ارزیابی اثر فضایی سهم انواع کاربری های زمین در وقوع تصادفات (مطالعه موردی: شهر شیراز)

نوع مقاله : مقاله پژوهشی

نویسندگان

محمد صدیق باور ¹

علی نادران ²

محمود صفارزاده ³

¹ باشگاه پژوهشگران جوان و نخبگان، واحد شیراز، دانشگاه آزاد اسلامی، شیراز.

² برنامه ریزی و مهندسی حمل و نقل، دانشکده مهندسی عمران، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران.

³ برنامه ریزی و مهندسی حمل و نقل، دانشکده مهندسی عمران و محیط زیست، دانشگاه تربیت مدرس، تهران.

10.22067/jfcei.2023.80945.1214

چکیده

نوع کاربری زمین در هر ناحیه نرافیکی مهمترین عامل تعیین کننده تعداد وسایل نقلیه، ویژگی‌های هندسی و ترافیکی در آن ناحیه می‌باشد. کاهش تراکم و اختلاط انواع وسایل نقلیه و در محیط‌های شهری به منظور کاهش تصادفات یکی از مهمترین دغدغه‌های مهندسین حمل و نقل می‌باشد. در این مطالعه به منظور بررسی تاثیر سهم انواع کاربری ها در هریک از نواحی ترافیکی شهر شیراز بر احتمال وقوع تصادف، از روش دو مرحله‌ای شامل شناسایی انواع کاربری‌های تاثیرگذار در بروز تصادف و همچنین اثرات فضایی بین متغیرهای مستقل و داده‌های تصادف در فضا استفاده گردیده است همچنین از روش‌های هسته چگالی کرنل به منظور یافتن پهنای باند مناسب جستجوی مشاهدات، رگرسیون وزن دار جغرافیایی و رگرسیون پواسن وزن دار جغرافیایی به منظور بررسی اثرات فضایی تاثیر سهم انواع کاربری‌ها بر وقوع تصادف استفاده شده است. ننایج مطالعه نشان داده‌ است که از بین مدل‌های مذکور مدل محلی GWPR براساس معیارهای صحت سنجی عملکرد بهتری نسبت به مدل جهانی پواسن و مدل محلی GWR دارد همچنین وجود کاربری مسکونی، تجاری، بایر و متروکه و اختلاط مسکونی و غیر مسکونی بیشترین تاثیر را در بروز تصادف دارند. بررسی اثرات فضایی انواع کاربری اراضی در هریک از نواحی ترافیکی در این مطالعه می‌تواند به منظور انجام اقدامات ایمنی بسیار حائز اهمیت باشد.

کلیدواژه‌ها

کاربری اراضی

رگرسیون پواسن وزن دار جغرافیایی

رگرسیون وزن دار جغرافیایی

تصادف

هسته چگالی کرنل

---

عنوان مقاله English

The spatial effect of the contribution of land use types in the occurrence of accidents based on traffic area zones (Case study: Shiraz metropolis)

نویسندگان English

mohammad sedigh bavar ¹

Ali Naderan ²

Mahmoud Saffarzadeh ³

¹ Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

² Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

³ Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran

چکیده English

The type of land use in each traffic area zone (TAZ) is the most important factor determining the number of vehicles, geometric and traffic characteristics in that zone. Any factor in the urban environment that causes congestion and attraction of vehicles at certain times increases the probability of an accident in that area.In this study, in order to investigate the effect of the share of the types of uses in each of the traffic areas of Shiraz city on the probability of an accident, a two-step method including the identification of the types of uses influencing the occurrence of accidents as well as spatial effects between independent variables and accident data in space Kernel density estimate (KDE) methods have also been used in order to find the suitable bandwidth for searching observations, geographically weighted regression (GWR) and geographically weighted Poisson regression (GWPR) in order to investigate the spatial effects of the contribution of land use types on the occurrence Accident is used .The results of the study have shown that among the mentioned models, the local GWPR model has a better performance than the global Poisson model and the local GWR model based on validation criteria. Also, the presence of residential, commercial, barren and abandoned uses and the mixing of residential and non-residential use have the greatest impact on They have accidents. Examining the spatial effects of land use types in each of the traffic areas in this study can be very important in order to carry out safety measures.

