Demo Data from VLDB 2026 / reviewers in the wild / expert
Wanyi Xie
dblp:293/3393
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2024
0009-0003-1464-7171ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 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% | |
| Artificial intelligence
1 paper |
Optimization for machine learning · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Smart cities and intelligent transportation › ride-hailing
order dispatching |
0.8 | 1 | 2024 | Longer Pick-Up for Less Pay: Towards Discount-Based Mobility Services · IEEE Trans. Knowl. Data Eng. 2024 |
Machine learning › Optimization for machine learning
optimization |
0.2 | 1 | 2024 | Longer Pick-Up for Less Pay: Towards Discount-Based Mobility Services · IEEE Trans. Knowl. Data Eng. 2024 |
Methods — techniques the papers use, named apart from their topics
customized optimization methods · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Longer Pick-Up for Less Pay: Towards Discount-Based Mobility ServicesabstractWith the rapid development of mobile Internet technology, on-demand car-hailing services have become essential for people's daily commuting. Order dispatch is a critical problem in on-demand car-hailing services. However, in most existing works, the service provider is asked to set a unified pick-up distance to prevent long waiting time for requesters. Indeed, different requesters have different tolerance for pick-up distance, and some requesters may accept longer pick-ups if offered discounts for payment. Regarding this fact, we formulate discount-based order dispatch as a coupling of two subproblems, discount determination and order dispatch, aiming to dispatch more orders and thereby more platform profits. We propose customized methods to solve the problems for shared and non-shared mobility services, respectively. We also conduct extensive experiments to evaluate the effectiveness and efficiency of our proposed methods on a real dataset, which shows that our methods can achieve 170% improvements in non-shard services and 43% improvements in ridesharing services on average in terms of attained profit compared to the widely adopted baselines. Wanyi Xie, Zhijia Chen, Chen Zhang 0013, Libin Zheng 0001, Peng Cheng 0003, Jian Yin 0001, Xuemin Lin 0001 |
IEEE Trans. Knowl. Data Eng. | 1 |