ارزیابی رضایتمندی شهروندان از سیستم حمل و نقل درون‌شهری همدان
محورهای موضوعی : مطالعات برنامه ریزی شهری و منطقه ای
حامد عباسی
1
(استادیار گروه جغرافیا، دانشگاه لرستان، خرم‌آباد، ایران)
کلید واژه: شبکه عصبی مصنوعی, حمل و نقل, همدان, رضایتمندی, رگرسیون غیرخطی,
چکیده مقاله :
حمل و نقل یکی از مهم‌ترین زیربناهای تشکیل‌دهنده زندگی شهری است که شکل و چگونگی توسعه اجتماعی و اقتصادی شهر را تعیین می‌کند. در این راستا، بررسی کیفیت حمل و نقل شهر همدان که می‌تواند در توسعه آن بسیار مفید باشد؛ مورد توجه قرار گرفت. جهت ارزیابی میزان رضایتمندی شهروندان از سیستم حمل و نقل از مدل رگرسیون غیرخطی و شبکه عصبی مصنوعی استفاده شد. بدین منظور ابتدا با تدوین پرسش‌نامه‌ای که بر اساس سه شاخص اصلی (وضعیت تجهیزات و تأسیسات، وضعیت ساختار کالبدی و وضعیت مدیریت و شیوه خدمات رسانی) پایه‌ریزی گردید؛ دیدگاه شهروندان جمع‌آوری شد. سپس با اتحاذ این شاخص‌ها به‌عنوان متغیر مستقل و میزان رضایتمندی به‌عنوان متغیر وابسته، یک مدل رگرسیون غیرخطی اجرا شد. میزان همبستگی و جذر میانگین مربعات خطای خروجی از این مدل به ترتیب به مقدار 914/0 و 334/0 مقدار بدست آمد. در رویکردی دیگر با استفاده از شبکه عصبی مصنوعی، یک مدل با ساختار سه نرون ورودی، یک لایه پنهان و یک نرون خروجی پایه‌ریزی شد. همبستگی خروجی این مدل به مقدار 998/0 مقدار و جذر میانگین مربعات خطای آن در حدود 6 برابر کمتر از مدل رگرسیونی محاسبه شد. نتایج نشان دادند که مدل شبکه عصبی با تخمین توأمان روابط خطی و غیرخطی، از انعطفاف‌پذیری و قابلیت مناسب‌تری نسبت به رگرسیون غیرخطی برخوردار است. از طرفی شاخص‌های قیمت‌گذاری با ضریب (853/0)، برابری و رفاه با (795/0) و کاهش تقاضای سفر با (790/0) مقدار، اثرگذارترین شاخص‌ها در رضایتمندی شهروندان از شبکه حمل و نقل شهری هستند.
Transportation is one of the most important constituent parts of urban life that determines the shape and way of social and economic development of the city. In this regard, the quality of transportation in Hamadan city, which can be very useful in its development, was considered. The nonparametric regression model and artificial neural network were used to assess the citizens' satisfaction from the transportation system. For this purpose, first, a questionnaire was developed based on three main indicators (equipment and facilities status, physical status, management status, and service delivery). Citizens' perspective was gathered. Then, using a nonparametric regression model, independent indicators and satisfaction as an independent variable were used. The correlation and root mean square of the output errors of this model were 0.914 and 0.334 respectively. In another approach, using an artificial neural network, a model with three intrinsic neuron structures, one hidden layer and one output neuron was constructed. The output correlation of this model was 0.998 and the mean square error of the error was about 6 times lower than regression model. The results showed that the neural network model with both linear and nonlinear correlation estimates is more versatile and more suitable than nonparametric regression. On the other hand, price indices with coefficient (0.885), equality and welfare with (0.795) and decrease in demand for travel (0.790) are the most effective indicators for citizens' satisfaction with urban transport network.
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Hataminejhad, H., Pour Ahmad, A., Sangbar Faraji, H. & Azimi, A. (1392). Measuring the Satisfaction of Users of the Public Transport System in the South Alborz Region. Economy and Urban Management, 9, 105-123. (In Persian).
