Hyper-pool: pooling private trips into high-occupancy transit-like attractive shared rides

TL;DR

Hyper-pool introduces a novel ride-pooling algorithm that aggregates trips into high-occupancy, public transport-like shared rides, significantly increasing pooling efficiency and attractiveness.

Contribution

It presents a new demand-side aggregation approach that enhances ride pooling by generating stop-to-stop and hyper-pooled rides, improving occupancy and operational efficiency.

Findings

Pooled over 220 travelers into 40 rides in Amsterdam

Achieved an average occupancy of 5.8 passengers per vehicle

Demonstrated applicability to real-size demand datasets

Abstract

We propose Hyper-pool, an analytical, offline, utility-driven ride-pooling algorithm to aggregate individual trip requests into attractive shared rides of high-occupancy. We depart from our ride-pooling ExMAS algorithm where single rides are pooled into attractive door-to-door rides and propose two novel demand-side algorithms for further aggregating individual demand towards more compact pooling. First, we generate stop-to-stop rides, with a single pick up and drop off points optimal for all the travellers. Second, we bundle such rides again, resulting with hyper-pooled rides compact enough to resemble public transport operations. We propose a bottom-up framework where the pooling degree of identified rides is gradually increased, thereby ensuring attractiveness at subsequent aggregation levels. Our Hyper-pool method outputs the set of attractive pooled rides per service variant for a given travel demand. The algorithms are publicly available and reproducible. It is applicable for real-size demand datasets and opens new opportunities for exploiting the limits of ride-pooling potential. In our Amsterdam case-study we managed to pool over 220 travellers into 40 hyper-pooled rides of average occupancy 5.8 pax/veh.