Advancements in Road Safety Management Analysis

Road Safety Management (RSM) can be briefly defined as the tasks of preparing and implementing road safety policies. Many studies have been carried out on RSM, trying to identify success factors and reference best practice examples, but the complexity of the subject and the difficulty of quantitative data collection make it difficult a clear and comprehensive understanding. According to the EC-funded DACOTA research project, the weakest components of RSM systems in Europe are policy implementation and funding and the lack of knowledge-based road safety policy making. The main objective of the research, undertaken within the FERSI's working group on Road Safety Management (RSM), is to better investigate in several European countries those two RSM key functions: funding and research. Particularly the study aims at 1) exploring the existing structures, processes and factors affecting funding and research performances; 2) defining an assessment framework able to measure single country performances with reference to the efficiency and effectiveness of road safety funding and research, possibly shifting from a qualitative to a more quantitative approach. Based on the available knowledge on these two topics (research and funding), an assessment framework is defined and a set of qualitative and quantitative indicators for funding and research performance measurement is proposed. A desk analysis aiming at collecting available data useful to estimate the proposed indicators is conducted and a preliminary analysis with this subset of indicators is undertaken. A subset of research indicators (bibliometric) are used to estimate road safety research outputs performance of a country in terms of productivity and quality of research and international collaboration activities. Preliminary results show a positive correlation among them, even if the linear correlation turns to be not so strong. Countries are ranked on the basis of a composite index of all the three indicators

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

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, 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 Sao 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 Sao 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. Document type: Articl

Consultation of a panel of experts on the needs for data and technical tools in road safety policy-making. Deliverable 1.1/4.1 of the EC FP7 project DaCoTA

Consultation of a panel of experts on the needs for data and technical tools in road safety policy-making. Deliverable 1.1/4.1 of the EC FP7 project DaCoT

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

Steady-state solutions and multi-class calibration of Gipps' microscopic traffic flow model

This study sought to make a contribution to the problem of calibration of Gipps’ microscopic traffic flow model. The approach followed in the paper consists in first deriving traffic stream models, in the form of steady-state solutions of car-following models, and then in fitting such models to stationary traffic data. To this aim, traffic stream models for the Gipps model were first attained and an explicit formula for the flow at capacity, as a function of microscopic parameters, is provided. Analysis of the models for different combinations of microscopic parameters explained the widely-held belief that the Gipps model is unable to reproduce unstable traffic phenomena. To be suitable for model parameter calibration in simulation practice, single-class models were generalized to a multi-class traffic scenario for which a calibration procedure was developed. Once applied to real motorway traffic data, the latter proved its effectiveness in terms of error statistics. Values of calibrated parameters were all significant and consistent with expectations. Moreover they were consistent with the observed aggregate measures (e.g. flow at capacity). Finally, unlike non-stationary, model-based approaches, the computing time required by the multi-class calibration presented is negligible, allowing us to calibrate a large number of parameters i.e. to calibrate different classes of vehicles