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].
Dynamic Rebalancing Dockless Bike-Sharing System based on Station Community Discovery
Authors: Jingjing Li, Qiang Wang, Wenqi Zhang, Donghai Shi, Zhiwei Qin
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We design a simulator built on real-world data from Di Di Chuxing to test the algorithm performance. The extensive experimental results demonstrate that our approach outperforms in terms of service level, proο¬t, and complexity compared with the state-of-the-art approach. |
| Researcher Affiliation | Collaboration | 1Beijing University of Posts and Telecommunications, Beijing, China 2Didi Chuxing, Beijing, China 3Di Di Research America, Mountain View, CA, USA |
| Pseudocode | Yes | Algorithm 1 Flow-graphed Community Discovering (FCD) |
| Open Source Code | No | The paper does not provide a link or explicit statement about the public availability of its source code. |
| Open Datasets | No | The experiments are conducted on "real-world data from Di Di Chuxing" which suggests internal data and no public access information (link, DOI, citation) is provided for this dataset. |
| Dataset Splits | No | The paper mentions using historical trip data for station definition and predicted demand, and divides a day into time-slots, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts) for model evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | Yes | We solve the STMIP model using the Python extension of the IBM ILOP CPLEX Optimization Studio version 12.9. |
| Experiment Setup | Yes | Parameters for the STMIP with FCD approach are summarized as follows: the number of stations N is determined to be 110. The capacity of trucks C v is set to be 10 for the experiments. The accumulation days of historical trips is set to D = 7. We divide a day into 48 time-slots, where t = 30 minutes. We assume that the average truck speed is 5 m/s, so the variable u is equal to 0.2 s/m. The price of per order and the moving cost of per bike are set to 1.5 RMB and 1 RMB, respectively. |