1374220 - A macroscopic model for shared-micromobility traffic flow

Since its conception, shared automated transportation (SAT) has promised efficiency, accessibility, and safety benefits. SAT includes micromobility (e-scooters and e-bikes), automated shuttles, and shared-use service. In contrast to systems that only include privately owned autonomous vehicles, SAT systems guarantee a fair sharing of these advantages. Micromobility and automated vehicle (AV) technologies can complement one another and sustainably accelerate the adoption of automated transportation systems. For instance, automated shuttles can be connected with shared-bike services. Similar infrastructure, such as shared bus-bike lanes, has been implemented by cities worldwide to encourage public and active transportation. With the advent of automated shuttles (or similar services) and e-bikes and e-scooters, we expect that these transportation facilities will develop into SAT networks. The primary goal of this research is to analyze and model the macro-level properties of mixed shared-use traffic flow—the time-dependent spatial interactions among automated shuttles, bicycles, and e-scooters with the other vehicle classes (human and automated) in SAT. We develop a mathematical model for the mixed traffic flow that depicts the movement of automated and human-driven cars and maximizes mobility goals. A more sophisticated version of the cell transmission model, which divides a road link into several cells and propagates traffic based on density (vehicles per length) and flow (vehicles per unit time) conditions, is the embedded traffic flow model in the mathematical program. First, we generalize the model to incorporate several kinds of automated cars as well as micromobility modes (e.g., bicycles and e-scooters) (passenger, shuttles, and delivery trucks). The generalization will also consider how the heterogeneity in the size and speed of vehicle units impacts the relationship between density and flow. For each type of vehicle, class-specific densities, mean safe-keeping distances, and perception-reaction times will be used to characterize the essential attributes, density, and flow rate (e-scooter, bicycle, and automated shuttles).