تحلیل زمانی‌ـ مکانی تصادفات عابران پیاده با استفاده از سری زمانی و شاخص Moran’s I تفاضلی (مطالعۀ موردی: شهر مشهد)

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکدۀ مهندسی علوم زمین، دانشگاه صنعتی اراک، اراک، ایران

چکیده

سابقه و هدف: عابران پیاده، به‌دلیل نبودِ تدابیر حفاظتی، آسیب‌پذیرترین کاربران راه شناخته می‌شوند و ایمنی آنها در حوزۀ برنامه‌ریزی حمل‌ونقل بسیار حیاتی است. مطالعات متعددی که تصادفات عابران پیاده را تحلیل کرده‌اند معمولاً بر مدل‌های پیش‌بینی، عوامل خطر و الگوهای مکانی‌ـ زمانی متمرکز بوده‌اند. این تحلیل‌ها بر اهمیت شناسایی مناطق پرخطر و اجرای اقدامات پیشگیرانه تأکید می‌کنند. تأثیرات خودهمبستگی مکانی و زمانی در درک الگوهای تصادفات بسیار مهم است و شاخص‌هایی مانند Moran’s I و تخمین تراکم کرنل، در این حوزه، کاربرد گسترده‌ای دارند. با توجه به موارد یادشده، مطالعۀ پیش رو تأثیر رشد سریع اجتماعی‌ـ اقتصادی در تصادفات ترافیکی و نیاز به مداخلات ایمنی هدفمند برای حفاظت از عابران پیاده را، در شهر مشهد در ایران، به‌صورت برجسته و مؤثر نشان می‌دهد.
مواد و روش‌ها: در این پژوهش، با استفاده از تحلیل اکتشافی سری زمانی، تصادفات عابران پیاده به‌صورت ماهیانه و ساعتی در بازه‌ای پنج‌ساله (1394-1398) بررسی شده است. در گام بعد، وجود خودهمبستگی زمانی و همچنین روند در وقوع تصادفات عابران پیاده مورد بحث قرار گرفته و سپس، با استفاده از تحلیل یکنواختی سری زمانی، زمان تغییر در وقوع تصادفات بررسی شده است. درنَهایت، به‌منظور استخراج الگوهای مکانی تغییرات تصادفات عابران پیاده در بازۀ زمانی مطالعاتی، از شاخص Moran’s I تفاضلی استفاده شده است.
نتایج و بحث: با استفاده از تحلیل‌های سری زمانی، الگوی زمانی و وجود خودهمبستگی زمانی معنادار در مقادیر ماهیانه و ساعتی تصادفات عابران پیاده تأیید شد. نتایج آزمون من‌ـ کندال با لحاظ‌کردن تأثیرات خودهمبستگی نیز وجود روند معنادار در تصادفات عابران پیاده را به‌ازای ماه‌های گوناگون سال و ساعات متفاوت شبانه‌روز تأیید کرد. همچنین، ازطریق تحلیل یکنواختی سری زمانی با استفاده از آزمون بیشاند، زمان وقوع تغییرات ناگهانی تصادفات در ساعت‌های متفاوت شبانه‌روز (7:00-8:00 صبح) و همچنین ماه‌های گوناگون سال (تیر و شهریور) شناسایی شد. نتایج استفاده از شاخص Moran’s I تفاضلی نیز همبستگی مکانی معنادار در تغییرات تصادفات عابران پیاده، بین دو بازۀ زمانی آغاز (سال 1394) و پایان زمان تحلیل (سال 1398) را نشان داد و نواحی دارای تمرکز تغییرات معنادار شناسایی شد.
نتیجه‌گیری: در این پژوهش، عابران پیاده به‌منزلۀ یکی از آسیب‌پذیرترین کاربران راه مورد توجه قرار گرفته‌اند و تغییرات وقوع تصادفات مرتبط با آنها در بازۀ زمانی پنج‌ساله‌ای (1394-1398)، با استفاده از تحلیل‌های سری زمانی و همچنین تحلیل زمانی‌ـ مکانی Moran’s I تفاضلی، در کلان‌شهر مشهد ارزیابی شده است. خودهمبستگی زمانی معنادار در مقیاس ماهیانه و ساعتی نیز در وقوع تصادفات به تأیید رسید و وقوع تصادفات عابران پیاده، در ماه‌های متفاوت سال و همچنین ساعات متفاوت شبانه‌روز، نیز روند مشخصی را نشان داد و درنَهایت، زمان وقوع تغییرات ماهیانه و ساعتی شناسایی شد. نتایج بیانگر خودهمبستگی زمانی‌ـ مکانی معنادار در تغییر تصادفات، در حد فاصل برش زمانی ابتدا (سال 1394) و انتهای زمان تحلیل (1398)، به‌ازای ماه‌های متفات بود. درعین‌حال همبستگی زمانی‌ـ مکانی معناداری، به‌ازای ساعات متفاوت، وجود ندارد و ازاین‌رو کوچک کردن مقیاس زمانی به از دست رفتن همبستگی‌های مکانی نیز منجر می‌شود. نتایج این پژوهش می‌تواند، در قالب گام نخست شناسایی و تحلیل الگوهای زمانی‌ـ مکانی، تغییرات تصادفات عابران پیاده را شناسایی و کمک کند متخصصان حوزۀ ایمنی و تصمیم‌گیرندگان، با بازرسی‌های محلی، نواحی استخراج‌شده را ارزیابی نمایند.

