In automated lane-free traffic, vehicles can choose any arbitrary lateral location. This enables vehicle flocking where, compared to platooning, the grouping of vehicles is possible with smaller space gaps, not only longitudinally but also laterally. Vehicle flocking can fulfill several purposes, such as increasing the road capacity, saving energy by reducing the aerodynamic drag force, and dampening shockwaves. Within this paper, we develop a control framework for modeling vehicle flocks in automated lane-free traffic. The proposed control algorithm considers two types of agents: [ ] -agents representing potential flock mates and a [ ] -agent representing the virtual leader with collective objectives (e.g., slowing down in the case of traffic congestion ahead). Our algorithm is based on energy functions for flock centering and collision avoidance, a consensus algorithm for velocity matching, and navigational feedback exerted by the virtual leader. The virtual leader’s path, which should be followed by the flock, is defined in an upper-level controller. In addition, a feedback algorithm for dynamic road boundary control is implemented. We simulate the proposed approach with very promising results. We show that vehicle flocks are efficiently formed within a few seconds, speeds are successfully aligned, and vehicle arrangements stay stable under different scenarios. In addition, the lateral and longitudinal flock extension changes with different energy functions and changing road boundaries, and vehicle flocks follow the trajectory of the virtual leader. Most importantly, vehicle flocks stay stable in the case of perturbations and the induced shock is dampened efficiently because of slight changes in the vehicles’ lateral locations.

The need for additional capacity in motorway networks during periods of high demand is unavoidable if congestion is to be prevented. Increasing capacity by building new roads is often infeasible, leaving operation-based traffic control measures as the primary approach to exploit the existing infrastructure. In this paper, the novel concept of dynamic lane configuration is introduced, which opens a new avenue in motorway traffic control that harnesses the infrastructure for traffic improvement. The lateral capacity of the existing motorway infrastructure is under-utilized due to lanes that are much wider than the vehicle’s width. Dynamic lane configuration suggests that while current wide lanes ensure safety during high-speed driving, lower speed limits can be actively imposed during times of high traffic demand, allowing the lane width to be reduced, thanks to the reduced required lateral gap between vehicles at lower longitudinal speeds. By narrowing the lanes prior to congestion, it is possible to reclaim wasted space and add lanes to the road, leading to a dynamic capacity increase during the operation. This dynamic infrastructure layout with demand-responsive lane configuration during operation bridges the traffic management and infrastructure design. A model-based optimal control approach is developed to model the dynamic lane configuration and to define the times and locations of changing lane configuration. The proposed approach is tested in a simulation environment on two different motorway networks, each with a different configuration and demand profile. The promising results indicate the potential of the proposed approach in congestion mitigation and reducing travel time.

The growing popularity of mobility-on-demand fleets increases the importance to understand the impact of mobility-on-demand fleets on transportation networks and how to regulate them. For this purpose, transportation network simulations are required to contain corresponding routing methods. We study the trade-off between computational efficiency and routing accuracy of different approaches to routing fleets in a dynamic network simulation with endogenous edge travel times: a computationally cheap but less accurate Network Fundamental Diagram (NFD) based method and a more typical Dynamic Traffic Assignment (DTA) based method. The NFD-based approach models network dynamics with a network travel time factor that is determined by the current average network speed and scales free-flow travel times. We analyze the different computational costs of the approaches in a case study for 10,000 origin-destination (OD) pairs in a network of the city of Munich, Germany that reveals speedup factors in the range of 100. The trade-off for this is less accurate travel time estimations for individual OD pairs. Results indicate that the NFD-based approach overestimates the DTA-based travel times, especially when the network is congested. Adjusting the network travel time factor based on pre-processed DTA results, the NFD-based routing approach represents a computationally very efficient methodology that also captures traffic dynamics in an aggregated way.

Several issues in designing a vehicle platoon control system must be considered; among them, the speed consensus and space/gap regulation between the vehicles play the primary role. In addition, reliable and fast gap-closing/opening actions are highly recommended for establishing a platoon system. Nonetheless, the lack of research on designing a single algorithm capable of simultaneously coping with speed-tracking and maintaining a secure headway, as well as the gap-closing/opening problems, is apparent. As deep reinforcement learning (DRL) applications in driving strategies are promising, this paper develops a multi-task deep deterministic policy gradient (DDPG) car-following algorithm in a platoon system. The proposed approach combines gap closing/opening with a unified platoon control strategy; as such, an effective virtual inter-vehicle distance is employed in the developed DRL-based platoon controller reward. This innovative new distance definition, which is based on the action taken by the ego-vehicle, leads to a precise comprehension of the agent’s actions. Moreover, by imposing a specific constraint on a variation of the ego-vehicle’s relative speed with respect to its predecessor, the speed chattering of the ego-vehicle is reduced. The developed algorithm is implemented in the realistic traffic simulator, SUMO (Simulation of Urban Mobility), and the performance of the developed control strategy is evaluated under different traffic scenarios.