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- Abbas, Zainab, et al.
(författare)
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Real-time Traffic Jam Detection and Congestion Reduction Using Streaming Graph Analytics
- 2020
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Ingår i: <em>2020 IEEE International Conference on Big Data (Big Data)</em>. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3109-3118
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Konferensbidrag (refereegranskat)abstract
- Traffic congestion is a problem in day to day life, especially in big cities. Various traffic control infrastructure systems have been deployed to monitor and improve the flow of traffic across cities. Real-time congestion detection can serve for many useful purposes that include sending warnings to drivers approaching the congested area and daily route planning. Most of the existing congestion detection solutions combine historical data with continuous sensor readings and rely on data collected from multiple sensors deployed on the road, measuring the speed of vehicles. While in our work we present a framework that works in a pure streaming setting where historic data is not available before processing. The traffic data streams, possibly unbounded, arrive in real-time. Moreover, the data used in our case is collected only from sensors placed on the intersections of the road. Therefore, we investigate in creating a real-time congestion detection and reduction solution, that works on traffic streams without any prior knowledge. The goal of our work is 1) to detect traffic jams in real-time, and 2) to reduce the congestion in the traffic jam areas.In this work, we present a real-time traffic jam detection and congestion reduction framework: 1) We propose a directed weighted graph representation of the traffic infrastructure network for capturing dependencies between sensor data to measure traffic congestion; 2) We present online traffic jam detection and congestion reduction techniques built on a modern stream processing system, i.e., Apache Flink; 3) We develop dynamic traffic light policies for controlling traffic in congested areas to reduce the travel time of vehicles. Our experimental results indicate that we are able to detect traffic jams in real-time and deploy new traffic light policies which result in 27% less travel time at the best and 8% less travel time on average compared to the travel time with default traffic light policies. Our scalability results show that our system is able to handle high-intensity streaming data with high throughput and low latency.
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| 2. |
- Baars, Holger, et al.
(författare)
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An overview of the first decade of Polly(NET) : an emerging network of automated Raman-polarization lidars for continuous aerosol profiling
- 2016
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Ingår i: Atmospheric Chemistry And Physics. - : Copernicus GmbH. - 1680-7316 .- 1680-7324. ; 16:8, s. 5111-5137
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Tidskriftsartikel (refereegranskat)abstract
- A global vertically resolved aerosol data set covering more than 10 years of observations at more than 20 measurement sites distributed from 63 degrees N to 52 degrees S and 72 degrees W to 124 degrees E has been achieved within the Raman and polarization lidar network Polly(NET). This network consists of portable, remote-controlled multiwavelength-polarization-Raman lidars (Polly) for automated and continuous 24/7 observations of clouds and aerosols. Polly(NET) is an independent, voluntary, and scientific network. All Polly lidars feature a standardized instrument design with different capabilities ranging from single wavelength to multiwavelength systems, and now apply unified calibration, quality control, and data analysis. The observations are processed in near-real time without manual intervention, and are presented online at polly.tropos.de. The paper gives an overview of the observations on four continents and two research vessels obtained with eight Polly systems. The specific aerosol types at these locations (mineral dust, smoke, dust-smoke and other dusty mixtures, urban haze, and volcanic ash) are identified by their Angstrom exponent, lidar ratio, and depolarization ratio. The vertical aerosol distribution at the Polly(NET) locations is discussed on the basis of more than 55 000 automatically retrieved 30 min particle backscatter coefficient profiles at 532 nm as this operating wavelength is available for all Polly lidar systems. A seasonal analysis of measurements at selected sites revealed typical and extraordinary aerosol conditions as well as seasonal differences. These studies show the potential of Polly(NET) to support the establishment of a global aerosol climatology that covers the entire troposphere.
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