Detecting queue length on urban arterials using floating car data (FCD)

Altıntaşı, Oruç
Accurate estimation of queue lengths whether in the approach of a signalized intersection or near a bottleneck location along an uninterrupted urban arterial is essential for better traffic management. This requires reliable traffic data, which is traditionally obtained from loop detectors, video cameras, etc. More recently, Floating Car Data (FCD) is being increasingly used as an alternative traffic data source due to its lower cost and high coverage area. Commercially available FCD is obtained from GPS equipped vehicles moving in the traffic and can provide speed or travel time data for many segments for even 1-min intervals in real-time. The main focus of this thesis is to develop a mathematical model to estimate queue length (QL) in both signalized intersections and uninterrupted arterials using FCD. The model is mainly based on determination of speed threshold value for QL estimation. Speed field, generated from FCD using 4-node quadratic interpolation technique, was used to generate imaginary vehicle trajectory data and provided iso-speed contours in FCD. The model performance was first tested in VISSIM environment by creating a hypothetical approach leg of a signalized intersection. Later, model performance was evaluated in two study corridors (an uninterrupted urban arterial and a signalized intersection) located in Ankara. For the signalized case, selection of speed threshold of 20 km/h provided promising estimation results with a root mean square error (RMSE) of 23.21 m and a mean absolute percentage error (MAPE) of 7.68%. For the uninterrupted corridor, selection of speed threshold as 42 km/h provided the maximum QL profile over time.
Citation Formats
O. Altıntaşı, “Detecting queue length on urban arterials using floating car data (FCD),” Ph.D. - Doctoral Program, Middle East Technical University, 2018.