A Route-Affecting Region Based Approach for Feature Extraction in Transportation Route Planning

Fandel Lin, Hsun Ping Hsieh, Jie Yu Fang

研究成果: Conference contribution

摘要

Traffic deployment is highly correlated with the quality of life. Current research for passenger flow estimation in transportation route planning focuses on origin-destination matrices (OD) analysis; however, we claim that urban functions and geographical environments around passing area and stations should also be considered because they affect the demand of public transportation. For the route-based demand prediction task, we therefore define route-affecting region (RAR) to model the influential region of routes. Based on the proposed RAR, we further proposed route-based feature extraction approaches along with adopting several regression models to do high accurate inference. Given heterogeneous features and faced with the competitive and transfer effects of existing routes, our proposed RAR-based feature engineering methods are effective for handling and combining dynamic and static data which are high-correlated with passenger volumes. The experiments on bus-ticket data of Tainan and Chicago, with public transit network structures different from each other, show the adaptability and better performance of our proposed RAR-based approach compared to traditional OD-based feature extraction strategies.

原文English
主出版物標題Machine Learning and Knowledge Discovery in Databases
主出版物子標題Applied Data Science Track - European Conference, ECML PKDD 2020, Proceedings
編輯Yuxiao Dong, Dunja Mladenic, Craig Saunders
發行者Springer Science and Business Media Deutschland GmbH
頁面275-290
頁數16
ISBN(列印)9783030676667
DOIs
出版狀態Published - 2021
事件European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
持續時間: 2020 九月 142020 九月 18

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12460 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
城市Virtual, Online
期間20-09-1420-09-18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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