Community-Based Trip Sharing for Urban Commuting
Authors: Mohd. Hafiz Hasan, Pascal Van Hentenryck, Ceren Budak, Jiayu Chen, Chhavi Chaudhry
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |