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Dynamic prediction of queues and delays at signalized intersections utilize data obtained from automated traffic sensors as inputs for Intelligent Transportation Systems (ITS) applications. Errors are inevitable during automated data handling, especially when the traffic involved is heterogeneous and lacking in lane discipline. This paper presents a model-based estimation scheme that can handle both mixed traffic conditions and erroneous detector input data to estimate queue and, in turn, delay. The models were developed for Queue within Advance Detector (QWAD) and Queue beyond Advance Detector (QBAD) scenario. The statistical properties of detector errors were incorporated into the estimation scheme, and the scheme was tested for varying traffic conditions. The estimation scheme's performance was evaluated using field data from a signalized intersection in Chennai, India, and simulated data. It is found that the incorporation of statistical properties of detector error allows accurate estimation of queues and delays despite erroneous data input. [ABSTRACT FROM AUTHOR]

Drifter buoys are globally deployed to measure surface meteorological and oceanographic variables. A Lagrangian drifting buoy (Pradyu II) to measure sea-surface temperature and current has been developed at the National Institute of Ocean Technology, Chennai. The drifter buoy with geostationary satellite communication (INSAT-3C) to have near real-time data at every hour is a unique attempt in the history of drifting buoy nodes. This article describes Pradyu II drifting buoy node, design of low-power embedded system, communication network and field test results from an experiment conducted in the Bay of Bengal during March-April 2013. The results from Pradyu II are compared with commercially available drifting buoy (Marlin-Yug), moored data buoy (BD11) and remotely sensed data. [ABSTRACT FROM AUTHOR]