Optimization-Based Comparison of Rebalanced Docked and Dockless Micromobility Systems

Shared micromobility systems are rapidly pervading urban environments. Usually, they are either dockless, in line with free-floating paradigms whereby vehicles can be left and picked up anywhere within the region of operation, or have docking stations with predefined parking slots. In this paper, we present an optimization-based framework to analyze and compare the advantages and disadvantages of these two different types of micromobility systems. We also include the possibility of rebalancing the system by the operator. First, we leverage graph theory to build a linear time-invariant network flow model of the two systems and use it to frame the time-optimal routing problem. Specifically, we formulate a linear program (LP) for the dockless system and a mixed-integer linear program (MILP) for the docked one whereby we jointly optimize the siting of the docking stations. Given their structure, both problems can be solved with off-the-shelf algorithms and global optimality guarantees. Second, we showcase our framework with a case study of Manhattan, NYC, whereby we quantitatively compare the performance achievable by the two micromobility paradigms. Our simulations suggest that increasing the number of stations of docked micromobility systems may decrease the average travel time up to a minimum aligned with the travel time achievable by dockless systems. Thereby, adding more stations does not significantly improve the system’s performance. Moreover, due to the slightly asymmetric travel demands, a mild rebalance of the system is enough to boost its performance.