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..
LC-RNN: A Deep Learning Model for Traffic Speed Prediction
Authors: Zhongjian Lv, Jiajie Xu, Kai Zheng, Hongzhi Yin, Pengpeng Zhao, Xiaofang Zhou
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real datasets demonstrate that our proposed LC-RNN outperforms seven well-known existing methods. |
| Researcher Affiliation | Collaboration | Zhongjian Lv1, Jiajie Xu1,2,3 , Kai Zheng4 , Hongzhi Yin5 , Pengpeng Zhao1, Xiaofang Zhou5,1 1 School of Computer Science and Technology, Soochow University, China 2 Provincial Key Laboratory for Computer Information Processing Technology, Soochow University 3 State Key Laboratory of Software Architecture (Neusoft Corporation), China 4 University of Electronic Science and Technology, China 5 The University of Queensland, Australia |
| Pseudocode | No | The paper describes the model and its components but 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 | No | The paper describes two datasets, Beijing and Shanghai, which were collected from trajectory data. However, it does not provide concrete access information such as a specific link, DOI, repository name, or formal citation for a publicly available or open dataset. |
| Dataset Splits | Yes | The data of the first 4 months were used as the training set, and the remaining 1 month as the test set. ... Among the data, the last 15 days are test set and the others are training set. ... We select 90% of the training data for training model, and the remaining 10% is chosen as the validation set with 3 early stopping. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'adam optimizer' but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | We train our network with the following hyper-parameters setting: mini-batch size (48), learning rate (0.0002) with adam optimizer, 1 A filters (32) and 2 A filters (16) in each LC layer. We select 90% of the training data for training model, and the remaining 10% is chosen as the validation set with 3 early stopping. |