Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Dispatching Through Pricing: Modeling Ride-Sharing and Designing Dynamic Prices
Authors: Mengjing Chen, Weiran Shen, Pingzhong Tang, Song Zuo
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct empirical evaluations of our solution through real data of a major ridesharing platform and show its advantages over fixed pricing schemes as well as several prevalent surgebased pricing schemes. |
| Researcher Affiliation | Collaboration | Mengjing Chen1 , Weiran Shen1 , Pingzhong Tang1 and Song Zuo2 1Institute for Interdisciplinary Information Sciences, Tsinghua University 2Google Research |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We perform our empirical analysis based on a public dataset from a major ride-sharing company.3 The dataset includes the orders in a city for three consecutive weeks (Jan. 1st, 2016 – Jan. 21st, 2016) and the total number of orders is more than 8.5 million. [...] 3The dataset is provided by Didi for an algorithm competition. |
| Dataset Splits | No | The paper describes how the dataset was processed and used for input (e.g., averages of statistics, estimations), but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper mentions being "limited by computing resource" but does not provide specific hardware details such as GPU/CPU models, memory, or specific cloud instance types used for experiments. |
| Software Dependencies | No | The paper mentions using "standard gradient descent algorithms" and the "fmincon function" (implying MATLAB), but it does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The length of each timestep is set to be 15 minutes and the number of steps in simulation is 96 (so 24 hours in total). For both FIXED and SURGE, we use the perminute price fitted from data as the base price, α = 0.5117, and allow the surge ratio β to be in [1.0, 5.0]. |