Hesam, M. (2017).Tourists satisfaction of rural tourism destinations (Case study: villages of Foman County). Journal of planning of setelmaent of humanties, 12, 803-8. (In Persian).
Huang, M. C. (1998). Ab empircal study on the model of relationship value-loyalty for thebanking industry, M.A Thesis, National YunLin University of Science & Technology, Yunlin Taiwan.
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Kayri, M. (2016).Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks:A Comparative Empirical Study on Social Data, Journal of Math. Comput. Appl. 21:1-11.
Lee S., Ryu, J. H., Lee ,M. J., & Won J, S. (2006).The Application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea, Mathematical Geology, 38 (2), 199-220.
MacDonald, E., Backsell, M., Gonzalez, Ri., & Papalambros, P. (2006). The KanoMethod’s Imperfections, and Implications in Product Decision Theory. International Design Research Symposium.
Menhaj, M. (2002).Neural Networks and Artificial Intelligent Basic. First edition AmirKabir University. Press, 350. (In Persian).
Moghaddamnia, A., Ghafari Gousheh, M., Piri, J., Amin S., & Han D. (2009). Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources. 32: 88–97. (In Persian).
Moghimi, A. & Amini KhezrAbadi, M. (2014). Study of Satisfaction of Travelers with the Quality of Travel Services of Brunshahri. Journal of Traffic Management Studies, 33, 109-132. (In Persian).
Omarzadeh, B. Gharlakh, M. & Pour Ahmad, A. (2010). Evaluation and analysis of the efficiency of the BRT transportation system and its general satisfaction in the metropolis of Tehran. Human Geographic Research, 38, 19-38. (In Persian).
Piraali, A. & Sayyadat, S. (1393). Strategic Planning of Traffic Control Management in Shiraz; Journal of Traffic Management Studies, 32, 66-42. (In Persian).
Kuo, Y. C. (1999). The study of job stress, job satisfaction, burnout, and nsure satisfaction among flight attendant, Unpublished MBA Thesis, National Sun Yat-Sen University, Taiwan, ROC.
Rukno-Din Eftekhari, A-R., Ramezannezhad, Y., & Pour Taheri, M. (2017). Satisfaction of tourists from the tourist destinations of rural areas of Guilan province. Human Geography Research, 3, 585-571. (In Persian).
Safi, M.H., Fallahi Khoshkenab, M., Rasel, M. & Rahgozar, M. (2011).Assessing and prioritizing needs of faculty members on the basis of Kano’s Model. Payesh, 10 (4), 459-468. (In Persian).
Sharma, B., & Venugopalan, K. (2014). Comparison of Neural Network Training Functions for Hematoma Classification in Brain CT Images. Journal of Computer Engineering (IOSR-JCE), 16, 31-35.
Soltani, A. & Fallah Manshadi, A. (2012). Integrating the transport system to sustainable transport, case study; Shiraz metropolis, Urban Management Studies, 5, 47-60. (In Persian).
Sprint, P., & Smithon, N. (2007). Non Applicable Statistical Methods, Teranclete by Hossein Ali Nirvmand, Ferdowsi University Press, Mashhad.
Vazifehdust, H., & Farokhian, S. (2009). Survey of Customer Satisfaction in San Suan Product Planning by Kano Model, Jounal of Marketing Management, 4 (7), 137-157. (In Persian).
_||_Amanpour, S., Maleki, S. & Hosseini, N. (2017). Survey of the Satisfaction of Urban Travelers with the Quality of Urban Public Transport Services in Ahwaz Metropolis. Environmental Studies of Haft Hesar, 15, 37-46. (In Persian).
Ahadi, M.R., & Ghanizadeh Hesar, E. (2017). Neutralization of neighborhood traffic with a revitalization approach using the SWOT model (Mooreddah: Uradmieh Yard Shahi neighborhood). Human Geography Research, 49 (4), 755-767. (In Persian).
Bates, D. & Watts, D. (2002). Nonlinear Regression and Its Applications; Translation of Rezaeipedeh, Hojjat and Baznia, Abolghasem, Ferdowsi University Press.
