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Kun Pang Kou

dblp:295/7224 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Mathematical optimization
scheduling
0.712023
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
0.512021
Near-Optimal Fixed-Route Scheduling for Crowdsourced Transit System · ICDE 2021
Smart cities and intelligent transportation
public transit scheduling
0.512021
Near-Optimal Fixed-Route Scheduling for Crowdsourced Transit System · ICDE 2021
Mathematical optimization
discrete optimization
0.512021
Near-Optimal Fixed-Route Scheduling for Crowdsourced Transit System · ICDE 2021
Mathematical optimization › scheduling
scheduling optimization
0.512021
Near-Optimal Fixed-Route Scheduling for Crowdsourced Transit System · ICDE 2021
Smart cities and intelligent transportation
public transit
0.212023
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
0.112021
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
YearPublicationVenuePosition
2023 Near-Optimal Scheduling for Crowdsourced Transit System With Skip-Stop Tactic
abstract
Efficient 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 System
abstract
Bus 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
ICDE4