Development and implementation of a game theory based ride-sharing technique

Clicks: 1
ID: 285979
2022
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #3,080 of 3,757 articles by views in Malay Journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Mobile internet technologies have sparked a multitude of opportunities for people to interact with one another and share resources. These have paved way to the rise of a phenomenon known as the “sharing economy” defined as sharing the resources through the internet. An application of sharing economy is ride-sharing where drivers offer their vehicles as a mode of public transportation to multiple passengers. In this work, we propose a Game Theory-based solution to address the stable matching among riders while minimizing their cost. Two stable matching techniques are proposed in this study, namely: First-Come, First-Served (FCFS) and Best Time Sharing (BT). FCFS discovers pairs based on earliest time of pair occurrences, while BT prioritizes selecting pairs with high proportion of shared distance between passengers to the overall distance of their trips. We evaluate our methods through extensive simulation from empirical taxi traces from Jakarta, Singapore, and New York. Results in terms of of post- stable matching cost savings, travel distance, successful matches, running trips, and spatio-temporal distribution have been evaluated to gauge the performance with respect to the no ridesharing condition. BT outperformed FCFS in terms of generating more pairs with compatible routes. Additionally, in the NY dataset with high amount of trip density, BT has efficiently reduced the number of trips present at a given time. On the other hand, FCFS has been more effective in pairing trips for the JK and SG datasets because of lower density due to limited amount of trajectories. The Game Theory (GT) pricing model 5 proved to generally be the most beneficial to the ride share’s cost savings, specifically leaning toward the passenger benefits. Analysis has shown that the stable matching algorithm reduced the overall number of trips while still adhering to the temporal frequency of trips within the dataset. Moreover, our developed Best Time Pairing and Game Theory Pricing methods served the most efficient based on passenger cost savings. Applying these stable matching algorithms will definitely benefit more users and will encourage more ridesharing instances.
Reference Key
persistent_1760657218_68f17f42d3e89 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Go, Kerwin D.
Journal Malay Journal
Year 2022
DOI
DOI not found
URL
https://animorepository.dlsu.edu.ph/etdb_ece/7
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.