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Ride-pooling systems, despite being an appealing urban mobility mode, stillstruggle to gain momentum. While we know the significance of critical mass inreaching system sustainability, less is known about the spatiotemporal patternsof system performance. Here, we use 1.5 million NYC taxi trips (sampled over asix-month period) and experiment to understand how well they could be servedwith pooled services. We use a utility-driven ride-pooling algorithm andobserve the pooling potential with six performance indicators: mileagereductions, travellers' utility gains, share of pooled rides, occupancy,detours, and potential fleet reduction. We report distributions and temporalprofiles of about 35 thousand experiments covering weekdays, weekends,evenings, mornings, and nights. We report complex spatial patterns, with gainsconcentrated in the core of the network and costs concentrated on theperipheries. The greatest potential shifts from the North in the morning to theCentral and South in the afternoon. Offering pooled rides at the fare 32% lowerthan private ride-hailing seems to be sufficient to attract pooling yetdynamically adjusting it to the demand level and spatial pattern may beefficient. The patterns observed in NYC were replicated on smaller datasets inChicago and Washington, DC, the occupancy grows with the demand with similartrends.

Spatiotemporal variability of ride-pooling potential -- half a year New York City experiment