Summary of Taxi Dispatching Strategies with Compensations, by Holger Billhardt et al.
Taxi dispatching strategies with compensations
by Holger Billhardt, Alberto Fernández, Sascha Ossowski, Javier Palanca, Javier Bajo
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a new heuristic algorithm for taxi assignment to customers in big cities, considering factors such as passenger waiting times, driver costs and time, traffic density, CO2 emissions, and individual driver revenues. The approach uses domain-specific heuristics to tackle the dynamic problem of pairing passengers and taxis, taking into account the autonomous nature of taxi drivers who may not agree to assignments that are globally efficient but individually detrimental. The algorithm is tested against traditional assignment strategies, showing potential to reduce customer waiting times while being economically beneficial for individual drivers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make big cities more efficient by finding better ways to match taxis with customers. It’s like a game of matching, where the goal is to get people and taxis to the right places at the right time. The authors came up with a new way to do this that takes into account things like traffic congestion and the needs of individual drivers. They tested their idea against some other methods and found that it can really make a difference. |
