پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 426 |
| تعداد مقالات | 5,554 |
| تعداد مشاهده مقاله | 6,686,489 |
| تعداد دریافت فایل اصل مقاله | 5,654,388 |
اولویت‌بندی عوامل زیرساختی مؤثر بر ایمنی راه‌های دوخطه با رویکردهای پیشگیرانه و واکنشگرا (مطالعه موردی: محور اهر-تبریز) | ||
| نشریه مهندسی عمران امیرکبیر | ||
| دوره 54، شماره 4، تیر 1401، صفحه 1419-1440 اصل مقاله (1.72 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2021.19521.7193 | ||
| نویسندگان | ||
| شهاب حسن پور* 1؛ فرهاد حدادی2 | ||
| 1استادیار،گروه مهندسی عمران، دانشگاه آیت الله العظمی بروجردی، بروجرد، ایران | ||
| 2گروه مهندسی عمران، دانشگاه صنعتی شاهرود، شاهرود، ایران | ||
| چکیده | ||
| شناسایی عوامل زیرساختی مؤثر در تصادفات راه‌های دوخطه برون شهری مبتنی بر دو رویکرد پیشگیرانه و واکنش گرا از روش های ارتقاء ایمنی جادهای است. هدف پژوهش حاضر، شناسایی و اولویت بندی عوامل مؤثر بر ایمنی راه برونشهری دوخطه محور اهر- تبریز توسط سه مدل شبکه عصبی، تاپسیس و رگرسیون لجستیک است. از روش تحلیل سلسله مراتبی نیز برای تعیین اوزان معیارها و زیرمعیارهای مرتبط در روش تاپسیس استفاده می شود. سپس، این مطالعه به مقایسه نتایج اولویت بندی سه روش پیشنهادی بر اساس دو رویکرد مورد نظر می پردازد. نتایج حاصل از مدل شبکه عصبی نشان داد که این مدل با درستی پیش بینی 86 درصد متغیرهای زیرساختی به ترتیب وجود قوس های افقی و قائم، درصد خودروهای سنگین، وضعیت روسازی، وضعیت زهکشی راه، حجم ترافیک عبوری و وضعیت دوربین کنترل سرعت را اولویت بندی می کند. در حالی که بر اساس روش تاپسیس، اولویت متغیرهای زیرساختی مؤثر بر ایمنی مسیر به ترتیب شامل وضعیت زهکشی، وضعیت روسازی، وضعیت دوربین کنترل سرعت، وجود قوس های افقی و قائم، وضعیت علائم مورد استفاده در راه، درصد خودروهای سنگین، وضعیت روشنایی راه، حجم ترافیک عبوری و وضعیت آرام‌سازی ترافیک می باشد. همچنین، نتایج تحلیل رگرسیون لجستیک نشان داد که این مدل با درستی 74/82 درصد توانایی اولویت بندی عوامل زیرساختی شامل متغیرهای وضعیت روسازی، وضعیت دوربین کنترل سرعت، وضعیت روشنایی راه، حجم ترافیک عبوری، و وضعیت علائم مورد استفاده در راه دارد که این متغیرها به ترتیب اثرگذاری بیشتری بر اساس شانس بیشتر در مدل نسبت به دیگر متغیرهای زیرساختی در احتمال وقوع شدت تصادفات دارند. مقایسه عملکردی رویکردهای واکنشگرا و پیش گیرانه با استفاده از آزمون های اسپیرمن و تی-تست نیز نشان داد که بین دو مدل شبکه عصبی و رگرسیون لجستیک همبستگی در عملکرد وجود دارد و دو مدل در رویکرد واگنش گرا تفاوت معناداری وجود ندارد. در حالی که بین این مدل ها و مدل تاپسیس تفاوت معناداری در عملکرد رویکرد وجود دارد. در نتیجه دو مدل عصبی و رگرسیون لجستیک در رویکرد واکنش گرا و مدل تاپسیس در رویکرد پیش گیرانه طبقه بندی می شوند و مدل شبکه عصبی به دلیل مقدار درستی پیش بینی قابل توجه در پیش بینی شدت تصادفات از قابلیت بهتری نسبت به سایر مدل ها در شناسایی و اولویت بندی عوامل زیرساختی کاربرد دارد. | ||
| کلیدواژه‌ها | ||
| راه‌های دوخطه؛ ایمنی جادهای؛ رویکرد پیشگیرانه؛ رویکرد واکنشگرا؛ روش‌های اولویت‌بندی | ||
| موضوعات | ||
| ایمنی؛ حمل و نقل شهری؛ حمل و نقل عمومی | ||
| عنوان مقاله [English] | ||
| Prioritization of infrastructure factors affecting on the safety of two-lane roads using proactive and reactive methods (Case study: Ahar-Tabriz road) | ||
| نویسندگان [English] | ||
| Shahab Hassanpour1؛ Farhad hadadi2 | ||
| 1Assistant Professor, Faculty of Civil Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran | ||
| 2Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran | ||
| چکیده [English] | ||
