Connected and Automated Driving (CAD) will bring disruption to individuals, economies, and society. Most forms of CAD require some level of support from the infrastructure for their safe operation, in particular communications. However, additional infrastructure services to support CAD could improve safety and robustness and bring further benefits such as increased capacity. However, the infrastructure requirements of vehicle Original Equipment Manufacturers (OEMs) are not always clear, and it is therefore difficult for National Road Authorities (NRA) to prepare future levels of support for CAD, given rapidly evolving technology and uncertain projections of future CAD demand. There is a need to articulate those requirements, bringing stakeholders together to formulate a structured approach, and a roadmap that will advance safe and smart roads that support CAD.

This paper presents the DiREC project (consortium partners: TRL, ARUP, TU Delft, VTT, VTI and FEHRL) funded by the CEDR Transnational Road Research Programme Call 2020 with funding provided by CEDR members of Belgium (Flanders), Denmark, Ireland, Israel, Netherlands, Norway, Sweden, Switzerland and the United Kingdom. DiREC is seeking to address the above challenge. The project has established a CAV-Readiness Framework (CRF) based on a level of service approach to understand the needs of CAD, and to define the infrastructure and services that NRAs could provide to support these needs.

The estimation of the free flow speed (FFS) distribution is important for capacity analysis, determination of the level-of-service, and setting speed limits. Subjective time headway thresholds have been commonly used to identify vehicles travelling under free flow speed conditions i.e., vehicles whose speeds are not influenced by the vehicle in front. Since, the headway a driver operates under the free flow state is subjective and varies from driver to driver, such approaches can introduce biases in the FFS estimation. Therefore, in this paper a parametric probabilistic latent approach is proposed based on discrete choice utility theory to estimate the FFS distribution on urban roads and simultaneously the probability that drivers perceive their state as constrained by the vehicle in front. This methodology is used to estimate the impacts of road characteristics and Posted Speed Limit (PSL) changes on the FFS distribution using an extensive dataset of speed observations from urban roads with varying characteristics. The results show that the simultaneous estimation of the free flow speed distribution and the state the driver is in (e.g., free or constrained) is feasible. The analysis indicates that the FFS is influenced by several road characteristics such as land use, on-street parking and the presence of sidewalks. The PSL change impacts not only the distribution of the free flow vehicles but also the speed distribution of the constrained vehicles. The constrained probabilities vary depending on the PSL change with higher probabilities for lower speed limits.

In-vehicle technologies and cooperative services are attracting a lot of attention for their potential to deal with congestion problems and improve traffic safety. This paper aims to investigate the impact of infrastructure-to-vehicle cooperative systems, case of COOPERS, at the aggregate level, on traffic performance. A factorial experiment is designed with two factors: traffic demand and penetration of the system with three levels each. In total, nine scenarios are investigated. To replicate driving behavior with and without the system, speed distributions from a simulator experiment are used. A motorway section of 4 km is built in VISSIM simulation software. Indicators such as speed, density, delays and travel times are chosen to evaluate and compare the motorway performance with and without the system. The results show that drivers driving with the system activated are more aware and alert to near future traffic conditions compared to driving without the system. Driving with the system activated is characterized by smoother and longer speed decelerations when approaching critical incident/accident events. The results show as well that the factors investigated significantly impact the motorway performance. Congestion reduces the impact of the system whereas higher penetration levels improve traffic operation on the motorway. Future research directions can include (1) investigating the impact of the system at the micro level such as lane changing or car-following behaviors; (2) levels of compliance with the system, which is an important aspect as well.