Price and Time Proposal Optimization for Ride-Hailing Services Based on Individual Utilities
Author:
Affiliation:
1. NTT Human Informatics Laboratories, NTT Corporation
2. School of Data Science, Yokohama City University
Publisher
Japanese Society for Artificial Intelligence
Subject
Artificial Intelligence,Software
Link
https://www.jstage.jst.go.jp/article/tjsai/38/1/38_38-1_C-M13/_pdf
Reference30 articles.
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2. [Alonso-Mora 17] Alonso-Mora, J., Samaranayake, S., Wallar, A., Frazzoli, E., and Rus, D.: On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment, Proceedings of the National Academy of Sciences, Vol. 114, No. 3, pp. 462–467 (2017)
3. [Asghari 16] Asghari, M., Deng, D., Shahabi, C., Demiryurek, U., and Li, Y.: Price-aware real-time ride-sharing at scale: An auctionbased approach, in SIGSPATIAL, pp. 1–10 (2016)
4. [Asghari 18] Asghari, M. and Shahabi, C.: ADAPT-pricing: a dynamic and predictive technique for pricing to maximize revenue in ridesharing platforms, in SIGSPATIAL, pp. 189–198 (2018)
5. [Bagnoli 06] Bagnoli, M. and Bergstrom, T.: Log-concave probability and its applications, in Rationality and Equilibrium, pp. 217–241, Springer (2006)
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