کلیدواژه‌ها English

Land use

Accident

shiraz metropolis

Kernel Density Estimate

Geographical Weighing Poisson Regression

مراجع

[1] G. Al-Hasani, M. Asaduzzaman, and A.H. Soliman, “Geographically weighted Poisson regression models with different kernels: Application to road traffic accident data Communications in Statistics: Case Studies,” Data Analysis and Applications, vol. 7, no. 2, pp. 166-181, 2021. https://doi.org/10.1080/23737484.2020.1869628

 [2] S. A. Almasi, and H. R. Behnood, “Exposure based geographic analysis mode for estimating the expected pedestrian crash frequency in urban traffic zones; case study of Tehran,” Accident Analysis and Prevention, vol. 168, pp. 106576, 2022. https://doi.org/10.1016/j.aap.2022.106576

[3] S. A. Almasi, H. R. Behnood, and R. Arvin, “Pedestrian crash exposure analysis using alternative geographically weighted regression models,” Journal of advanced transportation, 2021. https://doi.org/10.1155/2021/6667688

 [4] E. A. Atumo, H. Li, and X. Jiang, “Segment-Level Spatial Heterogeneity of Arterial Crash Frequency Using Locally Weighted Generalized Linear Models,” Transportation research record: Journal of the Transportation Research Board, vol. 2677. no. 3, 2022. https://doi.org/10.1177/03611981221126510

[5] H. Bozdogan, “Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions,” Psychometrika, vol. 52, no. 3, pp. 345-37, 1987. https://doi.org/10.1007/BF02294361

[6] M. Duan, H. Ya, L. Zhang, and H. Jia, “Traffic safety analysis of intersections between the residential entrance and urban road,” Procedia-Social and Behavioral Sciences, vol. 96, pp. 1001-1007, 2013. https://doi.org/10.1016/j.sbspro.2013.08.114  

[7] M. Effati, and M. V. Saheli, “Examining the influence of rural land uses and accessibility-related factors to estimate pedestrian safety: The use of GIS and machine learning techniques,” International journal of transportation science and technology, vol. 11, no. 1, pp. 144-157, 2022. https://doi.org/10.1016/j.ijtst.2021.03.005

[8] N. Fiorentini, D. Pellegrini, and M. Losa, “Overfitting Prevention in Accident Prediction Models: Bayesian Regularization of Artificial Neural Networks,” Transportation Research Record, vol. 2677, no. 2, 2022. https://doi.org/10.1177/03611981221111367  

[9] L. Fuentes, R. Truffello, and M. Flores, “Impact of Land Use Diversity on Daytime Social Segregation Patterns in Santiago de Chile. Buildings,” buildings, vol. 12, no. 2, pp. 149, 2022. https://doi.org/10.3390/buildings12020149

[10] M. J. T. L. Gomes, F. Cunto, and A. R da Silva, “Geographically weighted negative binomial regression applied to zonal level safety performance models,” Accident Analysis & Prevention, vol. 106, pp. 254-261, 2017. https://doi.org/10.1016/j.aap.2017.06.011  

[11] H. Harirforoush, and L. Bellalite,  “A new integrated GIS-based analysis to detect hotspots: a case study of the city of Sherbrooke,” Accident Analysis & Prevention, vol. 130, pp. 62-74, 2019. https://doi.org/10.1016/j.aap.2016.08.015

[12] C.D. Kang, “The S+ 5Ds: Spatial access to pedestrian environments and walking in Seoul, Korea,” Cities, vol. 77, pp. 130-141, 2018. https://doi.org/10.1016/j.cities.2018.01.019

[13] S. S. A. Kazmi, M. Ahmed, R. Mumtaz, and Z. Anwar, “Spatiotemporal clustering and analysis of road accident hotspots by exploiting GIS technology and Kernel density estimation,” The Computer Journal, vol. 65, no. 2, pp. 155-176, 2022. https://doi.org/10.1093/comjnl/bxz158

[14] H. Khaksar, S. A. Almasi, and A. A. Goharpoor, “Application of Geographical-Spatial Models in Predicting the Frequency of Road Crash (Case Study: Main Road Network of Hamadan Province),” Journal of Transportation Research, vol. 19, no. 1, pp. 45-58, 2022.https://doi.org/10.22034/tri.2021.250276.2815

[15] K. Kim, P. Pant, and E. Yamashita, “Accidents and accessibility: Measuring influences of demographic and land use variables in Honolulu, Hawaii,” Transportation research record:Journal of the Transportation Research Board, vol. 2147, no. 1, pp. 9-17, 2010. https://doi.org/10.3141/2147-02

[16] W. Larson, F. Liu, and A. Yezer, “Energy footprint of the city: Effects of urban land use and transportation policies,” Journal of Urban Economics, vol. 72, no. 2-3, pp. 147-159, 2012. https://doi.org/10.1016/j.jue.2012.05.001  