کلیدواژه‌ها

عنوان مقاله [English]

Spatio-Temporal Analyses of Pedestrian Accidents Using Time Series and Differential Moran’s I, Case Study: Mashhad

نویسندگان [English]

  • Matin Shahri
  • Mohammad Amin Ghannadi
Dep. of Geoscience Engineering, Arak University of Technology, Arak, Iran
چکیده [English]

Introdction: Pedestrians are considered the most vulnerable road users due to their lack of protective measures, making their safety crucial in transportation planning. Numerous studies have analyzed pedestrian accidents, focusing on predictive models, risk factors, and spatial-temporal patterns. These analyses highlight the importance of identifying high-risk areas and implementing preventive measures. Spatial and temporal autocorrelation effects are significant in understanding accident patterns, and methods like Moran’s I index and Kernel Density Estimation are commonly used. The study of Mashhad, Iran, emphasizes the impact of rapid socio-economic growth on traffic accidents and the need for targeted safety interventions to protect pedestrians.
Materials & Mrthods: In this study, a time series exploratory analysis was used to examine pedestrian accidents on a monthly and hourly basis over a five-year period (2015-2019). Next, the presence of temporal autocorrelation and trends in pedestrian accidents were discussed. Then, using time series homogeneity analysis, the change points in the occurrence of accidents were examined. Finally, to extract spatial patterns of changes in pedestrian accidents during the study period, the differential Moran’s I index was applied.
Results & Discussions: Using time series analysis, the temporal pattern and significant temporal autocorrelation in the monthly and hourly values of pedestrian accidents were confirmed. The results of the Mann-Kendall test, considering the effects of autocorrelation, also confirmed the presence of a significant trend in pedestrian accidents for different months of the year and different hours of the day. Additionally, through the homogeneity analysis of the time series using the Buishand test, the timing of sudden changes in accidents at different hours of the day (7:00-8:00 AM) and different months of the year (July and September) was identified. The results of using the differential Moran’s I index also showed significant spatial correlation in the changes in pedestrian accidents between the initial time period (2015) and the end of the analysis period (2019), identifying areas with significant changes.
Conclusion: In this study, pedestrians, as one of the most vulnerable road users, were considered, and the changes in the occurrence of related accidents over a five-year period (2015-2019) were evaluated using time series analysis and differential Moran’s I spatio-temporal analysis in the metropolis of Mashhad. Significant temporal autocorrelation in monthly and hourly scales was also confirmed in the occurrence of accidents, showing a specific trend in pedestrian accidents in different months of the year and different hours of the day. Finally, the timing of monthly and hourly changes was identified. The results showed significant spatio-temporal autocorrelation in the changes in accidents between the initial (2015) and final (2019) time slices for different months. However, there was no significant spatio-temporal correlation for different hours, indicating that reducing the temporal scale leads to the loss of spatial correlations. The results of this study can serve as a first step in identifying and analyzing spatio-temporal patterns, identifying changes in pedestrian accidents, and allowing safety experts and decision-makers to evaluate the identified areas through local inspections.

کلیدواژه‌ها [English]

  • Spatio-temporal analyses
  • Time series
  • homogeneity analyses
  • Differential Moran’s I
  • Pedestrian accidents

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