Conference proceeding
C-3-GAN: Complex-Condition-Controlled Urban Traffic Estimation through Generative Adversarial Networks
2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021), pp.1505-1510
IEEE International Conference on Data Mining
01/01/2021
DOI: 10.1109/ICDM51629.2021.00196
Abstract
Given historical traffic distributions and associated urban conditions observed in a city, the conditional urban traffic estimation problem aims at estimating realistic future projections of the traffic under a set of new urban conditions, e.g., new bus routes, rainfall intensity and travel demands. The problem is important in reducing traffic congestion, improving public transportation efficiency, and facilitating urban planning. However, solving this problem is challenging due to the strong spatial dependencies of traffic patterns and the complex relations between the traffic and urban conditions. In this paper, we tackle the challenges by proposing a novel Complex-Condition-Controlled Generative Adversarial Network (C-3-GAN) for urban traffic estimation of a region under various complex conditions. C-3-GAN features the following three novel designs on top of standard cGAN model: (1) an embedding network mapping the complex conditions to a latent space to find representations of the urban conditions; (2) an inference network to enhance the relations between the embedded latent vectors and the traffic data. Extensive experiments on real-world datasets demonstrate that our C-3-GAN produces high-quality traffic estimations and outperforms state-of-the-art baseline methods.
Details
- Title: Subtitle
- C-3-GAN: Complex-Condition-Controlled Urban Traffic Estimation through Generative Adversarial Networks
- Creators
- Yingxue Zhang - Worcester Polytechnic InstituteYanhua Li - Worcester Polytechnic InstituteXun Zhou - University of IowaZhenming Liu - William & MaryJun Luo - Lenovo
- Contributors
- J Bailey (Editor)P Miettinen (Editor)Y S Koh (Editor)D Tao (Editor)Xindong Wu (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- 2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021), pp.1505-1510
- Publisher
- IEEE
- Series
- IEEE International Conference on Data Mining
- DOI
- 10.1109/ICDM51629.2021.00196
- ISSN
- 1550-4786
- eISSN
- 2374-8486
- Number of pages
- 6
- Grant note
- Safety Research using Simulation University Transportation Center (SAFER-SIM) IIS-1942680; CNS1952085; CMMI1831140; DGE-2021871; CRII1755769; OAC-1835821; III-2008557 / NSF; National Science Foundation (NSF) 69A3551747131 / U.S. Department of Transportation's University Transportation Centers Program
- Language
- English
- Date published
- 01/01/2021
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380442202771
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