| Identifying effective infrastructure factors that contributed to accidents of two-lane roads based on proactive and reactive methods is one of the ways to increase road safety. Therefore, the present study is firstly aimed to identify, and prioritize factors affecting accidents on rural road of Ahar-Tabriz using artificial neural network (ANN), TOPSIS and multinomial logistic regression (MNL) models. AHP was used for the weighting of criteria and subcriteria in the TOPSIS model. Secondly, this study compares the results of priorities in three models based on proactive and reactive methods. The results of the ANN model indicated that this model predicts accident severity via 86%, in which prioritized factors such as horizontal and vertical curves, percentage of heavy vehicles, pavement condition, road drainage condition, the volume of passing cars and use of speed control cameras, respectively. However, the TOPSIS model has shown that the priority of the infrastructure factors affecting road safety includes drainage condition, pavement condition, use of speed control cameras, horizontal and vertical curves, road signs, percent of heavy cars, road lighting condition, the volume of passing cars, and traffic calming, respectively. In addition, the MNL model indicated that this model is capable of prediction in accident severity via 74.82% in which pavement condition, use of speed control cameras, road lighting condition, the vehicle of passing cars, and road signs are ranked as the most effective factors involved with accidents on rural roads, respectively. Therefore, by making a performance comparison based on Spearman's rank correlation coefficient, T-test analysis, it is found that there is no difference between ANN and MNL models; however, there is a significant difference between TOPSIS and other models. Thus, ANN and MNL models are reactive methods, and the TOPSIS model is a proactive method in which the ANN model, due to higher accident severity prediction, is selected as the best predictive model. | ||
| کلیدواژه‌ها [English] | ||
| Two-lane roads, Road safety, Proactive method, Reactive method, Prioritization models | ||
| مراجع | ||
|
[1] M. Ahadi, M. Hassanpour, P. Bashiri, P. Bashiri, Strategies to promote safety to prevent pedestrian accidents in the city of Qazvin, Safety Promotion and Injury Prevention, 4(3) (2017) 143-150. [2] G.A. Shafabakhsh, F. Fathi, A. Zayerzadeh, Prioritization of eventful roads correction using artificial nrural networks, Journal of Modeling in Engineering, 8(20) (2010), 71-81. [3] A. Tavakolli Kashani, R. Rabieian (2014). Investigation Effect of Driver's Age and Environmental Factors on Risk of Motorcycle Accident Based On Concept of Quasi-Induced Exposure. Quarterly Journal of Transportation Engineering, 6(2) (2014) 213-224. [4] J.R. Jones, A method to quantify road safety audit data and results, Utah State University, (2013). [5] S. Afandizadeh, M. Hossen Hamedi, J. Sadeghi, Recognizing the Effective Factors of Suburb Roads Accidents of Chaharmahal & Bakhtiari Province and the Study of it's Reasons (Case Study: Khuzestan Road), Chaharmahal &Bakhtiaris of police science, 1396(17) (2017) 15-34. [6] R. Ghadimi, Statistical analysis of road accidents on the Ahar-Tabriz road in the period 2012 to 2014, Conference on Civil Engineering, Architecture and Urban Planning of the Islamic world, Tabriz, (2018). [7] M. Ameri, S. Effendizadeh, R. Jalal Kamali, M.H. Rezaeifar, M. Wamegh, Prioritization of safety measures in suburban roads using RSAR method, 15th International Conference on Transportation and Traffic Engineering, (2015). [8] S. Afandizadeh, S. Hassanpour., Evaluating the Effect of Roadway and Development Factors on the Rural Road Safety Risk Index. Advances in Civil Engineering, 2020. [9] A. Abdi, Z. Mosadeq, H. Bigdeli Rad, Prioritizing Factors Affecting Road Safety Using Fuzzy Hierarchical Analysis. Journal of Transportation Research, 17(3) (2020) 