Optimal Escape Interdiction on Transportation Networks
Authors: Youzhi Zhang, Bo An, Long Tran-Thanh, Zhen Wang, Jiarui Gan, Nicholas R. Jennings
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation shows that our approach significantly outperforms baselines in solution quality and scales up to realistic-sized transportation networks with hundreds of intersections. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 2Department of Electronics and Computer Science, University of Southampton, UK 3School of Cyberspace, Hangzhou Dianzi University, China 4Department of Computer Science, University of Oxford, UK 5Departments of Computing and Electrical and Electronic Engineering, Imperial College, UK |
| Pseudocode | Yes | Algorithm 1: EIGS; Algorithm 2: better DO (y); Algorithm 3: better AO (x) |
| Open Source Code | No | The paper does not provide any statement about releasing code or a link to a code repository. |
| Open Datasets | No | The paper uses the 'Grid model with Random Edges (GRE) to generate urban road network topologies [Peng et al., 2014]'. While it cites a paper for the model used to generate the data, it does not state that the actual dataset used in their experiments is publicly available or provide a link/citation for that specific data. |
| Dataset Splits | No | The paper discusses experimental setup and evaluation but does not specify any training, validation, or test splits. |
| Hardware Specification | Yes | All computations are performed on a machine with a 3.20GHz quad core CPU and 16.00GB memory. |
| Software Dependencies | Yes | All LPs and MILPs are solved with CPLEX (version 12.6). |
| Experiment Setup | No | The paper describes parameters for generating network topologies and traffic models (e.g., L W = 8 8 nodes, (p, q) = (0.4, 0.2), |D| = 10 exit nodes, m = 4 resources, Ce = 6, Te in [1,10], traffic demand in [0,6], turning preference matrix R uniformly generated). These are environmental parameters, not specific hyperparameter values or system-level training settings for their proposed algorithms (e.g., learning rate, batch size, or iterative convergence criteria). |