Abstract
Abstract. Traffic management applications including congestion detection and tracking rely on traffic from multiple sources to model the traffic conditions. The sources are either stationary sensors which include inductive loop detectors (ILD), radar stations and Bluetooth/WiFi/BLE sensors or Floating Car Data (FCD) from moving vehicles which transmit their locations and speeds. The different sources have their inherent strengths and weaknesses but when used together, they have the potential to provide traffic information with increased robustness. Multi-sensor data fusion has the potential to enhance the estimation of traffic state in real-time by reducing the uncertainty of individual sources, extending the temporal and spatial coverage and increasing the confidence of data inputs. In this study, we fuse data from different FCD providers to improve travel time and average segment speeds estimation. We use data from INRIX, HERE and TomTom FCD commercial services and fuse the speeds based on their confidence values and granularity on virtual sub-segments of 250 m. Speeds differences between each pair of datasets are evaluated by calculating the absolute mean and standard deviation of differences. The evaluation of systematic differences is also performed for peak periods depending on the day of the week. INRIX FCD speeds are compared with ground truth spot speeds where both datasets are measured at a 1-minute interval which show good agreement with an error rate of between 8–20%. Some issues that affect FCD accuracy which include data availability and reliability problems are identified and discussed.
Highlights
Road traffic monitoring relies on the accurate collection of information from different sources for comprehensive spatial and temporal traffic analysis
We show the necessary steps required to fuse Floating Car Data (FCD) data to a common road segmentation network to estimate average speeds and travel times at high granularity
The FCD speeds from all the providers successfully estimated travel times but with varying performance depending on the traffic conditions
Summary
Road traffic monitoring relies on the accurate collection of information from different sources for comprehensive spatial and temporal traffic analysis. The third category is data from Bluetooth/WiFi/BLE sensors mounted on consecutive locations on a road providing segment travel times and average speeds. Radar and ILD sensors measure multi-lane absolute vehicle counts and spot speeds but do not provide accurate travel times and average speeds over road segments. Multi-sensor data fusion techniques seek to use these different data sources to provide comprehensive and more reliable traffic state estimation. Traffic management applications employ these techniques for improved traffic state estimation using data from stationary detectors, FCD providers and Bluetooth/WiFi/BLE sensors. These data sources are combined with strengths in one source complementing the other's weaknesses to increase the robustness of the data. FCD and Bluetooth/WiFi/BLE sensors, for example, provide extensive coverage compared to stationary detectors which only provide information at point locations due to cost implications
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