運公司調查客運業職業駕駛員的人因特性資料,並透過智慧行車紀錄器取得偏差駕駛行為資料。首先藉由偏差駕駛行為資料建立駕駛風險分級評估機制,將駕駛員依據潛在危險程度區分為高低等級,而後探討人因特性可能導致的偏差駕駛行為及駕駛風險分級結果,透過量化駕駛員的駕駛風險以進行有效的車隊管理。本研究應用人工神經網路(Artificial neural network, ANN)模型建構人因特性與個別偏差駕駛行為和整體駕駛風險分級結果之間的關聯性模型,模型表現出良好的預測準確度,接著透過斯皮爾曼相關得出駕駛疲勞、症狀疾病及高神經質是造成駕駛風險的主要因素,充足的睡眠、高和善性及高年家戶所得則是減少駕駛風險的主要因素。針對研究結果提出管理建議,期望能在事故前端的角度來防範交通事故的發生,進一步提升企業的安全誠信與聲譽。
英文摘要 Due to the growing trend of public transportation, inter-city bus (highway bus) as ground transport plays an essential role in Taiwan. However, the traffic accidents involving with bus usually accompany serious casualties and financial loss. The fatality rate of bus are 10 times higher than sedans in recent years, and it shows urgency of bus safety in Taiwan. Thus, driving behavior is gradually valued by inter-bus carriers. The bus driver is charged with serious responsibility, and his driving behavior is influenced to a significant degree by his own human factors.
This study will collect information of inter-city bus driver human factors and the status of aberrant driving behavior from the case study company. A relative risk level evaluation mechanism will be developed based on the frequency and distribution of aberrant driving behavior. The research aims to enhance efficiency of the fleet management system and highway safety in general through quantifying relative driving risk of each driver. We apply artificial neural network (ANN) models to predict the frequency of aberrant driving behavior and the risk level of each driver by individual human factors. The predictive models perform high accuracy in case. Spearman correlation coefficient was used to calculate the correlation between the human factors and driving risk. Driving fatigue, symptom, disease and high neuroticism would cause high driving risk; Enough sleep hours, high agreeableness and high annual household income lead to low driving risk. By establishing a systematic driving risk assessment mechanism, inter-city bus industry can reduce the occasion of traffic accidents and further raise corporate integrity and reputation.
論文目次 ABSTRACT i
摘要 ii
誌謝 iii
TABLE OF CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES ix
CHAPTER 1 INTRODUCTION 1
1.1 Research background and motivation 1
1.2 Research objectives 3
1.3 Research flow chart 5
CHAPTER 2 LITERATURE REVIEW 8
2.1 Human Factors 8
2.1.1 Ergonomics 9
2.1.2 Personality 11
2.1.3 Human factors affecting driving performance 13
2.2 Driving Behavior 15
2.2.1 Aberrant driving behavior 16
2.2.2 Driving risk 19
2.2.3 Studies of driving behavior 20
2.3 Performance 23
2.3.1 Job performance 23
2.3.2 Driving performance 24
2.3.3 Studies of driving performance 27
2.4 Driver Grading System 28
2.4.1 Grading system in transportation 28
2.4.2 Studies of driver grading system 29
2.5 Summary 31
CHAPTER 3 RESEARCH METHODOLOGY 34
3.1 Research Structure 34
3.2 Research Variable and Questionnaire Design 37
3.2.1 Driver’s human factors 38
3.2.2 Aberrant driving behavior 48
3.3 Research Methodology 50
3.3.1 Literature review and organization 50
3.3.2 Data collection 50
3.3.3 Descriptive statistical analysis 51
3.3.4 Correlation analysis 51
3.3.5 Box-and-whisker plot 51
3.3.6 Jenks natural breaks optimization 52
3.3.7 Elbow method 54
3.3.8 Artificial Neural Network (ANN) Model 54
3.3.9 Performance assessment 63
CHAPTER 4 EMPIRICAL EXPERIMENT 65
4.1 Team Research Overview 65
4.2 Data Description 67
4.2.1 Description of human factor 68
4.2.2 Description of driving behavior 72
4.2.3 Correlation analysis 73
