Demo Data from EDBT 2026 / reviewers in the wild / expert
Kun Pang Kou
dblp:295/7224
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2 · 2 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.
| Theoretical computer science
2 papers |
Mathematical optimization · 92% Algorithmic game theory and mechanism design · 8% | |
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Smart cities and intelligent transportation · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Mathematical optimization
scheduling |
0.7 | 1 | 2023 | Near-Optimal Scheduling for Crowdsourced Transit System With Skip-Stop Tactic · IEEE Trans. Knowl. Data Eng. 2023 |
Smart cities and intelligent transportation › public transit scheduling
bus scheduling |
0.5 | 1 | 2021 | Near-Optimal Fixed-Route Scheduling for Crowdsourced Transit System · ICDE 2021 |
Smart cities and intelligent transportation
public transit scheduling |
0.5 | 1 | 2021 | Near-Optimal Fixed-Route Scheduling for Crowdsourced Transit System · ICDE 2021 |
Mathematical optimization
discrete optimization |
0.5 | 1 | 2021 | Near-Optimal Fixed-Route Scheduling for Crowdsourced Transit System · ICDE 2021 |
Mathematical optimization › scheduling
scheduling optimization |
0.5 | 1 | 2021 | Near-Optimal Fixed-Route Scheduling for Crowdsourced Transit System · ICDE 2021 |
Smart cities and intelligent transportation
public transit |
0.2 | 1 | 2023 | Near-Optimal Scheduling for Crowdsourced Transit System With Skip-Stop Tactic · IEEE Trans. Knowl. Data Eng. 2023 |
Algorithmic game theory and mechanism design › mechanism design
crowdsourcing |
0.1 | 1 | 2021 | Near-Optimal Fixed-Route Scheduling for Crowdsourced Transit System · ICDE 2021 |
Methods — techniques the papers use, named apart from their topics
dynamic programming · 2.3k-clustering · 1.3approximation algorithm · 1.3k-clustering reduction · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Near-Optimal Scheduling for Crowdsourced Transit System With Skip-Stop TacticabstractEfficient bus scheduling is a crucial component for the improvement of public transit services. Without systematic optimization and efficient shift arrangement, the bus scheduling system may suffer from poor vehicle loading rate or crowd onboard, resulting in wasted energy and passenger dissatisfaction. In this paper, we consider a crowdsourced bus service system (on a fixed route) that receives user requests as input and computes the scheduling of buses with flexible departure time and skip-stop to minimize the travel time of users. We first show that the general problem of computing the optimal scheduling is NP-hard. On the other hand, for the case when skip-stop is not adopted, we propose the Optimized Departure Time (ODT) algorithm that computes optimal scheduling. Our algorithm is built on an innovative reduction of the problem to some variants of the k-clustering problem and an efficient application of dynamic programming. On top of ODT, we further improve the effectiveness of the solution by utilizing the power of skip-stop tactic, named ODTS. Our experimental results demonstrate that ODT and ODTS dramatically outperform existing algorithms for the bus scheduling problem in terms of effectiveness and efficiency. Moreover, the solutions given by ODTS are very close to the optimum. Hanlin Li 0002, Xiaowei Wu 0001, Leong Hou U, Kun Pang Kou |
IEEE Trans. Knowl. Data Eng. | 4 |
| 2021 | Near-Optimal Fixed-Route Scheduling for Crowdsourced Transit SystemabstractBus scheduling is a crucial component for public transport service. Inefficient shift arrangement leads to poor vehicle loading rate or crowd inboard. In this paper, we consider a crowdsourced bus service system (on a fixed route) that receives user requests as input and computes a scheduling of buses with flexible departure time and skip-stop to minimize the travel time of users. We first show that the general problem of computing the optimal scheduling is NP-hard. Then we propose the Optimized Departure Time (ODT) algorithm that computes an optimal scheduling, which is built on an innovative reduction of the problem to a variant of the k-clustering problem, and an efficient application of dynamic programming. On top of ODT, we propose the Optimized Departure Time with Skip-Stop (ODTS) algorithm, which further improves the effectiveness of the solution by utilizing skip-stop. Our experimental results demonstrate that ODT and ODTS dramatically improve the baseline solution and outperform existing algorithms for the bus scheduling problem, which are very close to the optimum. Hanlin Li 0002, Xiaowei Wu 0001, Leong Hou U, Kun Pang Kou |
ICDE | 4 |