Once trusted, automated vehicles (AVs) will gradually appear in urban areas. Such a transition is an opportunity in transport planning to control undesired impacts and possibly mitigate congestion at a time when both conventional vehicles (CVs) and AVs coexist. This paper deals with the complex transport decision problem of designing part of the network that is exclusive for AVs through a nonlinear programming model. The objective function minimises the costs of travel times where vehicles circulate under user equilibrium. The model evaluates the benefits of having an AVs-dedicated infrastructure and the associated costs from the detouring of CVs. Three planning strategies are explored: incremental, long-term and hybrid planning. The first creates a subnetwork evolving incrementally over time. The second reversely designs a subnetwork from the optimal solution obtained at a ratio of 90% AVs. The third limits the incremental planning towards that optimal long-term solution. The model is applied to the city of Delft, in the Netherlands. Two scenarios are analysed, with and without AV-dedicated roads, at several AV penetration rates. We find that implementing dedicated roads for AVs reduces the overall costs and congestion up to 16%. However, CV detouring is inevitable at later network stages, increasing the total distance travelled (up to 8%) and congestion in the surroundings of AV subnetworks. Concerning the planning strategies, incremental planning is appropriate for starting in the initial stages and is the strategy that most tackles CV detouring. The hybrid or the long-term strategies are more suitable to be applied after a ratio of 50% AVs, and the hybrid planning is the strategy that most reduces delay. @en

AV subnetworks is a way to deal with automated traffic and its technological need that will likely increase during the AVs deployment period. This strategy carries many benefits, yet some inconveniences are worth to mention. One of them relies on the fact that the design of AV subnetworks is often in practice focused on mitigating congestion in the peak-hours. However, designing for the most congested hour can be quite delicate when such a strategy is fixed throughout the day. The remaining part of the day involves different mobility patterns and shifting trips patterns throughout the day, i.e., different Origin-Destination pairs. When such O-D pairs are inside these AV subnetworks, CV owners cannot drive, and therefore a new mode of transport is necessary. This paper focuses on the lower-level decision problem, i.e., the traffic distribution during the transition period while AVs are being deployed in urban areas and AV subnetworks are expanding. A nonlinear mathematical programming model is presented to perform the trip distribution, where walking appears as an alternative. The main objective of this paper is to study the impacts of AV subnetworks from a CV owners' perspective. A novel formulation guarantees that CV trips starting inside AV subnetworks throughout the day aren't ignored-this means an alternative mode of transport, in this case, walking. This paper evaluates throughout the day when such situations would likely occur in a case study of the city of Delft, in the Netherlands, in two scenarios with AV subnetworks. The experiments revealed that walking is somehow inevitable when AVs reach 75% of the vehicle fleet-increasing travel costs up to 26.0% and 43.8%.

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With automated vehicles (AVs), reversible lanes could be a sustainable transportation solution once there is vehicle-to-infrastructure connectivity informingAVs about the lane configuration changes. This paper introduced the reversible lane network design problem (RL-NDP), formulated in mixed-integer non-linear mathematical programming-both the traffic assignment and the reversible lane decisions were embedded. The model was applied on an hourly basis in the case study of the city of Delft, the Netherlands. Reversible lanes are examined under no traffic equilibrium (former paths are maintained); user-equilibrium (UE) assignment (AVs decide their own paths); and system-optimum (SO) traffic assignment (AVs are forced to follow SO paths). We found out that reversible lanes reduce congested roads, total travel times, and delays up to 36%, 9%, and 22%, respectively. The SO scenario was revealed to be beneficial in reducing the total travel time and congested roads in peak hours, whereas UE is equally optimal in the remaining hours. A dual-scenario mixing SO and UE throughout the day reduced congested roads, total travel times, and delay up to 40%, 8%, and 19%, respectively, yet increased 1% in travel distance. The spatial analysis suggested a substantial lane variability in the suburbs, yet a strong presence of reversible lanes in the city center.

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