33-44. [10] A.A.E. Doğan, A. Payidar ANgüngör, Estimating road accidents of Turkey based on regression analysis and artificial neural network approach, Advances in transportation studies, 16 (2008) 11-22. [11] Y. Xie, D. Lord, Y. Zhang, Predicting motor vehicle collisions using Bayesian neural network models: An empirical analysis, Accident Analysis & Prevention, 39(5) (2007) 922-933. [12] M. Yasin Çodur, A. Tortum, An artificial neural network model for highway accident prediction: A case study of Erzurum, Turkey, PROMET-Traffic&Transportation, 27(3) (2015) 217-225. [13] Y.C. Chang, S.Y. Lee, P.L. Liu, C.C. Chang, Injury prediction based on safety climate questionnaire score using artificial neural networks”, In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 1241-1244). IEEE. [14] A. Sarkar, A. Emami, Prediction of road accidents using neural networks and correction of road tolls with the help of genetic algorithm, International Conference on New Research in Engineering Sciences, Tehran, Shabak Knowledge Management Institute, University of Tehran, (2016). [15] S. Mollai Ali Abadi, M. Hosseinpour, S.R. Elyasi, Predicting the severity of road accidents using artificial neural networks, a case study of Ardabil-Sarcham axis, International Conference on Civil Engineering, Architecture and Urban Planning of Contemporary Iran, Tehran, Osweh University - Tehran - Shahid Beheshti University, (2017). [16] M. Beheshti Nia, H. Bargbid, A hybrid model to identify effective factors in road transportation hazards and prioritize black spots (case study: Nezshabour-Sabzevar axis), Quarterly Journal of Transportation Engineering, 10(3) (2019) 445-459. [17] Q. Bao, D. Ruan, Y. Shen, E. Hermans, D. Janssens, Improved hierarchical fuzzy TOPSIS for road safety performance evaluation, Knowledge-based systems, 32 (2012) 84-90. [18] M. Rosić, D. Pešić, D. Kukić, B. Antić, M. Božović, Method for selection of optimal road safety composite index with examples from DEA and TOPSIS method, Accident Analysis & Prevention, 98 (2017) 277-286. [19] G. Fancello, M. Carta, P. Fadda., Road intersections ranking for road safety improvement: comparative analysis of multi-criteria decision making methods, Transport Policy, 80 (2019) 188-196. [20] Z. Liu, J. He, C. Zhang, L. Xing, B. Zhou, The impact of road alignment characteristics on different types of traffic accidents, Journal of Transportation Safety & Security, (2019) 1-24. [21] F. Haghighat, Application of a multi-criteria approach to road safety evaluation in the Bushehr Province, Iran, PROMET-Traffic&Transportation, 23(5) (2011) 341-352. [22] P. Moridi, A. Moradi, F. Taheri, R. Yarahmadi, A. AsgharFarshad, Prioritization of effective factors in traffic accident based on multiple criteria decision making in the traffic police (Tehran city), Iran Occupational Health, 15(5) (2018) 21-29. [23] S. Cafiso, A. Di Graziano, G. Di Silvestro, G. La Cava, B. Persaud, Development of comprehensive accident models for two-lane rural highways using exposure, geometry, consistency and context variables, Journal of Accident Analysis and Prevention 42 (2010) 1072–1079. [24.] Y. Hassan, T. Sayed, V. Tabernero, Establishing a practical approach for design consistency [25] E. Celik, M. Gul, N. Aydin, A.T. Gumus, A.F. Guneri, A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets”, Knowledge-Based Systems, 85(329) (2015) 341. [26] Z. Chen, W.D. Fan, A multinomial logit model of pedestrian-vehicle crash severity in North Carolina. International journal of transportation science and technology, 8(1) (2019) 43-52. [27] L. Wahab, H. Jiang, A