Demo Data from VLDB 2026 / reviewers in the wild / expert
Jinjia Huang
dblp:153/0070
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2021
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Smart cities and intelligent transportation · 100% | |
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Smart cities and intelligent transportation
bike sharing systems |
0.5 | 1 | 2021 | Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice Restriction · AAAI 2021 |
Algorithmic game theory and mechanism design
resource allocation |
0.5 | 1 | 2021 | Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice Restriction · AAAI 2021 |
Methods — techniques the papers use, named apart from their topics
transshipment network design · 1.0threshold-based policy · 1.0simulation · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice RestrictionabstractMotivated by the Bike Angels Program in New York's Citi Bike and Boston's Blue Bikes, we study the use of (registered) volunteers to re-position empty bikes for riders in a bike sharing system. We propose a method that can be used to deploy the volunteers in the system, based on the real time distribution of the bikes in different stations. To account for (random) route demand in the network, we solve a related transshipment network design model and construct a sparse structure to restrict the re-balancing activities of the volunteers (concentrating re-balancing activities on essential routes). We also develop a comprehensive simulation model using a threshold-based policy to deploy the volunteers in real time, to test the effect of choice restriction on volunteers (suitably deployed) to re-position bikes. We use the Hubway system in Boston (with 60 stations) to demonstrate that using a sparse structure to concentrate the re-balancing activities of the volunteers, instead of allowing all admissible flows in the system (as in current practice), can reduce the number of re-balancing moves by a huge amount, losing only a small proportion of demand satisfied. Jinjia Huang, Mabel C. Chou, Chung-Piaw Teo |
AAAI | 1 |