4.2.4 Adjustment 76
4.3 Driving Risk Conversion 78
4.3.1 Features of driving behavior 78
4.3.2 Single driving risk 80
4.3.3 Converting process 80
4.4 Constructing the Overall Driving Risk Level 87
4.4.1 Overall driving risk 87
4.4.2 Driving risk level 88
4.5 Network Model for Predicting Driving Risk 90
4.5.1 Pre-processing and clarification of Model 90
4.5.2 Single behavior 94
4.5.3 Overall driving risk 95
4.5.4 Sensitivity analysis 97
4.6 Summary 100
CHAPTER 5 CONCLUSIONS AND SUGGESTIONS 102
5.1 Conclusions 102
5.2 Suggestions 107
REFERENCE 109
Appendix A Sample Distribution of Quantitative Human Factors 120
Appendix B Result of Random ANN Models 124
Appendix C Human Factor Questionnaire in Chinese 130
參考文獻 1. Agresti, A. (2018), “An introduction to categorical data analysis,” Wiley.
2. Agresti, A., & Kateri, M. (2011), “Categorical data analysis,” Springer Berlin Heidelberg.
3. Bagnasco, A., Siri, A., Aleo, G., Rocco, G. and Sasso, L. (2015), “Applying artificial neural networks to predict communication risks in the emergency department,” Journal of advanced nursing, Vol. 71, Issue 10, pp. 2293-2304.
4. Benítez, J. M., Castro, J. L., & Requena, I. (1997), “Are artificial neural networks black boxes?” IEEE Transactions on neural networks, Vol. 8, Issue 5, pp. 1156-1164.
5. Bholowalia, P., & Kumar, A. (2014), “EBK-means: A clustering technique based on elbow method and k-means in WSN,” International Journal of Computer Applications, Vol. 105, Issue 9.
6. Bishop, C. M. (1995), “Neural networks for pattern recognition,” Oxford university press.
7. Board of Certification in Professional Ergonomics (1996), Board of Certification in Professional Ergonomics bome world wide web page Available from: http://www.bcpe.org/
8. Bottou, L. (2012), “Stochastic gradient descent tricks,” Neural networks: Tricks of the trade, pp. 421-436, Springer, Berlin, Heidelberg.
9. Campbell, J. P. (1990), “Modeling the performance prediction problem in industrial and organizational psychology,” Handbook of Industrial and Organizational Psychology.
10. Chang, C. L. (2001), “Design of Highway Bus Monitoring Indexes and Development of Driver and Vehicle Database Management System — Application of Digital Vehicle Recorders,” Master thesis, Department of Transportation and Logistics Management, National Chiao Tung University (NCTU), Taiwan.
11. Chapman, B. P., & Goldberg, L. R. (2017), “Act-frequency signatures of the Big Five,” Personality and individual differences, Vol. 116, pp. 201-205.
12. Chen, F. C. (2005), “The study on driving effects of inter-city bus transportation,” Master thesis, Department of Transportation and Communication Management Science, National Cheng Kung University (NCKU), Taiwan.
13. Chen, H. C. (2014), “A Study on Human Factors, Driving Behaviors and Driving Performance for Inter-City Bus Drivers,” Master thesis, Department of Transportation and Communication Management Science, National Cheng Kung University (NCKU), Taiwan.
14. Chiang, F. H. (2004), “A Research on the Effects of Workers’Personality Traits and Work Values on Job Performance from Social Capital Perspective for Taiwan Manufacturing Industry,” Master thesis, Department of Transportation and Communication Management Science, National Cheng Kung University (NCKU), Taiwan.
15. Chliaoutakis, J. E., Darviri, C., & Demakakos, P. T. (1999), “The impact of young drivers’ lifestyle on their road traffic accident risk in greater Athens area,” Accident Analysis & Prevention, Vol. 31, Issue 6, pp. 771-780.
