Estimating the travel time for a given path is a fundamental problem in many urban transportation systems. However, prior works fail to well capture moving behaviors embedded in paths and thus do not estimate the travel time accurately. To fill in this gap, in this work, we propose a novel neural network framework, namely Deep Image-based Spatio-Temporal network (DeepIST), for travel time estimation of a given path. The novelty of DeepIST lies in the following aspects:1) we propose to plot a path as a sequence of -generalized images"which include sub-paths along with additional information, such as traffic conditions, road network and traffic signals, in order to harness the power of convolutional neural network model (CNN)on image processing; 2) we design a novel two-dimensional CNN, namely PathCNN, to extract spatial patterns for lines in images by regularization and adopting multiple pooling methods; and 3) we apply a one-dimensional CNN to capture temporal patterns among the spatial patterns along the paths for the estimation. Empirical results show that DeepIST soundly outperforms the state-of-the-art travel time estimation models by 24.37% to 25.64% of mean absolute error (MAE) in multiple large-scale real-world datasets.
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Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks
Given an urban development plan and the historical traffic observations over the road network, the Conditional Urban Traffic Estimation problem aims to estimate the resulting traffic status prior to the deployment of the plan. This problem is of great importance to urban development and transportation management, yet is very challenging because the plan would change the local travel demands drastically and the new travel demand pattern might be unprecedented in the historical data. To tackle these challenges, we propose a novel Conditional Urban Traffic Generative Adversarial Network (Curb-GAN), which provides traffic estimations in consecutive time slots based on different (unprecedented) travel demands, thus enables urban planners to accurately evaluate urban plans before deploying them. The proposed Curb-GAN adopts and advances the conditional GAN structure through a few novel ideas: (1) dealing with various travel demands as the "conditions" and generating corresponding traffic estimations, (2) integrating dynamic convolutional layers to capture the local spatial auto-correlations along the underlying road networks, (3) employing self-attention mechanism to capture the temporal dependencies of the traffic across different time slots. Extensive experiments on two real-world spatio-temporal datasets demonstrate that our Curb-GAN outperforms major baseline methods in estimation accuracy under various conditions and can produce more meaningful estimations.
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- NSF-PAR ID:
- 10195299
- Date Published:
- Journal Name:
- Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Page Range / eLocation ID:
- 842 to 852
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3394486.3403127
