Geographical unit based analysis in the context of transportation safety planning

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

“…The positive effect of passenger distance travelled is expected, given that it is considered as a measure of exposure to safety risk. Previous studies also indicated the positive effect of VKT, another exposure measure, on traffic crashes (Dumbaugh and Rae, 2009;Abdel-Aty et al, 2013;Aguero-Valverde, 2013). Hospital density resulted in a negative parameter of −0.00308 (95%BCI: −0.00435 to −0.00178).…”

“…road density, intersection density, road length (Amoros et al, 2003;Hadayeghi et al, 2003;Noland, 2003;Lovegrove and Sayed, 2006;Pirdavani et al, 2012;Tolón-Becerra et al, 2012;Jiang et al, 2016), roads with different functions (Lovegrove and Sayed, 2006;Huang et al, 2010), and road network structures Moeinaddini et al, 2014); traffic pattern variables, e.g. vehicle kilometres travelled (VKT) (Dumbaugh and Rae, 2009;Abdel-Aty et al, 2013;AgueroValverde, 2013), highway usage (Traynor, 2008), traffic volume (Quddus, 2008;Wier et al, 2009), speed (Quddus, 2008), volume to capacity ratios (Hadayeghi et al, 2003), and trip generation and distribution (Abdel-Aty et al, 2011); and environmental variables, e.g. rainfall (Coruh et al, 2015) and snowfall (Aguero-Valverde and Jovanis, 2006).…”

Developing crash modification functions to assess safety effects of adding bike lanes for urban arterials with different roadway and socio-economic characteristics

“…Thus, the full SPF was used for calculating CMFs in the cross-sectional and before-after with EB methods. Besides, socio-economic parameters were collected from the US Census and used in the analysis since crashes are related to demographic characteristics as well (Lerner et al, 2001;Romano et al, 2006;Males, 2009;Girasek and Taylor, 2010;Huang et al, 2010;Abdel-Aty et al, 2013;Lee et al, 2013). The functional form of SPF for fitting the NB regression models is shown in Eq.…”

“…It is worth noting that crash frequency decreases as median household income increases. This may be because income level is correlated with the other socio-economic factors such as education level and employment rate, and these factors can contribute to the higher crash risk (Huang et al, 2010;Abdel-Aty et al, 2013). For instance, since the people from low-income community are less likely to receive safety information and purchase newer and safer vehicle or equipment, they are more likely to be involved in crashes (Martinez and Veloz, 1996).…”

Comparison of Adjacency and Distance-Based Approaches for Spatial Analysis of Multimodal Traffic Crash Data

“…The macro level analysis of crash data usually serves the purpose to understand the impact of demographic or socioeconomic changes within an area on the crash trends. This broader perspective is highly beneficial to the planners who design and propose policies to control different factors with the aim to reduce crashes (Abdel-Aty et al, 2013). On the other hand, the micro level approach investigates the geometric or traffic flow factors and proposes engineering solutions for mitigation of crashes.…”