16. Costa, P. T., and McCrae, R. R. (1989), “The NEO-PI/NEO-FFI manual supplement,” Psychological Assessment Resources, Odessa.
17. Costa, P. T., and McCrae, R. R. (1992), “Professional manual for the NEO PI-R and NEO-FFI,” Psychological Assessment Resources, Odessa.
18. Di Milia, L., Smolensky, M. H., Costa, G., Howarth, H. D., Ohayon, M. M., & Philip, P. (2011), “Demographic factors, fatigue, and driving accidents: An examination of the published literature.” Accident Analysis & Prevention, Vol .4, Issue 2, pp. 516-532.
19. Di Milia, L., & Kecklund, G. (2013), “The distribution of sleepiness, sleep and work hours during a long distance morning trip: A comparison between night-and non-night workers,” Accident Analysis & Prevention, Vol. 53, pp. 17-22.
20. Feldman, J. A., & Ballard, D. H. (1982), “Connectionist models and their properties,” Cognitive science, Vol. 6, Issue 3, pp. 205-254.
21. Feng, S., Li, Z., Ci, Y., and Zhang, G. (2016), “Risk factors affecting fatal bus accident severity: Their impact on different types of bus drivers,” Accident Analysis and Prevention, Vol. 86, pp. 29-39.
22. Fiesler, E., & Beale, R. (1996), “Handbook of neural computation,” CRC Press.
23. Glorot, X., & Bengio, Y. (2010), “Understanding the difficulty of training deep feedforward neural networks,” In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249-256.
24. Golden, B. L., Wasil, E. A., Coy, S. P., and Dagli, C. H. (1997), “Neural networks in practice: survey results,” Interfaces in Computer Science and Operations Research, pp. 77-95. Springer, Boston, MA.
25. Gulian, E., Matthews, G., Glendon, A. I., Davies, D. R., and Debney, L. M. (1989), “Dimensions of driver stress,” Ergonomics, Vol. 32, pp. 585-602.
26. Haddon Jr, W. (1980), “Advances in the epidemiology of injuries as a basis for public policy,” Public health reports, Vol. 95, Issue 5, pp. 411-421.
27. Harrison, W. A. (2004), “Investigation of the driving experience of a sample of Victorian learner drivers,” Accident Analysis & Prevention, Vol. 36, Issue 5, pp. 885-891.
28. Hauke, J., & Kossowski, T. (2011), “Comparison of values of Pearson's and Spearman's correlation coefficients on the same sets of data,” Quaestiones geographicae, Vol. 30, Issue 2, pp. 87-93.
29. Haykin, S. (2001), “Neural network: A comprehensive foundation”, Prentice Hall.
30. Huang, Y. H., Sinclair, R. R., Lee, J., McFadden, A. C., Cheung, J. H., & Murphy, L. A. (2018), “Does talking the talk matter? Effects of supervisor safety communication and safety climate on long-haul truckers’ safety performance,” Accident Analysis & Prevention, Vol. 117, pp. 357-367.
31. IEA, 2000. The Discipline of Ergonomics. International Ergonomics Association. Available from: www.iea.cc
32. Jarašūniene, A., & Jakubauskas, G. (2007), “Improvement of road safety using passive and active intelligent vehicle safety systems,” Transport, Vol. 22, Issue 4, pp. 284-289.
33. Jastrzebowski, W. (1857), “An Outline of Ergonomics, or the Science of Work,” Based Upon the Truths drawn from the Science of Nature.
34. Karlik, B., & Olgac, A. V. (2011), “Performance analysis of various activation functions in generalized MLP architectures of neural networks,” International Journal of Artificial Intelligence and Expert Systems, Vol. 1, Issue 4, pp.111-122.
35. Kao, C. Y. (2008), “A Study on the Factors Affecting Driving Fatigue and Close Calls of Inter-city Bus,” Master thesis, Department of Transportation and Communication Management Science, National Cheng Kung University (NCKU), Taiwan.
