Neural approximate dynamic programming for on-demand ride-pooling
On-demand ride-pooling (e.g., UberPool, LyftLine, GrabShare) has recently become popular because of its ability to lower costs for passengers while simultaneously increasing revenue for drivers and aggregation companies (e.g., Uber). Unlike in Taxi on Demand (ToD) services – where a vehicle is assig...
Saved in:
Main Authors: | SHAH, Sanket, LOWALEKAR, Meghna, VARAKANTHAM, Pradeep |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2020
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/5992 https://ink.library.smu.edu.sg/context/sis_research/article/6995/viewcontent/5388_Article_Text_8613_1_10_20200511.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Joint pricing and matching for city-scale ride pooling
by: SHAH, Sanket, et al.
Published: (2022) -
Hierarchical value decomposition for effective on-demand ride pooling
by: JIANG, Hao, et al.
Published: (2022) -
Improved Parallel Approximation of a Class of Integer Programming Problems
by: Alon, N., et al.
Published: (2014) -
Approximating the Performance of a "Last Mile" Transportation System
by: Hai WANG,, et al.
Published: (2016) -
Successive linear approximation solution of infinite-horizon dynamic stochastic programs
by: Birge, J.R., et al.
Published: (2014)