TY - GEN
T1 - A Hybird Method with Gravity Model and Nearest-Neighbor Search for Trip Destination Prediction in New Metropolitan Areas
AU - Li, Man Ho
AU - Chen, Bo Yu
AU - Li, Cheng Te
N1 - Funding Information:
ACKNOWLEDGMENT This work is supported by the National Science and Technology Council (NSTC) of Taiwan under grants 110-2221-E-006-136-MY3, 111-2221-E-006-001, and 111-2634-F-002-022.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
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U2 - 10.1109/BigData55660.2022.10020439
DO - 10.1109/BigData55660.2022.10020439
M3 - Conference contribution
AN - SCOPUS:85147936705
T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
SP - 6553
EP - 6560
BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
A2 - Tsumoto, Shusaku
A2 - Ohsawa, Yukio
A2 - Chen, Lei
A2 - Van den Poel, Dirk
A2 - Hu, Xiaohua
A2 - Motomura, Yoichi
A2 - Takagi, Takuya
A2 - Wu, Lingfei
A2 - Xie, Ying
A2 - Abe, Akihiro
A2 - Raghavan, Vijay
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Big Data, Big Data 2022
Y2 - 17 December 2022 through 20 December 2022
ER -
