Hyper-pool: pooling private trips into high-occupancy transit-like attractive shared rides
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arXiv
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| Format: | Preprint |
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2022
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| author | Kucharski, Rafal Cats, Oded |
| author_facet | Kucharski, Rafal Cats, Oded |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_05940 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Hyper-pool: pooling private trips into high-occupancy transit-like attractive shared rides Kucharski, Rafal Cats, Oded Physics and Society 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. |
| title | Hyper-pool: pooling private trips into high-occupancy transit-like attractive shared rides |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2206.05940 |