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..
Community-Based Trip Sharing for Urban Commuting
Authors: Mohd. Hafiz Hasan, Pascal Van Hentenryck, Ceren Budak, Jiayu Chen, Chhavi Chaudhry
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that community-based trip sharing reduces daily car usage by up to 44%, thus producing significant environmental and traffic benefits and reducing parking pressure. |
| Researcher Affiliation | Academia | University of Michigan Ann Arbor, Michigan 48109 |
| Pseudocode | Yes | Algorithm 1 Shareability Graph for Inbound Trips" and "Algorithm 2 All Feasible Inbound Trip Sharing Routes |
| Open Source Code | No | The paper does not provide any specific links or statements about releasing the source code for the described methodology. |
| Open Datasets | No | The dataset contains access information of 15 parking structures located in downtown Ann Arbor. Each entry contains the ID, access time, and direction (in/out) of each customer throughout April 2017. This information was joined with the home address of every customer to reconstruct their daily trips. |
| Dataset Splits | No | The paper describes using a dataset of 15,000 commuters for its study but does not provide explicit training, validation, and test dataset splits with percentages or sample counts for reproducibility. |
| Hardware Specification | Yes | All models were executed on a high-performance computing cluster with 8 cores of a 2.5 GHz Intel Xeon E5-2680v3 processor, 64 GB of RAM, and a time limit of 120 hours. |
| Software Dependencies | Yes | The clustering stage converts all GPS coordinates to local Cartesian coordinates before applying MATLAB 2016b s clusterdata function. The rest of the algorithm was implemented in C++, using GUROBI 6.5.2 for solving the MIPs. |
| Experiment Setup | Yes | The values σlimit = 2 miles and Δ = 20 mins were used for most experiments. We also include results for σlimit = 2 1 miles and Δ = 20 10 mins to demonstrate the algorithm s sensitivity to these parameters. All models were executed on a high-performance computing cluster with 8 cores of a 2.5 GHz Intel Xeon E5-2680v3 processor, 64 GB of RAM, and a time limit of 120 hours. |