Bitner, J.M. (1990) Evaluating service encounters: The effects of Physical surroundings and employee responses. Journal of Marketing, 54, 82-99.
Department of Planning and Development of Hamedan Municipality. (2016).The statistics of the city of Hamedan. 14-16. (In Persian).
Fallston, M. & Margareta, F. (2008). Perceived Satisfaction with Public Transport Service in Nine European Cities. Journal of the Transportation Research Forum, 47 (3), 93-103.
Hataminejhad, H., Pour Ahmad, A., Sangbar Faraji, H. & Azimi, A. (1392). Measuring the Satisfaction of Users of the Public Transport System in the South Alborz Region. Economy and Urban Management, 9, 105-123. (In Persian).
Hesam, M. (2017).Tourists satisfaction of rural tourism destinations (Case study: villages of Foman County). Journal of planning of setelmaent of humanties, 12, 803-8. (In Persian).
Huang, M. C. (1998). Ab empircal study on the model of relationship value-loyalty for thebanking industry, M.A Thesis, National YunLin University of Science & Technology, Yunlin Taiwan.
Iran Statistics Center. (2016). Population and Housing Census. (In Persian).
Kayri, M. (2016).Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks:A Comparative Empirical Study on Social Data, Journal of Math. Comput. Appl. 21:1-11.
Lee S., Ryu, J. H., Lee ,M. J., & Won J, S. (2006).The Application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea, Mathematical Geology, 38 (2), 199-220.
MacDonald, E., Backsell, M., Gonzalez, Ri., & Papalambros, P. (2006). The KanoMethod’s Imperfections, and Implications in Product Decision Theory. International Design Research Symposium.
Menhaj, M. (2002).Neural Networks and Artificial Intelligent Basic. First edition AmirKabir University. Press, 350. (In Persian).
Moghaddamnia, A., Ghafari Gousheh, M., Piri, J., Amin S., & Han D. (2009). Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources. 32: 88–97. (In Persian).
Moghimi, A. & Amini KhezrAbadi, M. (2014). Study of Satisfaction of Travelers with the Quality of Travel Services of Brunshahri. Journal of Traffic Management Studies, 33, 109-132. (In Persian).
Omarzadeh, B. Gharlakh, M. & Pour Ahmad, A. (2010). Evaluation and analysis of the efficiency of the BRT transportation system and its general satisfaction in the metropolis of Tehran. Human Geographic Research, 38, 19-38. (In Persian).
Piraali, A. & Sayyadat, S. (1393). Strategic Planning of Traffic Control Management in Shiraz; Journal of Traffic Management Studies, 32, 66-42. (In Persian).
Kuo, Y. C. (1999). The study of job stress, job satisfaction, burnout, and nsure satisfaction among flight attendant, Unpublished MBA Thesis, National Sun Yat-Sen University, Taiwan, ROC.
Rukno-Din Eftekhari, A-R., Ramezannezhad, Y., & Pour Taheri, M. (2017). Satisfaction of tourists from the tourist destinations of rural areas of Guilan province. Human Geography Research, 3, 585-571. (In Persian).
Safi, M.H., Fallahi Khoshkenab, M., Rasel, M. & Rahgozar, M. (2011).Assessing and prioritizing needs of faculty members on the basis of Kano’s Model. Payesh, 10 (4), 459-468. (In Persian).
Sharma, B., & Venugopalan, K. (2014). Comparison of Neural Network Training Functions for Hematoma Classification in Brain CT Images. Journal of Computer Engineering (IOSR-JCE), 16, 31-35.
Soltani, A. & Fallah Manshadi, A. (2012). Integrating the transport system to sustainable transport, case study; Shiraz metropolis, Urban Management Studies, 5, 47-60. (In Persian).
Sprint, P., & Smithon, N. (2007). Non Applicable Statistical Methods, Teranclete by Hossein Ali Nirvmand, Ferdowsi University Press, Mashhad.
Vazifehdust, H., & Farokhian, S. (2009). Survey of Customer Satisfaction in San Suan Product Planning by Kano Model, Jounal of Marketing Management, 4 (7), 137-157. (In Persian).