Ricerc@Sapienza

Home-to-school routes are very sensitive areas: they represent, for children, a learning tool for their everyday activities, but if poorly designed, maintained and equipped they can expose them to traffic risks. Sidewalks’ inappropriate level of service and poor maintenance, especially, are main factors contributing to walking unsuitability, thus to poor comfort and safety levels for young pedestrians, and more in general for all the vulnerable non-motorized road users.

In the Intelligent Transportation Systems, integration of different components of the classical driver-vehicleinfrastructure system is supported by advances in technology and communications. This study presents a general road safety analysis framework that exploits different types of data on traffic, geometry, and accidents to develop a Road Safety Analysis Center and an on-board Road Safety Driver Advisory. The Road Safety Analysis Center considers different sources of data: accident inventories, road geometry, and floating car data, which reveal drivers' behavior.

The high energy consumption of the lighting installations in highway tunnels has become a hot topic in the last few years due to the high figures in terms of money, consumed energy, use of raw materials, emissions of greenhouse gases due to the remarkable number of manufactured elements, and maintenance, among others. In spite of the different strategies proposed up to date and their savings, the potential benefits of decreasing the maximum speed allowed in tunnels have not been considered in depth as a complementary measure yet.

Intelligent Transportation Systems aims at improving efficiency and safety of the transportation system by acting either on vehicle performances or assisting the driver with information on vehicle and traffic status. Although digital road graphs are available to derive quantitative parameters that describe the road geometry, the information provided usually includes speed limits and repetition of road signs.

Africa is the worst performing Continent in road safety and year after year the situation worsens. To reverse this trend, it is essential to bear in mind that road safety problems in Africa should be preferably considered in their own context, as well as the proposed solutions to address them.

Reliable and accurate data are a fundamental prerequisite to understand the magnitude of road safety problems in Africa and convince stakeholders to take certain actions. Reliable and accurate data are also needed to identify problems, risk factors and priority areas in order to formulate strategies, set targets and monitor performance.

One of the measures that can reduce the negative effects of road accidents is the quick arrive of emergency vehicles to the accident area. This measure requires an effective location in space and on time of these vehicles. This location can be decided after an analysis of the available data in order to find the spatial and temporal characteristics of road accidents. The study presented in this paper uses time series accident data of the 15 districts of Rome Municipality, collected in four months in 2016.

In 2011 the Africa Road Safety Action Plan (ARSAP) established an Action Plan to meet the objective of reducing road traffic crashes by 50% by the year 2020. Despite this effort, the situation worsens year after year and Africa is the continent with the worst road safety performance. To contribute reverse this trend, the SaferAfrica project, a joint effort of 17 partners from Africa and Europe, was launched in 2016. Within the framework of SaferAfrica project, the Crowdsourcing tool was developed and implemented through the African Road Safety Observatory.

Attention to the most vulnerable road users has grown rapidly over recent decades. The experience gained reveals an important number of fatalities due to accidents in urban branch roads. In this study, an analytical methodology for the calculation of urban branch road safety is proposed. The proposal relies on data collected during road safety inspections; therefore, it can be implemented even when historical data about traffic volume or accidents are not available.