Detection of urban traffic patterns from Floating Car Data (FCD)

Abstract

Abstract: Real time data collection in traffic engineering is crucial for better traffic corridor control and management. In the literature, many data collection methods have been used such as; magnetic loops, road tube counters, piezo sensors, radars, Bluetooth etc. to estimate the link occupancy, average speed or density of a corridor. More recently, Floating Car Data (FCD) has become another important traffic data source and has an increasing usage due to its lower cost and higher coverage despite its reliability problems. FCD obtained from GPS equipped vehicles moving in the traffic can provide speed or travel speed data for many segments for even 1-min intervals in real-time. Though not totally diverse providing more than one of the traffic flow parameters, measuring the effectiveness of this extensive data source in detecting some critical urban traffic states is the ultimate goal of this study. As a case study, 1-min interval FCD for an urban arterial in Ankara has been collected during the morning peak hour for 2 months. Average speed values were transformed into a qualitative 4-scale state parameter based on the Level of Service (LOS) definitions for urban roads. Pattern searches over consecutive segment states using different search length (i.e. 2 segments, 3 segments, etc.) showed that FCD is capable to detect recurrent congestion or bottleneck locations, and even have an idea about the length of queue formed before the bottlenecks.