When traffic density reaches a critical point, even minor speed disturbances may trigger traffic breakdown at freeway bottlenecks. To prevent it and maintain a free flow, it is important to inform drivers about the actual traffic state correctly so as to enable them to adjust their driving behavior according to dynamic changes in the traffic state. Conventionally, a traffic state is numerically represented by such indices as density, volume, and speed, which are converted into a simple sign to provide traffic state information to drivers. However, it can represent limited information insufficient for drivers to understand dynamics of traffic flow. In the present paper, we propose a novel method to represent complex traffic dynamics intuitively by applying data sonification, a technique for rendering sound in response to the online data. Concretely, individual vehicle data collected by loop detectors at a freeway bottleneck is rendered in sound signals. As a result of the conducted sensory evaluation experiment, it was found that respondents could correctly distinguish the traffic states including the free flow, flow at crowded condition, flow just before traffic breakdown, and jam flow.

In light of the increasing trend towards vehicle connectivity and automation, there will be areas and situations on the roads where high automation can be granted, and others where it is not allowed or not possible. These are termed 'Transition Areas'. Without proper traffic management, such areas may lead to vehicles issuing take-over requests (TORs), which in turn can trigger transitions of control (ToCs), or even minimum-risk manoeuvres (MRMs). In this respect, the TransAID Horizon 2020 project develops and demonstrates traffic management procedures and protocols to enable smooth coexistence of automated, connected, and conventional vehicles, with the goal of avoiding ToCs and MRMs, or at least postponing/accommodating them. 
Our simulations confirmed that proper traffic management, taking the traffic mix into account, can prevent drops in traffic efficiency, which in turn leads to a more performant, safer, and cleaner traffic system, when taking the capabilities of connected and autonomous vehicles into account.

The technological advances in connected and automated vehicles (CAVs) enable cooperative (automated) vehicles to exchange information with not only infrastructure but also among vehicles. Joint control of traffic signals and vehicle trajectories has the potential to improve traffic operations and environmental economy on urban roads. Exiting literature on CAV platooning on urban roads mainly focus on driver assistant systems, cooperative vehicle intersection control algorithms, CAV trajectory optimization, and the integrated optimization of traffic signals and vehicle trajectories. However, most of these algorithms were designed to optimize simple objective functions of the platoon leaders, which cannot reflect the benefits of the whole platoon. We propose a hierarchical approach for joint design of signal timing and cooperative (automated) vehicle trajectories at typical four-arm intersections with predetermined phase sequence. The upper layer of the proposed control approach determines the optimal lengths of signal phases based on optimal control and a surrogate model of CAV platoon dynamics. For simplification, only the accelerations of the platoon leaders and the first-stopping vehicles are optimized, while the other following vehicles are represented using a car-following model. The running cost considering multiple terms (i.e. throughput, comfort, travel delay, safety, fuel consumption) is piecewise according to the signal indication and the vehicle sequence in the platoon. Additional penalty terms for first-stopping vehicles have an advantage on longer horizon like consecutive signal cycles, rather than limited within one signal cycle. The acceleration and speed are constrained within maximal and minimal bounds. The lower layer optimizes trajectories of all vehicles using model predictive control approach under the fixed but optimal signal plan resulted from the upper layer. The signal indication in the lower layer is transferred to the first-stopping vehicle at the beginning of the current signal cycle, which can operate the stopping vehicles smoother than trajectories without communicating the signal information.