As the volume of transportation data grows, users grapple with the basics of big data implementation. The highest-priority request from more than 200 attendees of the 2017 Big Data Analytics Workshop was for case studies and examples of implementation. This workshop provides case studies of big data methods used in practice and hands-on analysis. The results of the workshop will be documented in a workbook and disseminated through the sponsoring committees.
Transportation agencies and Departments of Transportation (DOTs) rely on a dense network of CCTV cameras primarily for real time viewing and monitoring of traffic conditions on roadways . Very minimal to no automated analytics is performed on such high-valued asset. This presentation talks about computer vision systems that leverages recent developments in high performance computing and advanced machine learning algorithms to maximize the use of data streaming from CCTV cameras. The system could potentially provide various video analytic capabilities that will enable CCTV cameras to automatically extract important traffic information: traffic data (volume, density, speed and vehicle classification), traffic condition (congested, stalled vehicle, free – flow) and road condition information (snow, wet or dry pavement).
This presentation will briefly introduce several specific technical concepts related to storage and analysis of transportation big data, including distributed computing, Hadoop, and related ecosystem tools. The main portion of the presentation will focus on use of those back-end technologies to power big data analytics and tools to solve specific real-world transportation operations and planning problems. Finally, the presentation will address emerging technologies, such as blockchain, and their potential impact on transportation.
This presentation introduces the most recent development on the I-210 Connected Corridors Project, with particular emphasis on the new cloud based data hub. This presentation consists of two parts. In the first part, we will talk about the cloud based architecture and how we integrate various traffic systems and standardize heterogeneous data feeds using a common data format (e.g., TMDD). We will also talk about the data streaming technique using Kafka, the data storage methods using relational (e.g., Postgres) and non-relational (e.g., MongoDB and Cassandra) databases, and the data processing framework using SPARK. In the second part, we will talk about how to utilize the data to support the development of the decision support system, which includes the topics of arterial performance dashboard, traffic state estimation, Aimsun modeling, calibration and prediction, and development of the rules engine.