Ride-hailing and ride-pooling: State of the art, future research directions, and the role of ...
summary
Simulation studies claim automated ride-hailing raises fleet utilization even with 10% empty trips, and Munich micro-simulations show pooling bundles trips on main arterials while shifting extra mileage onto secondary streets. Results use an agent-based framework coupled to SUMO microscopic simulations to model vehicle-customer assignment, proactive repositioning and boarding processes. Scaling demand and fleet size amplifies pooling benefits, reservation features increase average occupancy, and scenarios quantify fleet-size tradeoffs and charging/relocation impacts for electric deployments.
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chapters (12)
- 0:00Mobility research at TUM
- 5:01Ride-hailing and pooling state of the art
- 10:30Ride-pooling operational challenges
- 16:23Fleet management strategies
- 21:49Carsharing demand analysis
- 27:05Autonomous shuttles impact
- 33:15Ride-pooling efficiency models
- 38:07Reservation and relocation strategies
- 43:15Dynamic stop allocation
- 47:04Boarding micro-simulation
- 51:05Scaling mobility platforms
- 55:06Future research and ethics