BIVARIATE MORAN'S I AND LISA TO EXPLORE THE CRASH RISKY LOCATIONS IN URBAN AREAS

Reducing the number and severity of urban crashes has always been a primary concern of urban planners and safety specialist. Identifying the critical locations is an essential step in urban management since the safety improvements are prioritized to hazardous regions. The spatial essence of crash data, particularly the presence of spatial autocorrelation reveals that crash occurrences are not only inclined to cluster in the same locations but within particular time intervals. This study aims to detect spatial-temporal dependencies among crash occurrences using bivariate Moran’s I and LISA (Local Indicator of Spatial Association). Employing the yearly number of crashes aggregated in 253 TAZs (Traffic Analysis Zone) in Mashhad, Iran over four successive years (2006 – 2009), indicated that both bivariate Moran’s I and LISA yielding significant patterns of spatial-temporal autocorrelation of crashes. The results of this analysis help the safety officials and urban planners to sufficiently allocate the limited resources such as budget and time by prioritizing the riskiest locations.

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