LAUSR

Bike sharing systems (BSSs) are becoming an important part of urban mobility in many cities given that they are sustainable and environmentally friendly. BSS operators spend great efforts to ensure bike and dock availability at each station. Measuring the quality of service (QoS) of each station and/or the entire system is critical for efficient system operations. The traditionally-known QoS measure reported in the literature is based on the proportion of problematic stations, which are defined as those with no bikes or docks available to users. This measure neither exposes the spatial dependencies between stations nor does it discriminate between stations in the BSS. Hence, we propose a novel QoS measure, namely the Optimal Occupancy, in which: 1) the temporal variations in arrival and pick up rates at individual stations are considered; 2) the discriminative property of the Optimal Occupancy is demonstrated using Analysis of Variance (ANOVA) procedures; and 3) geo-statistics, which have not been used before, are applied to explore the spatial Optimal Occupancy variations and model variograms for spatial prediction. This study uses an anonymized bike trip dataset from 34 stations in downtown San Francisco to compare the traditionally-known QoS measure and the proposed Optimal Occupancy measure. Results reveal that the Optimal Occupancy is beneficial, outperforms the traditionally-known QoS measure, and produces a better prediction of the QoS at nearby locations. In addition, the Optimal Occupancy can be used to predict candidate locations for the introduction of new stations in an existing BSS.