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

Abstract

Non-motorized transportation modes, especially cycling and walking, offer numerous benefits, including improvements in the livability of cities, healthy physical activity, efficient urban transportation systems, less traffic congestion, less noise pollution, clean air, less impact on climate change and decreases in the incidence of diseases related to vehicular emissions. Considering the substantial number of short-distance trips, the time consumed in traffic jams, the higher costs for parking vehicles and restrictions in central business districts, many commuters have found that non-motorized modes of transportation serve as viable and economical transport alternatives. Thus, local governments should encourage and stimulate non-motorized modes of transportation. In return, governments must provide safe conditions for these forms of transportation, and motorized vehicle users must respect and coexist with pedestrians and cyclists, which are the most vulnerable users of the transportation system. Although current trends in sustainable transport aim to encourage and stimulate non-motorized modes of transportation that are socially more efficient than motorized transportation, few to no safety policies have been implemented regarding vulnerable road users (VRU), mainly in large urban centers. Due to the spatial nature of the data used in transport-related studies,859 geospatial technologies provide a powerful analytical method for studying VRU safety frameworks through the use of spatial analysis. In this article, spatial analysis is used to determine the locations of regions that are characterized by a concentration of traffic accidents (black zones) involving VRU (injuries and casualties) in São Paulo, Brazil (developing country), and Rome, Italy (developed country). The black zones are investigated to obtain spatial patterns that can cause multiple accidents. A method based on kernel density estimation (KDE) is used to compare the two cities and show economic, social, cultural, demographic and geographic differences and/or similarities and how these factors are linked to the locations of VRU traffic accidents. Multivariate regression analyses (ordinary least squares (OLS) models and spatial regression models) are performed to investigate spatial correlations, to understand the dynamics of VRU road accidents in São Paulo and Rome and to detect factors (variables) that contribute to the occurrences of these events, such as the presence of trip generator hubs (TGH), the number of generated urban trips and demographic data. The adopted methodology presents satisfactory results for identifying and delimiting black spots and establishing a link between VRU traffic accident rates and TGH (hospitals, universities and retail shopping centers) and demographic and transport-related data.

Reviewing the use of Geographic Information System (GIS) to measure Sustainable Urban Transport performance

“…Whereas (Wang, 2005) explored the integrating of GIS as spatial analysis simulation models to add a temporal dimension and computer visualization to add more options for presentation, all works to support planning process. GIS with overlay analysis used traffic safety by mapping accident location data, performing kernel density analyses and combining the results (Machado et al, 2015). The domination of network analysis of GIS approach in Traffic and road network seen from previous research.…”

“…-to analyse the impact of traffic congestion on road accidents -to analyzed traffic safety for vulnerable road users by mapping accident location data -performing kernel density analyses and combining the results (Miller and Storm, 1996), (Thill, 2000), (Chen et al, 2011), (Mavoa et al, 2012), (Zhang et al, 2016), (Aljoufie, 2014), (Yigitcanlar and Dur, 2010), (Abreha, 2007), (Al-Ali, 2016), (Fedra, 1999), (Wang, 2005), (Machado et al, 2015), (Valdes et al, 2016) Transport Infrastructure -as presentation tools to presented result of analysis, with classification analysis -supports handling of spatial data from remotely sensed imagery and integrates it with other images and ancillary data from different sources -to forecast travel demand in collaboration between GIS based and spatial regression models (Dalumpines, 2008), (Aljoufie and Tiwari, 2016) The research topic in SUT performance is dominantly by traffic and road network and accessibility. Then, the average papers about noise pollution, public transport and transport infrastructure.…”

“…-traffic model for traffic flows mapping -traffic simulation and analysis -presenting road network -to assess the level of service of the existing public bus transport system -to identify deficient network elements (Paudel et al, 2009), (Gutiérrez et al, 2010), (Mavoa et al, 2012), (Murad, 2005), (Chapleau and Morency, 2005), (Ford et al, 2015), (Miller and Storm, 1996), (Thill, 2000), (Chen et al, 2011), (Mavoa et al, 2012), (Zhang et al, 2016), (Aljoufie, 2016a), (Yigitcanlar and Dur, 2010), (Abreha, 2007), (Al-Ali, 2016), (Fedra, 1999), (Wang, 2005), (Machado et al, 2015), (Brown and Affum, 2002), (Armstrong and Khan, 2004), (Gualtieri and Tartaglia, 1998), (Arampatzis et al, 2004), (Brown and Affum, 2002), (Miller and Storm, 1996) Buffer analysis -the space-time geography framework. isochrones (lines of equal travel time) are a natural way.…”

GIS for Sustainable Urban Transport

“…Claudia Soares et al [1] analyzed traffic safety for vulnerable road users by mapping accident location data (so called black spots), performing kernel density analyses and combining the results, through GIS overlays, with characteristics of surrounding areas and more traditional statistical regression to arrive at conclusions on which factors best explain high levels of traffic accidents. By their method they demonstrate the strengths of GI based data integration.…”

“…We see application of typical GIS strengths, such as digital map production (articles 1 and 2) [1,2], dynamic modelling (article 3) [3], spatial inventory of facilities/events (articles 1, 2, 4, 5 and 6) [1,2,[4][5][6], spatial data integration (articles 1, 2 and 5) [1,2,5] and spatial analysis (articles 1, 2, 4 and 6) [1,2,4,6], where it can be noted that several authors are using these GIS capabilities in combination and in integration with various statistical techniques (articles 1, 3 and 5) [1,3,5].…”