Balancing Efficiency and Fairness in On-Demand Ridesourcing
Authors: Nixie S. Lesmana, Xuan Zhang, Xiaohui Bei
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the effectiveness of the algorithms through extensive experiments on real-world datasets. |
| Researcher Affiliation | Academia | Department of Mathematical Sciences, Nanyang Technological University, Singapore 637371 Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61801-3080 |
| Pseudocode | Yes | Algorithm 1: REASSIGN (I, Mold, f) Input :Instance I = {G(V, R, E), {wvr}{v,r} E, {hv}v V}, current assignment Mold, fairness threshold f Fopt. Output :A new vehicle-request assignment Mnew 1 Compute a fair assignment Mfair 2 Set Mnew = Mold 3 while there exists v V such that hv + wv,Mnew(v) < f do 4 r Mnew(v) 5 if Mfair(v) is not matched in Mnew then 6 Mnew(v) Mfair(v) 7 else 8 while there exists v V such that Mnew(v ) = Mfair(v) do 9 Mnew(v) Mfair(v) 10 Mnew(v) = Mfair(v) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a link or explicit statement of release) to the source code for the methodology described. |
| Open Datasets | Yes | We use the publicly available dataset of taxi trips in New York City [14], which contains for each day the time and location of all of the pickups and drop-offs executed by each of the active taxis. [14] Dan Donovan, Brian; Work. New york city taxi trip data (2010-2013), 2016. |
| Dataset Splits | No | The paper describes how data is used in single-batch and multi-period settings but does not specify explicit training/validation/test dataset splits (e.g., percentages or counts) in the typical machine learning sense. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions a programming language (Python) implicitly by reference to typical ML environments but does not list any specific software libraries or solvers with version numbers. |
| Experiment Setup | Yes | We set the maximum waiting time constraint Ω= 210s, constant c = 1, and vehicle capacity χ = 4. We set the maximum waiting time constraint Ω= 150s and maximum delay time constraint Γ = 300s. |