PlanRanker: Towards Personalized Ranking of Train Transfer Plans-Reference-Cited by-同舟云学术

PlanRanker: Towards Personalized Ranking of Train Transfer Plans

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Xu Jia¹^ORCID,Tao Wanjie²^ORCID,Chen Zulong²^ORCID,Huang Jin²^ORCID,Liu Huihui²^ORCID,Wen Hong²^ORCID,Ni Shenghua²^ORCID,Dai Qun³^ORCID,Gu Yu⁴^ORCID

Affiliation:

1. Guangxi University, Nanning, China

2. Alibaba Group, Hangzhou, China

3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

4. Northeastern University, Shenyang, China

Funder

National Natural Science Foundation of China

Guangxi Natural Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599887

Reference32 articles.

1. N. Borole D. Rout N. Goel P. Vedagiri and T. V. Mathew. 2013. Multimodal Public Transit Trip Planner with Real-time Transit Data. Procedia - Social and Behavioral Sciences (2013) 775--784. N. Borole D. Rout N. Goel P. Vedagiri and T. V. Mathew. 2013. Multimodal Public Transit Trip Planner with Real-time Transit Data. Procedia - Social and Behavioral Sciences (2013) 775--784.

2. Paolo Campigotto , Christian Rudloff , Maximilian Leodolter , and Dietmar Bauer . 2016. Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm . IEEE Transactions on Intelligent Transportation Systems ( 2016 ), 92--102. Paolo Campigotto, Christian Rudloff, Maximilian Leodolter, and Dietmar Bauer. 2016. Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm. IEEE Transactions on Intelligent Transportation Systems (2016), 92--102.

3. Learning Points and Routes to Recommend Trajectories

4. Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation

5. Discovering popular routes from trajectories