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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Parallel Subtrajectory Alignment over Massive-Scale Trajectory Data
Authors: Lisi Chen, Shuo Shang, Shanshan Feng, Panos Kalnis
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with large trajectory datasets are conducted for evaluating the performance of our proposal. |
| Researcher Affiliation | Academia | 1University of Electronic Science and Technology of China 2Harbin Institute of Technology, Shenzhen, China 3King Abdullah University of Science and Technology, Saudi Arabia |
| Pseudocode | Yes | Algorithm 1: Traj Candidates Gen |
| Open Source Code | No | The paper does not include an explicit statement about releasing its source code or a direct link to a repository for the methodology described. |
| Open Datasets | Yes | For BN, we use taxi trajectory data collected by the T-drive project [Yuan et al., 2013]... For NYN, we use taxi trip data from New York3 |
| Dataset Splits | No | The paper mentions that |
| Hardware Specification | Yes | All algorithms are implemented in Java and run on a server with two Intel Xeon Processors Gold 5120 and 64GB RAM. |
| Software Dependencies | No | The paper only mentions |
| Experiment Setup | Yes | Unless stated otherwise, experiment results are averaged over 100 independent trials using different vs, ve, and O for efficacy and efficiency evaluations. The parameter settings are listed in Table 1. |