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This paper studies congestion-aware route-planning policies for intermodal Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility jointly with public transit under mixed traffic conditions (consisting of AMoD and private vehicles). First, we devise a network flow model to jointly optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. Second, we capture the effect of exogenous traffic stemming from private vehicles adapting to the AMoD flows in a user-centric fashion by leveraging a sequential approach. Since our results are in terms of link flows, we then provide algorithms to retrieve the explicit recommended routes to users. Finally, we showcase our framework with two case-studies considering the transportation sub-networks in Eastern Massachusetts and New York City, respectively. Our results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows. However, blending AMoD with public transit, walking and micromobility options can significantly improve the overall system performance by leveraging the high-throughput of public transit combined with the flexibility of walking and micromobility. [ABSTRACT FROM AUTHOR]

We study the problem of eco-routing for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. We propose an algorithm which can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. In order to show the effectiveness of our method in practice, we use a HERE Maps API to apply our algorithms based on traffic data in the city of Boston with more than 110,000 links. Moreover, we validate the performance of our eco-routing algorithm using speed profiles collected from a traffic simulator (SUMO) as input to a high-fidelity energy model to calculate energy consumption costs. Our results show significant energy savings (around 12%) for PHEVs with a near real-time execution time for the algorithm. [ABSTRACT FROM AUTHOR]

The complexity of plug-in hybrid-electric vehicles (PHEVs) motivates the simultaneous integration of component design and supervisory control strategy decisions. Methods from combined optimal design and control (co-design) are generally used to manage such integrated system design decisions. Although several studies have investigated the PHEV powertrain co-design problem, the impact of key vehicle performance criteria such as 0–60 mph acceleration time and all-electric-range (AER) has rarely been explicitly included in such system co-design problems. This is problematic as these vehicle performance criteria strongly affect component sizing and control strategy in a way that a non-performance-based co-design solution could become sub-optimal. Therefore, this study addresses this issue by formulating and solving a PHEV powertrain co-design problem that explicitly includes vehicle-level performance constraints. In particular, a three-phase, PHEV powertrain co-design problem is solved to simultaneously identify the optimal supervisory control strategies during the acceleration performance and standard duty cycle phases, along with the optimal component designs spanning all phases (acceleration performance, standard duty cycle, and AER performance) such that the vehicle powertrain cost is minimized. A relatively new, balanced co-design approach known as multidisciplinary dynamic system design optimization (MDSDO) is used to solve the problem. The optimal design and standard duty cycle supervisory control trajectories are compared to the solution of a non-performance-based co-design problem. The results indicate that the formal inclusion of vehicle performance criteria in a co-design problem significantly affects component design and supervisory control strategies. [ABSTRACT FROM AUTHOR]