Spatial analysis of harsh driving behavior events in urban networks using high-resolution smartphone and geometric data.

The aim of the present study is to conduct spatial analysis of harsh events of driving behavior across road segments of an urban road network. The adopted approach involved automating the segment characteristic extraction process for the urban network study area. Subsequently, naturalistic driving big data from an innovative smartphone application were map-matched to the segments that each driver traversed, and thus geometrical, road network and driver behavior spatial data frames were obtained per road segment. Global and local Moran's I coefficients were calculated based on a nearest-neighbour scheme, and indicated the presence of a certain degree of positive spatial autocorrelation both for harsh brakings (HBs) and for harsh accelerations (HAs). Furthermore, the creation of empirical and theoretical spherical variograms indicated that on average, about 190 m from each road segment centroid there is no observable spatial autocorrelation for HBs; the respective distance is 200 m for HAs. Geographically Weighted Poisson Regression (GWPR) models were used to model harsh event frequencies. Segment length and pass count are positively correlated with HB frequencies, while gradient and neighbourhood complexity are negatively correlated with HB frequencies. Curvature, segment length, pass count and the presence of traffic lights are positively correlated with HA frequencies. Road type and lane number were found to have a more circumstantial effect overall.

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