Recently, the number of traffic safety studies involving connected and autonomous vehicles (CAVs) has been increasing. Due to the lack of information regarding the real behaviour of CAVs in mixed traffic flow, traffic simulation platforms are used to provide a reasonable approach for testing various scenarios and fleets. It is necessary to analyse how traffic safety is affected when key parameter assumptions are changed. The current study conducts a sensitivity analysis to identify the parameters used in CAV calibration that have the highest influence on traffic safety. Using a microsimulation-based surrogate safety assessment model approach (SSAM), traffic conflicts were identified, and a ceteris paribus analysis was conducted to measure the effect of gradually changing each parameter on the number of conflicts. Afterwards, a two-at-a-time sensitivity analysis was performed to explore the influence of simultaneously varying two parameters. The results revealed that reaction time, clearance, maximum acceleration, normal deceleration, and the sensitivity factor are key parameters. Studying these parameters two at a time revealed that low maximum acceleration, when combined with other parameters, consistently resulted in the highest number of conflicts, while combinations with short reaction time always yielded the best traffic safety results. This investigation broadens the understanding of CAV behaviour for future implementation for both manufacturers and researchers.

Connected and Autonomous Vehicles (CAVs) are becoming a reality and are progressively penetrating the markets level by level. CAVs are a promising solution for traffic safety. However, robust studies are needed to explore and assess the expected behavior. This study attempts to evaluate traffic safety resulting from a near-real introduction of CAVs with different levels of automation (from Level 1 to Level 4). The investigation consisted of modeling different CAV levels using Gipps’ model, followed by the simulation of nine mixed fleets at a motorway segment. Subsequently, the Surrogate Safety Assessment Model was used for safety analysis. According to the results: (1) the gradual penetration of CAV levels led to a progressive reduction in traffic conflicts, ranging from 18.9% when the penetration of high levels of automation (Level 3 and Level 4 vehicles) is 5%, to 94.1% when all the vehicles on the traffic flow are Level 4; (2) human-driven vehicles and vehicles with low levels of automation (Level 1 and Level 2 vehicles) are more frequently involved in conflicts (as follower vehicles) than vehicles with high automation levels. E.g. human-driven vehicles are involved in conflicts from 8% to 122% more, while vehicles with high automation levels are involved in conflicts from 80% to 18% less than their sharing percentages, respectively, depending on different mixed fleets. This study confirms the theory and conclusions from previous literature that indicate a safety gain due to CAV penetration. Moreover, it provides a broader perspective and support for the introduction of CAVs levels.

The drivers of Powered Two-Wheelers (PTWs) pertain to the collective of so-called vulnerable road users. Crashes have scarcely decreased for these roadway users in recent years, whereas among other users, e.g. cars users, they have declined considerably. Meanwhile, the use of PTWs has risen sharply worldwide. This situation adds a further concern to transportation policies and makes evident the need to explore factors involved in PTW crashes. Yet there is a lack of studies specifically about road safety for PTWs. The present study therefore aspires to advance in the knowledge of the factors affecting PTW crashes on interurban roadways, by means of analyzing the effects of some variables not considered previously in this type of studies —mainly economic resources invested in roads—while also accounting specifically for the exposure to risk of PTWs (veh-km), along with relevant variables related to road traffic, the roadway infrastructure, and socioeconomic, meteorological and legislative factors. To this end, and bearing in mind the latest advancements of incorporating unobserved heterogeneity in count data models, different configurations of random parameters negative binomial models for data panels are presented. The realm of study is the network of national roads in Spain, distributed over 43 provinces, and the time period between 2007 and 2015. The results show significant associations for 11 of the variables considered: annual and accumulated investment in construction, expense on maintenance, proportion of motorways, light and heavy vehicle traffic, per capita GDP, age, unemployment rate, price of gasoline, and modification of the demerit point system (DPS). With respect to transport policy implications, the findings provided in this study may serve to monitor the effects of economic resources allocated to road construction and maintenance —along with other measures, such as gasoline prices and DPS—on PTWs safety.

This study analyzes how economic resources invested in roads may affect mortality, depending on the level of economic development of a country. To this end, 23 European countries were classified into two groups—high-income countries and low-income countries—according to their average Gross Domestic Product (GDP) per capita over the period 1998–2016. The economic resources are considered through the investment in construction and the maintenance expenditure. Further variables are included to control for several factors related to the infrastructure, socioeconomics, legislation, and meteorology. Fixed-effects panel data models were built separately for the interurban road network of each group of countries. These models also capture the international inequalities within each group and the country-specific national trend for the study period. The main results indicate a reduction effect on the fatality rate of road maintenance expenditure (in both groups), and of the investment in construction (in the low-income countries). Other variables—such as proportion of motorways, motorization rate, unemployment rate, GDP per capita, alcohol consumption, Demerit Point System, and mean annual precipitation—showed statistically significant results as well. Finally, the country-specific fixed effects and the country-specific trend were mapped geographically, to better reflect national conditions for achieving lower fatality rates in the high-income countries, and greater progress in reducing fatalities in the low-income countries. In the end, this study provides evidence to policy-makers that can help to achieve a safer and more sustainable transport system, namely, how to tackle an ongoing major problem—traffic-related deaths—when attending and allocating the economic resources that road infrastructure needs.