A Hybird Method with Gravity Model and Nearest-Neighbor Search for Trip Destination Prediction in New Metropolitan Areas

Man Ho Li, Bo Yu Chen, Cheng Te Li

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

As the urban population rises, so does the pressure on the city's transportation system. Most of the existing methods for passenger destination selection focus on processing the historical behaviors and travel trajectories of passengers. However, the existing methods face the generalization issue, the trained model cannot be applied to predict destinations in new metropolitan areas as the destination information is totally unseen and different from the training sets. To deal with the issue faced in IEEE BigData Cup 2022 - Trip Destination Prediction, in this work, we present a hybrid method. The main idea of our method is four-fold. The first is to implement the gravity model to capture human mobility between zones. The second contains two novel features to depict zones, including human traffic flow and feature class ratio. The third is to initialize the destinations in the new metropolitan area using the origin zones of multi-trip individuals. The last is to perform the nearest-neighbor search on both individuals and trips. The final destination prediction is produced by combining the gravity model and the nearest-neighbor search. Performance comparison reported by the competition leaderboard exhibits the superiority of our hybrid method, which also brings us to the fifth place in the competition.

原文English
主出版物標題Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
編輯Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
發行者Institute of Electrical and Electronics Engineers Inc.
頁面6553-6560
頁數8
ISBN(電子)9781665480451
DOIs
出版狀態Published - 2022
事件2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan
持續時間: 2022 12月 172022 12月 20

出版系列

名字Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference2022 IEEE International Conference on Big Data, Big Data 2022
國家/地區Japan
城市Osaka
期間22-12-1722-12-20

All Science Journal Classification (ASJC) codes

  • 建模與模擬
  • 電腦網路與通信
  • 資訊系統
  • 資訊系統與管理
  • 安全、風險、可靠性和品質
  • 控制和優化

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