New Developments in Data-Driven Congestion Detection
With the development of data collection technologies, transportation data have become more and more ubiquitous. This has triggered a series of data-driven research projects to investigate transportation phenomena. Some recent studies have proposed data-driven methods for congestion detection and prediction. Typical approaches for congestion detection include Global Positioning System (GPS) trace analysis, use of back propagation (BP) neural networks and Markov models, real-time adaptive background extraction, undedicated mobile phone data analysis, space-time scan statistics (STSS) based non-recurrent congestion (NRC) detection, etc. Several congestion prediction methods have also been developed such as adaptive data-driven real-time congestion prediction, traffic flow prediction using floating car trajectory data, Bayesian network analysis, deep learning theory, data mining based approaches (integration of K-means clustering, decision trees, and neural networks), Hierarchical fuzzy rule-based systems optimized with genetic algorithms, etc. These existing studies have made significant contributions to development of the methodologies and technologies for traffic congestion detection and prediction, but with the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT) technologies, new challenges and opportunities are continuously emerging with higher requirements for metrics such as detection and prediction accuracy, real-time results, and stability.
Recently, artificial intelligence (AI) has become one of the most promising techniques to tackle tremendously high-dimensional data analysis tasks. AI technologies have been applied for transportation analysis applications such as traffic signal control, network design, pedestrian crossing detection, travel time prediction, short term traffic volume prediction, and car ownership determinants. Specific applications include locating inspection facilities in traffic networks, real-time highway traffic condition assessment, and decision support in real-time for traffic flow management. However, the applications of AI technologies, especially deep learning, are still in their early stages in the transportation area. This proposed research attempts to extend AI technologies into large-scale transportation network analysis.
The research team has already established an on-line transportation platform, named the Digital Roadway Interactive Visualization and Evaluation Network (DRIVE Net). DRIVE NET can be used for sharing, integration, visualization, and analysis of transportation-related data. The proposed research aims to extend the functions of DRIVE Net by developing an AI platform for network-wide congestion detection and prediction using multi-source data.
A sample of the current DRIVE Net interface.
The primary objective of this project is to extend the functions of DRIVE Net by developing an AI platform for network-wide congestion detection and prediction using multi-source data. In particular, the research team aims to achieve the following research objectives:
- Develop new databases by employing the Microsoft Trusted Data Platforms for improving the efficiency of data management.
- Design an AI platform architecture to fully apply/use big data resources.
- Develop AI-based analytical models for network-wide congestion detection and prediction.
- Validate the research findings from the model results of the AI platform with real-world, multi-source data.
To achieve the above objectives, the team plans to work closely with WSDOT and Seattle DOT on a variety of multi-source data access and technology transfer activities. Furthermore, the team will work with industry partners for outreach activities and additional data support.
The primary deliverables for this project are:
- AI-based models developed for traffic congestion detection and prediction
- An interim report on database redesign and data management
- An interim report on AI platform architecture redesign
- An interim report on AI platform functions development
- An interim report on system tests that make use of multi-source data
- Final report
|Principal Investigator||Yinhai Wang, University of Washington|
|Funding Source||C2SMART Center: $82,500
University of Washington (cost-share): $41,250
|Total Project Cost||$123,750|
|USDOT Award #||69A3551747124|
|Start and End Dates||03/01/2018-05/31/2019|
|Implementation of Research Outcomes||Implementation of this research will include testing the function of congestion detection and prediction using multi-source data on the proposed AI platform.|
|Impacts/Benefits of Implementation||This project aims to provide trained and reusable deep learning models for traffic congestion detection and prediction and make traffic congestion analysis more convenient and efficient for public agencies, researchers, and traffic practitioners.|