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Jinjia Huang

dblp:153/0070 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Smart cities and intelligent transportation
bike sharing systems
0.512021
Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice Restriction · AAAI 2021
Algorithmic game theory and mechanism design
resource allocation
0.512021
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
YearPublicationVenuePosition
2021 Bike-Repositioning Using Volunteers: Crowd Sourcing with Choice Restriction
abstract
Motivated 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
AAAI1