  Ride-pooling remains a promising emerging mode with a potential to contribute
towards urban sustainability and emission reductions. Recent studies revealed
complexity and diversity among travellers' ride-pooling aptitudes. So far,
ride-poling analyses assumed homogeneity and/or determinism of ride-pooling
travellers. This, as we demonstrate, leads to a false assessment of
ride-pooling system performance. We experiment with an actual NYC demand from
2016 and classify travellers into four groups of various ride-pooling
behaviours (value of time and penalty for sharing), as reported in the recent
SP study. We replicate their random behaviour to obtain meaningful
distributions.
  Unsurprisingly, results vary significantly from the deterministic benchmark:
expected mileage savings were lower, while the utility gains for travellers
were greater. Observing performance of heterogeneous travellers, we find that
those with a low value of time are most beneficial travellers in the pooling
system, while those with an average penalty for sharing benefit the most.
Notably, despite the highly variable travellers' behaviour, the confidence
intervals for the key performance indicators are reasonably narrow and
system-wide performance remains predictable.
  Such findings shed a new light on the expected performance of large scale
ride-pooling systems. We argue, that the policy recommendations shall be
revised to accommodate behavioural heterogeneity.
