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In this paper, we propose a novel cost function to be embedded in a distributed algorithm for cooperative rerouting of connected and automated vehicles (CAVs) in urban networks. The computation is performed by intersection units managing the portion of the network for which they are in charge by sending updated routes to follow to the CAVs there. Moreover, the intersection units communicate among each other to be updated on the situation of the (in general nonhomogeneous) links of the network. The proposed approach allows the decomposition of the problem into subproblems, which are resolved distributively with little information exchange. The problems are constructed to obtain a fair compromise between user equilibrium and system optimum, taking advantage of the strengths and coping with the limitations of both. We 1) show the results obtained by running simulations with CAVs on the Sioux Falls network compared with a baseline scenario; 2) analyze the results obtained with different penetration rates of CAVs; and 3) provide a sensitivity analysis to investigate how parameters in our approach affect the results of the experiments.

A feedback control strategy is proposed for lane assignment at bottleneck locations. The strategy assumes that some vehicles equipped with vehicle automation and communication systems are capable of receiving and executing specific lane-changing orders or recommendations. From a previously proposed optimal control strategy based on a simplified multilane motorway traffic flow model and formulated as a linear quadratic regulator, a feedback control problem was designed. The aim was to maximize the throughput at bottleneck locations while distributing the total density at the bottleneck area over the lanes according to a given policy by optimal lane assignment of the vehicles upstream of the bottleneck. The feedback control decisions were based on real-time measurements of the traffic state and inflow. The proposed strategy was tested on a nonlinear first-order macroscopic multilane traffic flow model, which also accounted for the capacity drop phenomenon.

A feedback control strategy is proposed for lane assignment at bottleneck locations. The strategy assumes that some vehicles equipped with vehicle automation and communication systems are capable of receiving and executing specific lane-changing orders or recommendations. From a previously proposed optimal control strategy based on a simplified multilane motorway traffic flow model and formulated as a linear quadratic regulator, a feedback control problem was designed. The aim was to maximize the throughput at bottleneck locations while distributing the total density at the bottleneck area over the lanes according to a given policy by optimal lane assignment of the vehicles upstream of the bottleneck. The feedback control decisions were based on real-time measurements of the traffic state and inflow. The proposed strategy was tested on a nonlinear first-order macroscopic multilane traffic flow model, which also accounted for the capacity drop phenomenon.

This paper presents two case studies in which a macroscopic model-based approach for the estimation of traffic conditions, which the authors have recently developed, is employed and tested. The estimation method is developed for a mixed traffic scenario, in which traffic is composed of both ordinary and connected vehicles. Only average speed measurements, which may be obtained from connected vehicle reports, and a minimal number (sufficient to guarantee observability) of spot sensor–based total flow measurements are utilized. In the first case study, NGSIM microscopic data are used to test the capability of estimating traffic conditions on the basis of aggregated information retrieved from moving vehicles and considering various penetration rates of connected vehicles. In the second case study, a longer highway stretch with internal congestion is utilized to test the capability of the proposed estimation scheme to produce appropriate estimates for varying traffic conditions on long stretches. In both cases, the performances are satisfactory and the obtained results demonstrate the effectiveness of the method in both qualitative and quantitative terms.

This paper presents two case studies in which a macroscopic model-based approach for the estimation of traffic conditions, which the authors have recently developed, is employed and tested. The estimation method is developed for a mixed traffic scenario, in which traffic is composed of both ordinary and connected vehicles. Only average speed measurements, which may be obtained from connected vehicle reports, and a minimal number (sufficient to guarantee observability) of spot sensor–based total flow measurements are utilized. In the first case study, NGSIM microscopic data are used to test the capability of estimating traffic conditions on the basis of aggregated information retrieved from moving vehicles and considering various penetration rates of connected vehicles. In the second case study, a longer highway stretch with internal congestion is utilized to test the capability of the proposed estimation scheme to produce appropriate estimates for varying traffic conditions on long stretches. In both cases, the performances are satisfactory and the obtained results demonstrate the effectiveness of the method in both qualitative and quantitative terms.