Improving Customer Satisfaction in Bike Sharing Systems through Dynamic Repositioning
Authors: Supriyo Ghosh, Jing Yu Koh, Patrick Jaillet
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 supriyog@ibm.com, jingyu koh@mymail.sutd.edu.sg, jaillet@mit.edu |
| 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). |