Development of web-based analysis system for real-time decision support on arterial networks

With increasing amounts of data being collected for intelligent transportation systems on arterial networks, the archival, management, and analysis of complex network traffic data have become a challenge. Challenges include inconsistent data connections, data quality control, query performance, traffic prediction, and computational limitations. The web-based RADAR Net system is presented to address these challenges. This system adopts a relational database with link, intersection, and detector entities. Relational data demonstrate its query performance and scalability. The system contains four layers: offline server, online server (middleware), online server (Java Servlet), and online client. This four-layer design successfully distributes the computational burden on the server. To monitor arterial performance, link speeds are calculated directly from loop detector data retrieved in Bellevue, Washington. The system can dynamically predict and smooth real-time loop spot speeds by using an alpha-beta filter (a simplified version of the Kalman filter) while maintaining high system performance. The link speeds of the entire network are calculated and updated in real time. Many application modules (e.g., for capacity analysis and dynamic routing) based on the system architecture have been implemented and have proven the system feasible for performing real-time analysis and assisting decision making.