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

A two-phase clustering approach for traffic accident black spots identification: integrated GIS-based processing and HDBSCAN model

Identifying black spots effectively and accurately is a pivotal and challenging task to improve road traffic safety. A novel black spot identification model is proposed by integrating the GIS-based processing with hierarchical density-based spatial clustering of applications with noise. Additionally, the optimal clustering parameters are determined based on an internal validation indicator called the density-based clustering validation index to minimize the impact of subjectivity in parameter selection. The model is validated by collecting 3536 accident data from 1 August to 31 October 2020 in Hangzhou, China, and eventually identifies 39 black spots. The results show that: (1) The number of accidents contained in black spots account for 75% of all accidents, while the length of network in the black spots only account for 23.26% of the total road network length. (2) Compared with the conventional density-based spatial clustering of applications with noise model and K-means model, the proposed model achieves the best performance with more accidents gathered per unit road length. (3) The sample survey with 6 onsite of the identified black spots indicates that the proposed model has high recognition accuracy and recommend these sites for further investigation.

Vulnerable road-user deaths in Brazil: a Bayesian hierarchical model for spatial-temporal analysis

Reducing the road traffic injuries burden is relevant to many sustainable development goals (SDG), in particular SDG3 - to establish good health and well-being. To describe the spatial-temporal trends and identify hotspot regions for fatal road traffic injuries, a Bayesian hierarchical Poisson model was used to analyze data on vulnerable road users (bicyclist, motorcyclist and pedestrians) in Brazil from 1999 to 2016. During the study period, mortality rates for bicyclists remained almost unchanged (0.6 per 100,000 people) but rose dramatically for motorcyclists (from 1.0 in 1999 to 6.0 per 100,000 people in 2016) and decreased for pedestrians (from 6.3 to 3.0 per 100,000 people). Spatial analyses accounting for socio-economic factors showed that the central and northeastern microregions of Brazil are hotspot areas for fatal injuries among motorcyclists while the southern areas are for pedestrians.

Modelling Sustainable Urban Transport Performance in the Jakarta city Region: A GIS Approach

Measuring performance of Sustainable Urban Transport is an important effort to face the challenges of future trends. This study used Geographic Information System (GIS) application for modelling the performance of Sustainable Urban Transport (SUT) in the Jakarta city Region (JCR). The GIS applications include identifying the existing performance of SUT in Jakarta city, exploring the relationships between indicators of SUT, and producing a prediction model of SUT. Research methods used in this study were GIS techniques with geo-reference, classifications, polygon to raster, re-classifications, sum-weighted, ordinary least square (OLS), exploratory regression, and geographic weighted regression (GWR). The results revealed that the SUT model have more capability in measuring the performance of SUTs spatially and simply. This model is to visualize the effect of the indicator on the SUT performance and its influence, respectively. The results of this study also discovered that the JCR’s Sustainable Urban Transport Performance was in medium level. The outputs of this modelling were useful for evaluating the level of SUT performances in the city based on districts area. Overall, this study provides valuable information on the SUT performance of the JCR, also highlights some important challenges faced in the future of SUT program development.

Exploring spatial autocorrelation of traffic crashes based on severity

As a developing country, Iran has one of the highest crash-related deaths, with a typical rate of 15.6 cases in every 100 thousand people. This paper is aimed to find the potential temporal and spatial patterns of road crashes aggregated at traffic analysis zonal (TAZ) level in urban environments. Localization pattern and hotspot distribution were examined using geo-information approach to find out the impact of spatial/temporal dimensions on the emergence of such patterns. The spatial clustering of crashes and hotspots were assessed using spatial autocorrelation methods such as the Moran's I and Getis-Ord Gi* index. Comap was used for comparing clusters in three attributes: the time of occurrence, severity, and location. The analysis of the annually crash frequencies aggregated in 156 TAZ in Shiraz; from 2010 to 2014, Iran showed that both Moran's I method and Getis-Ord Gi* statistics produced significant clustering of crash patterns. While crashes emerged a clustered pattern, comparison of the spatio-temporal separations showed an accidental spread in distinct categories. The local governmental agencies can use the outcomes to adopt more effective strategies for traffic safety planning and management.