The relationship between urban street networks and the number of transport fatalities at the city level

Characterization of Black Spot Zones for Vulnerable Road Users in São Paulo (Brazil) and Rome (Italy)

“…According to the WHO, 1.24 million people were killed worldwide in 2010 due to traffic accidents. Middle-income countries, which are becoming motorized rapidly, are the hardest hit by traffic accidents, with approximately 70% of traffic-related deaths occurring in these countries [16,17]. This tragic scenario indicates that traffic accidents are a serious public health and welfare concern and can be considered as a global epidemic [12].…”

“…Public policies (government budget and engagement for transport safety and the level of enforcement of road traffic rules), personal characteristics (health and physical condition, income, education, age and gender), socio-economic conditions (economic growth) and demographic and geographic circumstances (average distance traveled, the amount of time traveled and the population density) are factors that influence the rates of VRU traffic accidents [16]. Consequently, understanding these various factors and identifying their separate and/or combined effects on accident frequency and severity is important [11].…”

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

“…A wide range of spatial units have been investigated, e.g. block groups (Dumbaugh and Rae, 2009), grid structure (Kim et al, 2006), census tracts (Wier et al, 2009), wards (Noland and Quddus, 2004), cantons (Aguero-Valverde, 2013), counties (Traynor, 2008;Huang et al, 2010), provinces (Erdogan, 2009;Tolón-Becerra et al, 2012) (Moeinaddini et al, 2014;Coruh et al, 2015), multiple provinces (Torre et al, 2007), states (Noland, 2003), countries (Kumara and Chin, 2004), and traffic analysis zones (TAZs) Hadayeghi et al, 2003;Lovegrove and Sayed, 2006;Pirdavani et al, 2012;Wang et al, 2013). Effects of spatial units on modelling results have been discussed in few studies Lee et al, 2014b;Xu et al, 2014).…”

“…population density (Hadayeghi et al, 2003;Noland and Quddus, 2004;Huang et al, 2010;Tolón-Becerra et al, 2012;Lee et al, 2014b), age groups (Noland, 2003;Noland and Oh, 2004;Noland and Quddus, 2004;Aguero-Valverde and Jovanis, 2006;Quddus, 2008;Huang et al, 2010;Aguero-Valverde, 2013;Lee et al, 2014a), income (Noland, 2003;Traynor, 2008;Pirdavani et al, 2012), GDPs (Kumara and Chin, 2004;Tolón-Becerra et al, 2012), and employment (Siddiqui et al, 2012); land use variables Lovegrove and Sayed, 2006;Pulugurtha et al, 2013;Wang et al, 2013;Lee et al, 2014b); healthcare variables Coruh et al, 2015); road infrastructure variables, e.g. 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 …”

Analyzing the relationships between the number of deaths in road accidents and the work travel mode choice at the city level

“…Although approximately 70% of traffic-related deaths occur in developing countries (Augustus, 2012), nearly 28,000 road fatalities were reported in European countries in 2012 (European Commission, 2013). These statistics indicate that concerns about road traffic crashes are very serious in developing countries and still exist in European countries (Moeinaddini et al, 2014a). Among all of the road fatalities in the EU, approximately 50% are car occupants, 15% are motorcyclists, 10% are cyclists or moped riders, and 20% are pedestrians (European Commission, 2013).…”