Detection of Urban Traffic Patterns from Floating Car Data (FCD)
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Pattern searches over consecutive segment states 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.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 (ie 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 bottlenecksread more
Citations
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References
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Proceedings ArticleDOI
Traffic Estimation And Prediction Based On Real Time Floating Car Data
TL;DR: This paper proposes two algorithms, respectively based on artificial neural networks and pattern-matching, designed to on-line perform short-term predictions of link travel speeds by using current and near-past link average speeds estimated by the OCTOTelematics FCD system.
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Data Collection of Freeway Travel Time Ground Truth with Bluetooth Sensors
TL;DR: Bluetooth sensors are introduced as a new and effective means of data collection of freeway ground truth travel time and results show that the new technology is a promising method for collecting high-quality travel time data that can be used as ground truth for evaluating other sources ofTravel time and other intelligent transportation system applications.
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Urban traffic congestion estimation and prediction based on floating car trajectory data
TL;DR: A novel approach to estimate and predict the urban traffic congestion using floating car trajectory data efficiently using a new fuzzy comprehensive evaluation method in which the weights of multi-indexes are assigned according to the traffic flows.
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Arterial Performance Measures with Media Access Control Readers: Portland, Oregon, Pilot Study
TL;DR: The real-time MAC reader information provides substantial opportunity to add new control and performance monitoring capability to other intelligent transportation system components, such as ramp metering, transit signal priority systems, and adaptive signal control.
Journal ArticleDOI
Identifying Urban Traffic Congestion Pattern from Historical Floating Car Data
Lin Xu,Yang Yue,Qingquan Li +2 more
TL;DR: Results show that the novel floating car data analysis method based on data cube for congestion pattern exploration can effectively identify and summarize the congestion pattern with efficient computation and reduced storage cost.