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

Document Type : Original Article

Authors

Dep. of Geoscience Engineering, Arak University of Technology, Arak, Iran

Abstract

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.

Keywords

References

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