36. Lajunen, T., & Summala, H. (1995), “Driving experience, personality, and skill and safety-motive dimensions in drivers' self-assessments,” Personality and Individual Differences, Vol. 19, Issue 3, pp. 307-318.
37. Lajunen, T., Parker, D., and Summala, H. (2004), “The Manchester driver behavior questionnaire: a cross-cultural study,” Accident Analysis and Prevention, Vol. 36, pp. 231-238.
38. Lerner, N. D, Steinberg, G. V., & Hanscom, F. R. (1999), “Development of Countermeasures for Driver Maneuver Errors” (No. FHWA-RD-00-022,).
39. Lewis, C. D. (1982), “Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting,” Butterworth-Heinemann.
40. Li, D. H., Liu, Q., Yuan, W., & Liu, H. X. (2010), “Relationship between fatigue driving and traffic accident,” Journal of traffic and transportation engineering (Xi'an, Shaanxi), Vol. 10, Issue 2, pp.104-109.
41. Li, P. Y. (2017), “Personalities Affecting Driving Behaviors and Classifying Mechanism for Driving Risk –A Case of Inter-city Bus Drivers,” Master thesis, Department of Transportation and Communication Management Science, National Cheng Kung University (NCKU), Taiwan.
42. Lin, C. H. (2016), “The Influence of Human Factors and Internal Service Quality on Driving Behavior and Driving Performance: The Case of City Bus Drivers,” Master thesis, Department of Transportation and Communication Management Science, National Cheng Kung University (NCKU), Taiwan.
43. Lin, Y. C. (2009), “The Research of Using the Tachograph Data in Intercity Bus to Probe into Large Passenger Vehicle Drivers’ Driving Performance,” Master thesis, Department of Transportation and Logistics, Feng Chia University (FCU), Taiwan.
44. Mallia, L., Lazuras, L., Violani, C., & Lucidi, F. (2015), “Crash risk and aberrant driving behaviors among bus drivers: the role of personality and attitudes towards traffic safety,” Accident Analysis & Prevention, Vol. 79, pp. 145-151.
45. McCrae, R. R., & John, O. P. (1992), “An introduction to the five‐factor model and its applications,” Journal of personality, Vol. 60, Issue 2, pp. 175-215.
46. Miyajima, C., Ukai, H., Naito, A., Amata, H., Kitaoka. N. and Takeda, K. (2011), “Driver risk evaluation based on acceleration, deceleration, and steering behavior,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1829-1832.
47. Moghaddam, F. R., Afandizadeh, S., and Ziyadi, M. (2011), “Prediction of accident severity using artificial neural networks,” International Journal of Civil Engineering, Vol. 9, Issue 1, pp. 41-49.
48. Monthly Statistics of Transportation and Communications Republic of China, Ministry of Transportation and Communications, 2019.
49. Motowidlo, S. J. (2003), “Job performance. Handbook of psychology: Industrial and organizational psychology, Vol. 12, pp. 39-53.
50. Mullen, N. W., Weaver, B., Riendeau, J. A., Morrison, L. E., & Bédard, M. (2010), “Driving performance and susceptibility to simulator sickness: Are they related?,” American Journal of Occupational Therapy, Vol. 64, Issue 2, pp. 288-295.
51. Murrell, K. (2012), “Ergonomics: Man in his working environment,” Springer Science & Business Media.
52. Naito, A., Miyajima, C., Nishino. T, Kitaoka. N. and Takeda, K. (2009), “Driver evaluation based on classification of rapid decelerating patterns,” IEEE International Conference on Vehicular Electronics and Safety, pp. 108-112.
53. Nasrabadi, N. M. (2007), “Pattern recognition and machine learning,” Journal of electronic imaging, Vol 16, Issue 4, p. 049901.
