Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects

Macro and micro models for zonal crash prediction with application in hot zones identification

“…TAZs 1 ; Hadayeghi et al, 2010;Abdel-Aty et al, 2011;Pulugurha et al, 2013;Dong et al, 2014Dong et al, , 2015Xu and Huang, 2015). Among them, TAZs are now the only traffic-related zone system and are superior in being easily integrated with the transportation planning process, thus having been widely adopted.…”

“…Last decade has witnessed fast growing scope of scientific research to investigate crash propensity on macroscopic levels. Different areawide characteristics were considered, including road characteristics such as intersections density Xu and Huang, 2015), road length with different speed limit (Abdel-Aty et al, 2011;, road length with different functional classification (Quddus, 2008;Hadayeghi et al, 2010), junctions and roundabouts (Quddus, 2008); traffic patterns such as traffic flow and vehicle speed (Quddus, 2008;Hadayeghi et al, 2010); trip generation and distribution (Abdel-Aty et al, 2011;Dong et al, 2014Dong et al, , 2015; environment conditions such as total precipitation/snowfall, and number of rainy/snowy days per year (Aguero-Valverde and Jovanis, 2006); land use (Pulugurha et al, 2013); and socioeconomic factors such as population density , age cohorts (Aguero-Valverde and Jovanis, 2006;Dong et al, 2015;Hadayeghi et al, 2010), household incomes (Xu and Huang, 2015) and employment (Quddus, 2008;Hadayeghi et al, 2010).…”

Spatiotemporal and random parameter panel data models of traffic crash fatalities in Vietnam

“…spatial dependence or correlation and spatial heterogeneity (Anselin, 1988), have been considered in macro-level safety analyses. For example, Bayesian spatial approaches have been used to account for possible spatial correlation between areas (Aguero-Valverde and Jovanis, 2006;Quddus, 2008;Siddiqui et al, 2012;Wang et al, 2013;Xu et al, 2014;Dong et al, 2015;Lee et al, 2015;Song et al, 2015;Siddiqui and Watkins, 2016). To consider spatial heterogeneity, previous macro-level safety studies have adopted the geographically weighted regression (GWR) models (Hadayeghi et al, 2003;Erdogan, 2009;Hadayeghi et al, 2010;Li et al, 2013) and random parameter models (Coruh et al, 2015;Xu and Huang, 2015).…”

Real-time crash prediction on freeways using data mining and emerging techniques

“…Even though, some studies [14] find that a generalized linear model-based approach may lead to biased estimates when the independent variables demonstrate strong nonlinear features. The commonly used techniques in predicting real-time crash likelihood are Neural Network (NN) [3,15,16] and Support vector machines (SVMs) [17,18].…”