multinomial logit analysis of factors associated with severity of motorcycle crashes in Ghana. Traffic injury prevention, 20(5) (2019) 521-527. [28] L.Y. Chang, Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network, Safety science, 43(8) (2005) 541-557. [29] S.M.H. Al-Tmeemy, H. Abdul-Rahman, Z. Harun, Contractors’ perception of the use of costs of quality system in Malaysian building construction projects, International Journal of Project Management, 30(7) (2012) 827–838. [30] D.B. Zumbo, A.M. Gadermann, C. Zeisser, Ordinal versions of coefficient alpha and theta for Likert rating scales, Journal of modern applied statistical methods, 6(1) (2007) 21-29. [31] G. Rebitzer, T. Ekvall, R. Frischknecht, D. Hunkeler, G. Norris, T. Rydberg,..., D.W. Pennington, Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications. Environment international, 30(5) (2004) 701-720. [32] S. UNEP, Guidelines for social life cycle assessment of products. United Nations Environment Programme (UNEP) and Society of Environmental Toxicology and Chemistry (SETAC), (2009), Belgium. [33] A. Hazen, J.E. Ehiri, Road traffic injuries: hidden epidemic in less developed countries, Journal of the National Medical Association, 98(1) (2006) 73. [34] M.A. Belin, P. Tillgren, E. Vedung, Vision Zero–a road safety policy innovation, International journal of injury control and safety promotion, 19(2) (2012) 171-179. [35] F. Wegman, S. Oppe. Benchmarking road safety performances of countries. Safety science, 48(9) (2010) 1203-1211. [36] S. Siegrist, Towards a method to forecast the effectiveness of national road safety programmes. Safety science, 48(9) (2010) 1106-1110. [37] E. Setyaningsih, L.S. Putranto, F.N. Soelami, Analysis of the visual safety perception and the clarity of traffic signs and road markings in the presence of road lighting in straight and curved road, In MATEC Web of Conferences 181 (2018), 04001, EDP Sciences. [38] S. Kaplan, S. Bortei-Doku, C.G. Prato, The relation between the perception of safe traffic and the comprehension of road signs in conditions of ambiguous and redundant information, Transportation research part F: traffic psychology and behaviour, 55 (2018) 415-425. [39] S.J. Ward, M.S. Wogalter, A.W. Mercer, Comprehension and training of international road signs. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (48(17) 2104-2108), Sage CA: Los Angeles, CA: SAGE Publications, (2004). [40] P. Delhomme, W. De Dobbeleer, S. Forward, A. Simões, Manual for designing, implementing, and evaluating road safety communication campaigns: Part I. Brussels: Belgian Road Safety Institute, (2009). [41] N. Haworth, C. Tingvall, N. Kowadlo, Review of best practice road safety initiatives in the corporate and/or business environment. Monash University Accident Research Centre Reports, 166 (2000) 119. [42] G. Al-Haji, Road safety development index: Theory, philosophy and practice (Doctoral dissertation, Linköping University Electronic Press), (2007). [43] T. Nemoto, D. Beglar, Likert-scale questionnaires, In JALT 2013 conference proceedings (pp. 1-8), (2014). [44] T.L. Saaty, Decision making with the analytic hierarchy process, International journal of services sciences, 1(1) (2008) 83-98. [45] S. Mokhtarimousavi, J.C. Anderson, A. Azizinamini, M. Hadi, Factors affecting injury severity in vehicle-pedestrian crashes: a day-of-week analysis using random parameter ordered response models and artificial neural networks, International journal of transportation science and technology, 9(2) (2020) 100-115. | ||
|
آمار تعداد مشاهده مقاله: 793 تعداد دریافت فایل اصل مقاله: 927 |
||