Towards City-Scale Mobile Crowdsourcing: Task Recommendations under Trajectory Uncertainties
Authors: Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments have been performed over the instances generated using the real Singapore transportation network. The results show that we can find significantly better solutions than the deterministic formulation. |
| Researcher Affiliation | Collaboration | Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra School of Information Systems, Singapore Management University 80 Stamford Road, Singapore 178902 {cenchen.2012, sfcheng, hclau,archanm}@smu.edu.sg ... This research is supported by the Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the IDM Programme Office, Media Development Authority(MDA), and partially supported by the Xerox Research Centre India. |
| Pseudocode | No | The paper does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper states, 'The network used in this evaluation is based on the actual public transit network in Singapore (4,296 nodes and 10,129 edges)', but does not provide concrete access information (e.g., a URL, DOI, or specific citation to a public dataset) for this network or the generated synthetic instances. |
| Dataset Splits | No | The paper evaluates using '1000 routine route realizations are sampled' for performance comparison, but it does not specify explicit training, validation, or test dataset splits for model training or evaluation in a traditional machine learning sense. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper mentions using 'standard commercial solvers such as CPLEX' but does not specify any version numbers for CPLEX or any other software dependencies. |
| Experiment Setup | Yes | The results are categorized using different detour limits and (K, Nt, N) tuples, where 20 synthetic instances are randomly generated for each category... µt is in the range of (0, 2]. |