[17] K. G. Le, P. Liu, and L.T. Lin, “Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam,” Geo-spatial Information Science, vol. 23, no. 2, pp. 153-164, 2020. https://doi.org/10.1080/10095020.2019.1683437

[18] K. G. Le, P. Liu and L.T. Lin, “Traffic accident hotspot identification by integrating kernel density estimation and spatial autocorrelation analysis: a case study,” International journal of crashworthiness, vol. 27, no. 2, pp. 543-553, 2022. https://doi.org/10.1080/13588265.2020.1826800

[19] B. D. Leibowicz, “Urban land use and transportation planning for climate change mitigation: A theoretical framework,” European Journal of Operational Research, vol. 284, no. 2, pp. 604-616, 2020. https://doi.org/10.1016/j.ejor.2019.12.034

[20] J. Liu, A. J. Khattak, and B. Wali, “Do safety performance functions used for predicting crash frequency vary across space? Applying geographically weighted regressions to account for spatial heterogeneity,” Accident Analysis & Prevention, vol. 109, pp. 132-142, 2017. https://doi.org/10.1016/j.aap.2017.10.012

[21] S. Mathew, S. S. Pulugurtha, and S. Duvvuri, “Exploring the effect of road network, demographic, and land use characteristics on teen crash frequency using geographically weighted negative binomial regression,” Accident Analysis & Prevention, vol. 168, pp. 106615, 2022. https://doi.org/10.1016/j.aap.2022.106615

[22] L. A. Merlin, C. R. Cherry, A. Mohamadi Hezaveh, and E. Dumbaugh, “Residential accessibility's relationships with crash rates per capita,” Journal of Transport and Land Use, vol. 13,no. 1, pp. 113-128, 2020. https://www.jstor.org/stable/26967238

[23] L. A. Merlin, E. Guerra, and E. Dumbaugh, “Crash risk, crash exposure, and the built environment: A conceptual review,” Accident Analysis & Prevention, vol. 134, pp. 105244, 2020. https://doi.org/10.1016/j.aap.2019.07.020

[24] K. M. Peera, R. S. Shekhawat, and C. Prasad, “Traffic analysis zone level road traffic accident prediction models based on land use characteristics,” International journal for traffic and transport engineering (Belgrade), vol. 9, no. 4, pp. 376-386, 2019. https://doi.org/10.7708/ijtte.2019.9(4).03

[25] L. Srikanth, and I. Srikanth, “A case study on kernel density estimation and hotspot analysis methods in traffic safety management,” Paper presented at the 2020 International Conference on COMmunication Systems and Networks (COMSNETS), 2020. https://ieeexplore.ieee.org/document/9027448

[26] M. R. Stevens, “Does compact development make people drive less? ,”Journal of the American Planning Association, vol. 83, no. 1, pp. 7-18, 2017. https://doi.org/10.1080/01944363.2016.1240044

[27] P. Stoker, A. Garfinkel-Castro, M. Khayesi, W. Odero, M. N. Mwangi, M. Peden, and R. Ewing, “Pedestrian safety and the built environment: a review of the risk factors ,” Journal of Planning Literature, vol. 30, no. 4, pp. 377-392, 2015. https://doi.org/10.1177/0885412215595438

[28] H. Sung, S. Lee, S. Cheon, and J. Yoon, “Pedestrian Safety in Compact and Mixed-Use Urban Environments: Evaluation of 5D Measures on Pedestrian Crashes,” Sustainability, vol. 14, no. 2, pp. 646, 2022. https://doi.org/10.3390/su14020646

[29] M. Umair, I. A. Rana, and R. H. Lodhi, “The impact of urban design and the built environment on road traffic crashes: a case study of Rawalpindi, Pakistan,” Case studies on transport policy, vol. 10, no. 1, pp. 417-426, 2022. https://doi.org/10.1016/j.cstp.2022.01.002

[30] Y. Zhang, H. Lu, and W. Qu, “Geographical detection of traffic accidents spatial stratified heterogeneity and influence factors,” International journal of environmental research and public health, vol. 17, no. 2, pp. 572, 2020. https://doi.org/10.3390/ijerph17020572

[31] S. Zhong, Y. Jiang, and O. A. Nielsen, “Lexicographic multi-objective road pricing optimization considering land use and transportation effects,” European Journal of Operational Research,vol. 298, no. 2, pp. 496-509, 2022. https://doi.org/10.1016/j.ejor.2021.05.048