Fine-Grained Trajectory-based Travel Time Estimation for Multi-city
Scenarios Based on Deep Meta-Learning
- URL: http://arxiv.org/abs/2201.08017v1
- Date: Thu, 20 Jan 2022 06:35:51 GMT
- Title: Fine-Grained Trajectory-based Travel Time Estimation for Multi-city
Scenarios Based on Deep Meta-Learning
- Authors: Chenxing Wang, Fang Zhao, Haichao Zhang, Haiyong Luo, Yanjun Qin, and
Yuchen Fang
- Abstract summary: Travel Time Estimation (TTE) is indispensable in intelligent transportation system (ITS)
It is significant to achieve the fine-grained Trajectory-based Travel Time Estimation (TTTE) for multi-city scenarios.
We propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time.
- Score: 18.786481521834762
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Travel Time Estimation (TTE) is indispensable in intelligent transportation
system (ITS). It is significant to achieve the fine-grained Trajectory-based
Travel Time Estimation (TTTE) for multi-city scenarios, namely to accurately
estimate travel time of the given trajectory for multiple city scenarios.
However, it faces great challenges due to complex factors including dynamic
temporal dependencies and fine-grained spatial dependencies. To tackle these
challenges, we propose a meta learning based framework, MetaTTE, to
continuously provide accurate travel time estimation over time by leveraging
well-designed deep neural network model called DED, which consists of Data
preprocessing module and Encoder-Decoder network module. By introducing meta
learning techniques, the generalization ability of MetaTTE is enhanced using
small amount of examples, which opens up new opportunities to increase the
potential of achieving consistent performance on TTTE when traffic conditions
and road networks change over time in the future. The DED model adopts an
encoder-decoder network to capture fine-grained spatial and temporal
representations. Extensive experiments on two real-world datasets are conducted
to confirm that our MetaTTE outperforms six state-of-art baselines, and improve
29.35% and 25.93% accuracy than the best baseline on Chengdu and Porto
datasets, respectively.
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