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..
Improving Customer Satisfaction in Bike Sharing Systems through Dynamic Repositioning
Authors: Supriyo Ghosh, Jing Yu Koh, Patrick Jaillet
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results from a simulation model built on a real-world bike sharing data set demonstrate that our approach is not only robust to uncertainties in customer demand, but also outperforms the existing state-of-the-art repositioning approaches in terms of reducing the expected lost demand. |
| Researcher Affiliation | Collaboration | 1IBM Research Center, Singapore 018983 2Singapore University of Technology and Design, Singapore 3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology EMAIL, jingyu EMAIL, EMAIL |
| Pseudocode | Yes | The optimization model for generating the repositioning solution is represented compactly in Table (1). |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described. |
| Open Datasets | Yes | We evaluate the performance of our approach... on a real-world data set from Hubway. [...] Data is taken from Hubway bike sharing company of Boston [http://hubwaydatachallenge.org/trip-history-data] |
| Dataset Splits | No | From 60 demand scenarios, 20 scenarios are used for training purposes and other 40 scenarios are used for testing. |
| Hardware Specification | Yes | All the linear optimization models are solved using IBM ILOG CPLEX Optimization Studio V12.7 on a 2.1 GHz Intel Xeon E5 machine with 16 cores and 32GB RAM. |
| Software Dependencies | Yes | All the linear optimization models are solved using IBM ILOG CPLEX Optimization Studio V12.7 on a 2.1 GHz Intel Xeon E5 machine with 16 cores and 32GB RAM. |
| Experiment Setup | Yes | We consider 6 hours of planning horizon in the morning peak (6AM-12PM) which is divided into 12 decision epochs, each having a duration of 30 minutes. [...] Table 2(a) summarizes the lost demand statistics at the pickup and drop-off time for different approaches with 3 episodes (i.e., a vehicle is allowed to visit maximum 3 stations in each of the decision epochs). |