原文传递
Large-Scale Full-Coverage Traffic Speed Estimation under Extreme Traffic Conditions Using a Big Data and Deep Learning Approach: Case Study in China
| 题名： | Large-Scale Full-Coverage Traffic Speed Estimation under Extreme Traffic Conditions Using a Big Data and Deep Learning Approach: Case Study in China |
| 正文语种： | 英文 |
| 作者： | Fan Ding, Ph.D.1; Zhen Zhang2; Yang Zhou3; Xiaoxuan Chen, Ph.D.4; Bin Ran, Ph.D.5 |
| 作者单位： | 1Research Associate, Dept. of Civil and Environmental Engineering, Univ. of Wisconsin–Madison, 2205 Engineering Hall, 1415 Engineering Dr., Madison, WI 53706 (corresponding author). 2Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Wisconsin–Madison, 2205 Engineering Hall, 1415 Engineering Dr., Madison, WI 53706. 3Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Wisconsin–Madison, 2205 Engineering Hall, 1415 Engineering Dr., Madison, WI 53706. 4ITS Engineer, TranSmart Technologies, Inc., 411 S. Wells St., Suite 1000, Chicago, IL 60607. 5Professor, Dept. of Civil and Environmental Engineering, Univ. of Wisconsin–Madison, 2205 Engineering Hall, 1415 Engineering Dr., Madison, WI 53706. |
| 关键词： | Full coverage; Cellular data; Big data; Traffic speed estimation; Deep learning; Long short-term memory; Extreme traffic conditions. |
| 摘要： | For both travelers and traffic operation centers, especially under extremely large traffic volumes, full-coverage traffic state monitoring of a major corridor is urgently needed. In the present paper, a traffic speed estimation method is proposed using a big data and deep learning approach under extreme traffic conditions. Particularly, a geospatial mapping method is proposed in this paper. This method ensures the scalability and easy-deployment, extracts phone speed (PSP) and phone count (PC) from raw cellular data, and estimates the traffic speed using a deep long short-term memory (DLSTM) neural network. The proposed method is used to estimate traffic speed for a major expressway in China that is installed with limited roadside equipment. The field test, which gives a promising performance, was performed during the Golden Week, the Chinese national holiday in 2014 (00:00 October 1 to 23:59 October 7) on the nearly 250-km-long busy freeway, G42, for both directions. The results suggest that the proposed cellular-based system can be an alternative and supplement solution for monitoring various practical traffic states, especially when only limited conventional roadside equipment is installed. |
| 出版年： | 2019 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 145 |
| 期： | 5 |
| 页码： | 1-13 |
相关文献
- Large-Scale Freeway Traffic Flow Estimation Using Crowdsourced Data: A Case Study in Arizona
- Physics-Informed Spatiotemporal Learning Framework for Urban Traffic State Estimation
- Artificial Neural Network–Based Traffic State Estimation Using Erroneous Automated Sensor Data
- Designing a Methodology to Calibrate and Validate Virtual Test Driving in a Large-Scale Network with Multisource Data
- Robust Deep Learning Architecture for Traffic Flow Estimation from a Subset of Link Sensors
- A New Framework for Regional Traffic Volumes Estimation with Large-Scale Connected Vehicle Data and Deep Learning Method
检索历史
应用推荐
