Towards Alleviating Traffic Congestion: Optimal Route Planning for Massive-Scale Trips
Authors: Ke Li, Lisi Chen, Shuo Shang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments offer insight into the accuracy and efficiency of our proposed algorithms. |
| Researcher Affiliation | Academia | University of Electronic Science and Technology of China, China |
| Pseudocode | Yes | Algorithm 1 Greedy Algorithm; Algorithm 2 ϵ-Refining Algorithm |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | The paper mentions using 'San Joaquin County Road Network (TG)' and 'New York Road Network (NYN)' with footnotes linking to general web pages (e.g., 'https://www.cs.utah.edu/~lifeifei/Spatial Dataset.htm', 'https://publish.illinois.edu/dbwork/open-data/'). However, it does not provide a formal citation with author names and year, a specific repository name, or direct access to the dataset files, which is required for concrete access information. |
| Dataset Splits | No | The paper describes how sources and destinations are randomly selected and departure times are randomly generated, but it does not specify any explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | All algorithms are implemented in Java and tested on a Windows 10 platform with Intel(R) Core(TM) i5-9300H Processor (2.40 GHz) and 16GB memory. |
| Software Dependencies | No | The paper states 'All algorithms are implemented in Java' but does not specify a version number for Java or any other software dependencies with their version numbers. |
| Experiment Setup | Yes | The default parameter settings are listed in Table 1. |