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 [1].
Robust Repositioning to Counter Unpredictable Demand in Bike Sharing Systems
Authors: Supriyo Ghosh, Michael Trick, Pradeep Varakantham
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive computational results from a simulation built on real world data set of bike sharing company demonstrate that our approach can significantly reduce the expected lost demand over the existing benchmark approaches.Experimental results on multiple synthetic data sets and a real world data set demonstrate that our approach significantly reduces the expected lost demand over the existing benchmark approaches and is robust to the uncertainty in demand. |
| Researcher Affiliation | Academia | Supriyo Ghosh School of Info. Systems Singapore Management Univ. EMAIL Michael Trick Tepper School of Business Carnegie Mellon University EMAIL Pradeep Varakantham School of Info. Systems Singapore Management Univ. EMAIL |
| Pseudocode | Yes | Algorithm 1: solve DRRPU(drrpu, t, d#) |
| Open Source Code | No | The paper does not provide a direct link to the source code for the methodology described, nor does it explicitly state that the code is being released or is available in supplementary materials for its own work. |
| Open Datasets | Yes | Data is taken from Hubway bike sharing company of Boston [http://hubwaydatachallenge.org/trip-history-data] |
| Dataset Splits | No | The paper describes generation of 'testing demand scenarios' but does not specify explicit train/validation/test splits from a single dataset in percentages or counts typically found in machine learning contexts. There is no mention of a 'validation' set used for tuning. |
| Hardware Specification | Yes | All the linear optimisation models were solved using IBM ILOG CPLEX Optimisation Studio V12.5 on a 3.2 GHz Intel Core i5 machine with 8GB DDR3 RAM |
| Software Dependencies | Yes | All the linear optimisation models were solved using IBM ILOG CPLEX Optimisation Studio V12.5 |
| Experiment Setup | Yes | We consider a planning horizon of 6 hours in the morning peak (6AM-12PM) and the duration of each decision epoch is 30 minutes.For both the data sets, we compute the lower bound on the arrival demand as (1) of the mean demand and upper bound as (1+ ) of the mean demand. To compute the bounds on arrival demand for each station and for each origin destination pair we set as 100%, while for the bounds on the system wide demand at each time step, is set as 10%.As the planning period for one decision epoch is 30 minutes, we set a time threshold of 3 minutes as a convergence criterion for our scenario generation approach. |