Research map / Graph learning / Graph algorithms and clustering
Community-Based Trip Sharing for Urban Commuting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| require same commuters on inbound and outbound routes (MIP-DD to MIP-DD-DIO) (ablation) | community-based ride sharing · Ann Arbor commuters | car usage reduction: 44% -> 20% | Hurt |
| require drivers to be the same every day (MIP-WD-DIO) (ablation) | community-based ride sharing · Ann Arbor commuters | car usage reduction: 20% -> 10% | Hurt |
| require passenger-driver pairings consistent every day (MIP-WD-WIO) (ablation) | community-based ride sharing · Ann Arbor commuters | car usage reduction: 10% -> 2% | Hurt |
| use global shareability graphs instead of clustering (ablation) | trip-sharing models · Ann Arbor commuters | car reduction and CPU time: +1% (MIP-WD-WIO) to +7% (MIP-DC) car reduction | Mixed |
| increase cluster diameter by 1 mile (ablation) | MIP-DC · outside city limits | car usage reduction: +6% | Helped |
| decrease cluster diameter by 1 mile (ablation) | MIP-DC · outside city limits | car usage reduction: -13% | Hurt |
| enlarge time window delta from 20 to 30 minutes (ablation) | MIP-WD-WIO, MIP-WD-DIO, MIP-DD-DIO · outside city limits | car usage reduction: additional 3%, 8%, 9% | Helped |
| add car balancing constraint (MIP-DC vs MIP-O) (ablation) | trip sharing · outside city limits | car count: about 2% (MIP-DC), 4% (MIP-DD) cost increase | Hurt |
Closest papers by meaning
- Algorithms for Trip-Vehicle Assignment in Ride-Sharing (AAAI 2018 · similarity 0.85)
- Optimizing Ride-Pooling Operations with Extended Pickup and Drop-Off Flexibility (AAAI 2026 · similarity 0.83)
- When Hashing Met Matching: Efficient Spatio-Temporal Search for Ridesharing (AAAI 2021 · similarity 0.82)
- Contract-based Inter-user Usage Coordination in Free-floating Car Sharing (AAAI 2021 · similarity 0.82)
- Pay Your Trip for Traffic Congestion: Dynamic Pricing in Traffic-Aware Road Networks (AAAI 2020 · similarity 0.82)
- Walkability Optimization: Formulations, Algorithms, and a Case Study of Toronto (AAAI 2023 · similarity 0.80)
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