54. Ngueutsa, R., & Kouabenan, D. R. (2017), “Accident history, risk perception and traffic safe behaviour,” Ergonomics, Vol. 60, Issue 9, pp. 1273-1282.
55. Norris, F. H., Matthews, B. A., & Riad, J. K. (2000), “Characterological, situational, and behavioral risk factors for motor vehicle accidents: a prospective examination,” Accident Analysis & Prevention, Vol. 32, Issue 4, pp. 505-515.
56. Ohayon, M., Wickwire, E. M., Hirshkowitz, M., Albert, S. M., Avidan, A., Daly, F. J., ... & Hazen, N. (2017), “National Sleep Foundation's sleep quality recommendations: first report,” Sleep Health, Vol. 3, Issue 1, pp. 6-19.
57. Öz, B., Özkan, T., & Lajunen, T. (2014), “Trip-focused organizational safety climate: Investigating the relationships with errors, violations and positive driver behaviours in professional driving,” Transportation research part F: traffic psychology and behaviour, Vol. 26, pp. 361-369.
58. Pan, W. N. (2006), “A Study on the Factors Affecting Driving Performance and Fuel Consumption of Intercity Bus,” Master thesis, Department of Transportation and Communication Management Science, National Cheng Kung University (NCKU), Taiwan.
59. Pérez-Marín, A. M., Ayuso, M., & Guillen, M. (2019), “Do young insured drivers slow down after suffering an accident?” Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 62, pp. 690-699.
60. Pheasant, S. (2014), “Bodyspace: Anthropometry, Ergonomics and the Design of Work: Anthropometry, Ergonomics and The Design of Work,” CRC Press.
61. Reason, J., Manstead, A., Stradling, S., Baxter, J. and Campbell, K. (1990), “Errors and violations on the roads: a real distinction?” Ergonomics, Vol. 33, pp. 1315-1332.
62. Rimmö, P. A., & Åberg, L. (1999), “On the distinction between violations and errors: sensation seeking associations,” Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 2, Issue 3, pp. 151-166.
63. Roccas, S., Sagiv, L., Schwartz, S. H., & Knafo, A. (2002), “The big five personality factors and personal values,” Personality and social psychology bulletin, Vol. 28, Issue 6, pp. 789-801.
64. Ronen, S., & Zuroff, D. C. (2017), “How does secure attachment affect job performance and job promotion? The role of social-rank behaviors,” Journal of Vocational Behavior, Vol. 100, pp. 137-148.
65. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986), “Learning representations by back-propagating errors,” Nature, 323, pp. 533-536.
66. Specht, D. F. (1990), “Probabilistic neural networks,” Neural networks, Vol. 3, Issue 1, pp.109-118.
67. Stutts J, Wilkins J, Scott O, Vaughn B (2003), “Driver risk factors for sleep-related crashes,” Accident Analysis and Prevention, Vol. 35, Issue 3, pp.321–331
68. Sullman, M. J. M., Meadows, M. L. & Pajo, K. B. (2002), “Aberrant driving behaviours among New Zealand,” Transportation Research Part F, Vol. 5, pp. 217-232.
69. Sun, S. C. (2012), “The association between sleep quality and traffic accidents among commercial bus drivers,” Master thesis, Graduate Institute of Injury Prevent and Control, Taipei Medical University (TMU), Taiwan.
70. Tavana, M., Abtahi, A. R., Di Caprio, D. and Poortarigh, M. (2017), “An Artificial Neural Network and Bayesian Network Model for Liquidity Risk Assessment in Banking,” Neurocomputing.
71. Tetko, I. V., Livingstone, D. J., & Luik, A. I. (1995), “Neural network studies. 1. Comparison of overfitting and overtraining,” Journal of chemical information and computer sciences, Vol. 35, Issue 5, pp. 826-833.
