Ride-pooling service assessment with rational, heterogeneous, non-deterministic travellers

TL;DR

This study evaluates ride-pooling performance considering diverse, non-deterministic traveller behaviors, revealing significant differences from traditional models and emphasizing the importance of accounting for heterogeneity in policy design.

Contribution

It introduces a novel analysis of heterogeneous, stochastic traveller behaviors in ride-pooling, challenging previous assumptions of homogeneity and determinism.

Findings

Heterogeneous traveller behaviors lead to lower mileage savings but higher utility gains.

Low value-of-time travellers benefit most from ride-pooling.

System performance remains predictable despite behavioural variability.

Abstract

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.