RSS 2017

ارائه مقالات پذیرفته‌شده در کنفرانس RSS2017

همزمان با برگزاری کنفرانس ایمنی راه و شبیه‌سازی سال ۲۰۱۷ – Road Safety and Simulation 2017، دو مقاله ارسال‌شده توسط اعضای تیم آزمایشگاه تحقیقانی ترافیک دانشگاه علم‌وصنعت ایران در این کنفرانس ارائه شد. در ادامه عناوین و خلاصه‌ این مقالات آورده شده است:

 

“Explanatory Spatial Analysis of Urban Traffic Crashes by Developing Semi-Parametric Geographically Weighted Poisson Regression”

Afshin Shariat Mohaymany, Matin Shahri, Saeed Rahmani
School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Email: shariat@iust.ac.ir
Email: matin_shahri@civileng.iust.ac.ir
Email: Saeed_rahmani@civileng.iust.ac.ir

 

Abstract:

The increasing road construction along with the increase in the number of vehicles in cities has enormously increased the number and severity of urban crashes. Therefore, developing innovative, robust models for the aim of crash prediction has been the concern of transportation specialists for so long. Global regression models with assuming Poisson or Negative Binomial distribution for errors describe the relationships between crashes and independent variables through estimating fixed coefficients which do not vary over the study area. Considering the spatial nature of crash data as well as its relating factors, actual spatial patterns might vary with local site conditions, which is also recognized as spatial non-stationarity. Accordingly, Semi parametric geographically weighted Poisson regression (S-GWPR) is employed for crash count data aggregated on traffic analysis zones (TAZs) in Mashhad, Iran, to capture the spatial heterogeneity that exists in the relationship between crash counts and explanatory variables over the study area. Then the global Poisson model and S-GWPR are compared by the measures of goodness of fit such as the correlation coefficient, percent deviance explained (PDE), AICc, and MSE to identify whether the new model indicates any improvement over the global one. Moran’s I indicator has been carried out on residuals of the two models to compare the ability of each model in addressing spatial autocorrelation. The results revealed that S-GWPR not only represents a significant improvement of model performance over the global model indicated, but also has successfully addressed the spatial autocorrelation of residuals. Furthermore, according to the results of local coefficients estimated for different network variables used in the model, regions with higher priority of improvement measures for different road types can be identified.

 


Spatio-temporal Analyses of Motorcycle Traffic Accidents Case Study: Mashhad, Iran

Matin Shahri, Afshin Shariat Mohaymany, Saeed Rahmani
School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Email: matin_shahri@civileng.iust.ac.ir
Email: shariat@iust.ac.ir
Email: Saeed_rahmani@civileng.iust.ac.ir

 

Abstract:

Motorcycle is known as one of the most frequently used transportation modes for different trip purposes. Motorcyclists are one of the most vulnerable users of the transportation system due to the low stability of the motorcycle and insufficient protection for passengers. This paper aims to detect spatio–temporal dependencies of motorcycle accidents (MAs) using bivariate global and local indicator of spatial association based on monthly analyses of aggregated motorcycle accidents over 253 traffic analysis zones (TAZ) in Mashhad, Iran for the successive three years (2006-2008) to investigate whether the accidents demonstrate cluster or disperse patterns of autocorrelation. The indicators were examined for number of motorcycle accidents that occurred in every TAZ in Mashhad for one month as the original variable and the spatial lag of number of motorcycle accidents for the next month as the second variable. The analyses were conducted separately for months of every seasons. The result of applying bivariate global autocorrelation statistics indicated significant global spatio-temporal pattern of clustering. In other words, it seems that motorcycle accidents in Mashhad have some kind of organized spatio–temporal patterns. More information on the type of clustering was provided by an analysis of bivariate local spatial autocorrelation indicators (BLISA). BLISA cluster maps for monthly motorcycle accidents have been extracted which indicate the patterns of clustering between motorcycle accidents per TAZ in one month and the average number of motorcycle accidents in the second month of study for its neighbours. Considering the fact that the results indicated the clustering of motorcycle accidents, the regions that needed to be particularly targeted with safety-attention programs are explored. It can provide some guidance for decision makers and intervention planners where they should implement intervention action plans. Therefore, the planners can appropriately allocate the limited budget and time for safety enhancements.

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