72. Thorndike, R. L. (1953), “Who belongs in the family?” Psychometrika, Vol, 18, Issue, 4, pp. 267-276.
73. Treat, J. R., Tumbas, N.S., McDonald, S. T., Shinar, D., Hume, R. D., Mayer, R. E., Stansifer, R. L. and Castellan, N. J. (1979), “Tri-level study of the causes of traffic accidents: Final report. executive summary,” Technical Report, Blommington, Indiana: Institute for Research in Public Safety, DOT/HS 805 09.
74. Tsao, W. J. (2011), “The Relationship between Job Characteristics and Aggressive Driving Behavior,” Master thesis, Department of Transportation and Logistics Management, National Chiao Tung University (NCTU), Taiwan.
75. Tseng, C. M. (2012), “Social-demographics, driving experience and yearly driving distance in relation to a tour bus driver’s at-fault accident risk,” Tourism management, Vol. 33, Issue 4, pp. 910-915.
76. Tukey, J. W. (1977), “Box-and-whisker plots,” Exploratory data analysis, pp. 39-43.
77. Uang, S. T., & Hwang, S. L. (2003), “Effects on driving behavior of congestion information and of scale of in-vehicle navigation systems,” Transportation Research Part C: Emerging Technologies, Vol. 11, Issue 6, pp. 423-438.
78. Vahedi, J., Shariat Mohaymany, A., Tabibi, Z., & Mehdizadeh, M. (2018), “Aberrant driving behaviour, risk involvement, and their related factors among taxi drivers,” International journal of environmental research and public health, Vol. 15, Issue 8, pp.1626.
79. Vetter, M., Schünemann, A. L., Brieber, D., Debelak, R., Gatscha, M., Grünsteidel, F., Herle, M., Mandler, G., & Ortner, T. M. (2018), “Cognitive and personality determinants of safe driving performance in professional drivers,” Transportation research part F: traffic psychology and behaviour, Vol. 52, pp. 191-201.
80. Wang, S. H. (2006), “A Study on Driving Behaviors, Driving Performance and Personality Traits for Inter-City Bus Drivers,” Master thesis, Department of Transportation and Communication Management Science, National Cheng Kung University (NCKU), Taiwan.
81. Wang, J., Zheng, Y., Li, X., Yu, C., Kodaka, K., & Li, K. (2015), “Driving risk assessment using near-crash database through data mining of tree-based model,” Accident Analysis & Prevention, Vol. 84, pp. 54-64.
82. Wei, C. H., Chen, Y. C., Li, P. Y. & Lai, J. W. (2018), “Constructing the Risk Level Models Based on Inter-City Bus Drivers’ Personalities,” Journal of the Chinese Institute of Transportation, Vol 30, No.3, pp. 219-246.
83. Werbos, P. (1974), “Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences,” Ph. D. dissertation, Harvard University.
84. Westerman, S. J. & Haigney, D. (2000), “Individual difference in driver stress, error and violation,” Personality and Individual Differences, Vol. 29, pp. 981-998.
85. Yagil, D. (2001), “Interpersonal antecedents of drivers' aggression,” Transportation research part F: traffic psychology and behaviour, Vol. 4, Issue 2, pp. 119-131.
86. Yearly Statistics of Police Administration Republic of China, Ministry of the Interior, 2018.
87. Yin, W. L. (2004), Application of Driving Behavior Questionnaire to Investigate the Relationship of Aberrant Driving Behaviors and Accidents. Master thesis, Department of Transportation and Logistics Management, National Chiao Tung University (NCTU), Taiwan.
88. 職業駕駛健康危害預防手冊 (民97年01月),行政院勞委會勞工安全衛生研究所。
89. 尹相志 (民97年),SQL Server 2008 Data Mining 資料採礦,悅知文化。
90. 葉怡成 (民98年),類神經網路模式應用與實作,儒林圖書有限公司。
91. 道路交通事故處理規範 (民104年03月05日),第二條,內政部警政署。
92. 道路交通安全規則(民107年06月29日),第五十三條,交通部。
93. 道路交通安全規則(民108年03月29日),第三條,交通部。
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2019-07-23起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2019-07-23起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw