Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions

Abstract

The design of future mobility solutions (autonomous vehicles, micromobility solutions, etc.) and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management policies. This requires tools to study such a coupling and co-design future mobility systems in terms of different objectives. This paper presents a framework to address such co-design problems. In particular, we leverage the recently developed mathematical theory of co-design to frame and solve the problem of designing and deploying an intermodal mobility system, whereby autonomous vehicles service travel demands jointly with micromobility solutions such as shared bikes and escooters, and public transit, in terms of fleets sizing, vehicle characteristics, and public transit service frequency. Our framework is modular and compositional, allowing one to describe the design problem as the interconnection of its individual components and to tackle it from a system-level perspective. Moreover, it only requires very general monotonicity assumptions and it naturally handles multiple objectives, delivering the rational solutions on the Pareto front and thus enabling policy makers to select a policy. To showcase our methodology, we present a realworld case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization tools to systematically assess the costs and benefits of interventions, and that such analytical techniques might inform policy-making in the future.

Analysis and Control of Autonomous Mobility-on-Demand Systems

“…The design of AV fleets impacts the performance of AMoD systems. Design problems include fleet sizing (39,40,41,42,43), fleet composition (e.g., capacity of vehicles and propulsion) (44), and autonomy (45,46). For instance, (39) solves two instances of fleet sizing: minimum fleet sizing and performance-driven fleet sizing.…”

“…Therefore, planning studies also focus on interactions between AMoD systems and existing infrastructure. Aspects of interest include allocation of parking space (48,49), optimal placement of charging stations, charge scheduling for electric AVs (50,51,52,53,54), contribution to congestion (55), joint operation with other modes of transportation (56,57), autonomous calibration (58), smart infrastructure (59), and achievable performance of the overall mobility system (45).…”

“…Such models are typically used along with graph-theoretic models, whereby demand is modeled via origin-destination pairs or via arrival and departure rates at each node. For instance, origin-destination pairs are specified by a pair of nodes ⟨o, d⟩ ∈ V × V (for time-invariant settings) or by a pair of nodes and a departure time ⟨o, d, τ ⟩ ∈ V × V × R≥0 (for time-varying settings) (45,73). • Stochastic demand models are ubiquitous in queueing-theoretic and continuum models.…”

“…Hence, fleet size results from the initial condition ∑ i vi(0). Third, fleet size can be imposed via hard constraints, especially in time-invariant network flow models, where the number of AVs can be estimated by multiplying the sum of customer and rebalancing flows with travel time (45). Finally, we remark two aspects.…”

“…First, each type of constraints can be readily generalized to mixed fleets, by imposing limitations on each vehicle class (95). Second, fleet sizing represents not only an operational constraint, but also a design parameter for planning studies (43,45).…”