Abstract
In this paper, we study the operations of a one-way station-based carsharing system implementing a complete journey reservation policy. We consider the percentage of served demand as a primary performance measure and analyze the effect of several dynamic staff-based relocation policies. Specifically, we introduce a new proactive relocation policy based on Markov chain dynamics that utilizes reservation information to better predict the future states of the stations. This policy is compared to a state-of-the art staff-based relocation policy and a centralistic relocation model assuming full knowledge of the demand. Numerical results from a real-world implementation and a simulation analysis demonstrate the positive impact of dynamic relocations and highlight the improvement in performance obtained with the proposed proactive relocation policy.
| Original language | English |
|---|---|
| Pages (from-to) | 82-104 |
| Number of pages | 23 |
| Journal | Transportation Research Part B: Methodological |
| Volume | 130 |
| DOIs | |
| State | Published - Dec 2019 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 11 Sustainable Cities and Communities
Keywords
- Carsharing
- Markov chain
- Operations
- Prediction
- Simulation
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Transportation
Fingerprint
Dive into the research topics of 'Dynamic prediction-based relocation policies in one-way station-based carsharing systems with complete journey reservations'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver