Articoli di riviste: "Traffic engineering – Data processing"

1

Knoop, Victor L., Serge P. Hoogendoorn, and Henk J. van Zuylen. "Processing Traffic Data Collected by Remote Sensing." Transportation Research Record: Journal of the Transportation Research Board 2129, no. 1 (January 2009): 55–61. http://dx.doi.org/10.3141/2129-07.

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Tarko, Andrzej P., and Nagui M. Rouphail. "Intelligent Traffic Data Processing for ITS Applications." Journal of Transportation Engineering 123, no. 4 (July 1997): 298–307. http://dx.doi.org/10.1061/(asce)0733-947x(1997)123:4(298).

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3

Mallikarjuna, C., A. Phanindra, and K. Ramachandra Rao. "Traffic Data Collection under Mixed Traffic Conditions Using Video Image Processing." Journal of Transportation Engineering 135, no. 4 (April 2009): 174–82. http://dx.doi.org/10.1061/(asce)0733-947x(2009)135:4(174).

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4

Sun, Yuan, Hao Xu, Jianqing Wu, Jianying Zheng, and Kurt M. Dietrich. "3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (June 8, 2018): 14–22. http://dx.doi.org/10.1177/0361198118775839.

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High-resolution vehicle data including location, speed, and direction is significant for new transportation systems, such as connected-vehicle applications, micro-level traffic performance evaluation, and adaptive traffic control. This research developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside light detection and ranging (LiDAR) sensor. Different from existing methods for vehicle onboard sensing systems, this procedure was developed specifically to extract high-resolution vehicle trajectories from roadside LiDAR sensors. This procedure includes preprocessing of the raw data, statistical outlier removal, a Least Median of Squares based ground estimation method to accurately remove the ground points, vehicle data clouds clustering, a principle component-based oriented bounding box method to estimate the location of the vehicle, and a geometrically-based tracking algorithm. The developed procedure has been applied to a two-way-stop-sign intersection and an arterial road in Reno, Nevada. The data extraction procedure has been validated by comparing tracking results and speeds logged from a testing vehicle through the on-board diagnostics interface. This data processing procedure could be applied to extract high-resolution trajectories of connected and unconnected vehicles for connected-vehicle applications, and the data will be valuable to practices in traffic safety, traffic mobility, and fuel efficiency estimation.

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Zhao, Liangbin, Guoyou Shi, and Jiaxuan Yang. "Ship Trajectories Pre-processing Based on AIS Data." Journal of Navigation 71, no. 5 (April 22, 2018): 1210–30. http://dx.doi.org/10.1017/s0373463318000188.

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Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands from January to February 2015. The results indicate that the algorithms can effectively improve the quality of AIS trajectories.

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Ivanov, Alexander, and Alexander Platov. "Environmental monitoring based on data processing of Internet of Things." E3S Web of Conferences 136 (2019): 01041. http://dx.doi.org/10.1051/e3sconf/201913601041.

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The concept of online monitoring of the urban environment is proposed. It is based on the online processing of hydrometeorological and traffic information received through the Internet of Things. The traditional approach of the Internet of things includes transfer and storage of huge arrays of measurements in digital form. This concept of online monitoring is primarily an analysis, evaluation of the results of processing information received from wireless networks. The concept was implemented at Nizhny Novgorod State University of Architecture and Civil Engineering in several services including Eco-routes, Quite-routes, in which the air pollution of the urban environment by vehicle emissions and the noise level from traffic flows are estimated in real time mode. The calculation is based on meteorological data and traffic flow velocity. The calculation and assessment of environment pollution is carried out at the request of the user via Internet. The concept includes micro weather and algal blooming monitoring of reservoirs and ponds. Developed services is the first to provide free short time health risk assessment for both decision makers and common internet users.

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Zhao, Ming, Norman W. Garrick, and Luke E. K. Achenie. "Data Reconciliation–Based Traffic Count Analysis System." Transportation Research Record: Journal of the Transportation Research Board 1625, no. 1 (January 1998): 12–17. http://dx.doi.org/10.3141/1625-02.

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Traffic volume data, especially average annual daily traffic (AADT), are important in transportation engineering. They are required in managing and maintaining existing facilities and in planning and designing new facilities. Many state highway agencies use the ramp counting procedure described in FHWA’s Traffic Monitoring Guide to estimate AADTs for freeways. The procedure involves counting all entrance and exit ramps between two established mainline counters (anchor points) and then reconciling the count data to estimate mainline AADT. The reconciling of count data includes three steps. First, AADTs for the ramps and the anchor points are estimated from the count data. Then AADT for each uncounted mainline link is calculated by addition or subtraction of ramp AADT to or from mainline AADT, starting from one anchor point. Finally, adjustments of the AADT are performed to achieve a match at the second anchor point if necessary. The process can be time-consuming and labor-intensive if it is done manually. A computer program to automate the process is required. The traffic count analysis system (TCAS) developed to automate the reconciling of count data in the ramp counting process is described. The TCAS was developed on the basis of data coaptation and data reconciliation techniques frequently used in the processing of network flow rate data. Data coaptation is used to calculate flow rates for uncounted links, and data reconciliation is used to adjust and balance the flow rates. The TCAS has been tested for the two longest freeways in Connecticut. The results are close to those from the ramp counting procedure. However, the TCAS significantly reduces the time and labor required for processing traffic volume data for freeways.

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Zhihuang Jiang. "Traffic Operation Data Analysis and Information Processing Based on Data Mining." Automatic Control and Computer Sciences 53, no. 3 (May 2019): 244–52. http://dx.doi.org/10.3103/s0146411619030040.

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Zhou, Xin. "Research on Front-End Fusion Processing Technology of Traffic Scenes." Journal of Architectural Research and Development 6, no. 2 (March 4, 2022): 1–7. http://dx.doi.org/10.26689/jard.v6i2.3707.

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With the intelligent development of road traffic control and management, higher requirements for the accuracy and effectiveness of traffic data have been put forward. The issue of how to collect and integrate data for traffic scenes has sought importance in this field as various treatment technologies have emerged. A lot of research work have been carried out from the theoretical aspect to engineering application.

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Chronopoulos, Anthony Theodore, and Gang Wang. "Traffic Flow Simulation through Parallel Processing." Transportation Research Record: Journal of the Transportation Research Board 1566, no. 1 (January 1996): 31–38. http://dx.doi.org/10.1177/0361198196156600104.

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Numerical methods for solving traffic flow continuum models have been studied and efficiently implemented in traffic simulation codes in the past. Explicit and implicit methods have been used in traffic simulation codes in the past. Implicit methods allow a much larger time step size than explicit methods to achieve the same accuracy. However, at each time step a nonlinear system must be solved. The Newton method, coupled with a linear iterative method (Orthomin), is used. The efficient implementation of explicit and implicit numerical methods for solving the high-order flow conservation traffic model on parallel computers was studied. Simulation tests were run with traffic data from an 18-mile freeway section in Minnesota on the nCUBE2 parallel computer. These tests gave the same accuracy as past tests, which were performed on one-processor computers, and the overall execution time was significantly reduced.

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Tso, Fung Po, and Dimitrios P. Pezaros. "Improving Data Center Network Utilization Using Near-Optimal Traffic Engineering." IEEE Transactions on Parallel and Distributed Systems 24, no. 6 (June 2013): 1139–48. http://dx.doi.org/10.1109/tpds.2012.343.

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12

PAMULA, Wieslaw, and Marcin Jacek KŁOS. "ON SITE PROCESSING OF VIDEO STREAM FOR MAPPING TRAFFIC PARAMETERS." Scientific Journal of Silesian University of Technology. Series Transport 117 (December 1, 2022): 175–89. http://dx.doi.org/10.20858/sjsutst.2022.117.12.

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Traffic surveillance provides crucial data for the operation of intelligent transportation systems. The growing number of cameras in the transport system poses a problem for the efficient processing of surveillance data. Processing of video data for extracting traffic parameters is usually done using image processing methods and requires substantial processing resources. An alternative way is to transform the video stream and map the traffic parameters using the obtained transform coefficients. Spatiotemporal wavelet transform of the video stream contents, using filter banks, is proposed for mapping traffic parameters. Performed tests prove good resilience to illumination changes of road scenes. Mapping errors are smaller than in the case of the commonly used video detectors at sites on multilane roads with low to moderate traffic load.

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Dolgikh, D. G., and A. M. Sukhov. "Systems of Internet Traffic Reservation. Experimental Data and Their Processing." Telecommunications and Radio Engineering 68, no. 13 (2009): 1183–88. http://dx.doi.org/10.1615/telecomradeng.v68.i13.70.

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14

Chunchu, Mallikarjuna, Ramachandra Rao Kalaga, and Naga Venkata Satish Kumar Seethepalli. "ANALYSIS OF MICROSCOPIC DATA UNDER HETEROGENEOUS TRAFFIC CONDITIONS." TRANSPORT 25, no. 3 (September 30, 2010): 262–68. http://dx.doi.org/10.3846/transport.2010.32.

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Collecting microscopic data is difficult under heterogeneous traffic conditions. This data is essential when modelling heterogeneous traffic at a microscopic level. In this paper, microscopic data collected under heterogeneous traffic conditions using a video image processing technique is presented. Data related to heterogeneous traffic such as vehicle composition in the traffic stream, a lateral distribution of vehicles, lateral gaps and longitudinal gaps have been collected. The lateral distribution of vehicles on a ten‐meter wide road has been analyzed with a specific emphasis on motorized two‐wheeler movement. Using trajectory data, an attempt to examine the gap maintaining the behaviour of vehicles under different traffic conditions has been made. Empirical relationships between the lateral gap and area occupancy have been proposed for various vehicle combinations. The influence of difference in the lateral positions of leading and following vehicles on the longitudinal gap has been analyzed.

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Ottaviano, Flavia, Fabing Cui, and Andy H. F. Chow. "Modeling and Data Fusion of Dynamic Highway Traffic." Transportation Research Record: Journal of the Transportation Research Board 2644, no. 1 (January 2017): 92–99. http://dx.doi.org/10.3141/2644-11.

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This paper presents a data fusion framework for processing and integrating data collected from heterogeneous sources on motorways to generate short-term predictions. Considering the heterogeneity in spatiotemporal granularity in data from different sources, an adaptive kernel-based smoothing method was first used to project all data onto a common space–time grid. The data were then integrated through a Kalman filter framework build based on the cell transmission model for generating short-term traffic state prediction. The algorithms were applied and tested with real traffic data collected from the California I-880 corridor in the San Francisco Bay Area from the Mobile Century experiment. Results revealed that the proposed fusion algorithm can work with data sources that are different in their spatiotemporal granularity and improve the accuracy of state estimation through incorporating multiple data sources. The present work contributed to the field of traffic engineering and management with the application of big data analytics.

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Ge, Dong-Yuan, Xi-Fan Yao, Wen-Jiang Xiang, En-Chen Liu, and Zhi-Bin Xu. "Theory and Method of Data Collection for Mixed Traffic Flow Based on Image Processing Technology." Mathematical Problems in Engineering 2021 (June 22, 2021): 1–8. http://dx.doi.org/10.1155/2021/9966494.

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As a key element of ITS (intelligent traffic systems), traffic information collection facilities play a key role, with ITS being able to analyze the state of mixed traffic more appropriately and can provide effective technical support for the design, management, and the evaluation of constructions. Traffic Infrastructure. Focusing on image processing technology, this study takes pedestrians, electric motor, and vehicles in mixed traffic flow as the research object, and Gaussian mixed model, Kalman filtering, and Fisher linear discriminant are introduced in the recognition system. On this basis, the mixed motion flow data acquisition framework model is elaborated in detail, which includes attribute extraction, object recognition, and object tracking. Given the difficulty in capturing reliable images of objects in real traffic scenes, this study adopted a novel background and foreground classification method with region proposal network so as to decrease the number of regions proposal from 2000 to 300, which can detect objects fast and accurately. Experiments demonstrate that the designed programme can collect the flow data by detecting and tracking moving object in the surveillance video for mixed traffic. Further integration of various modules to achieve integrated collection is another important task for further research and development. In the future, research on dynamic calibration of monocular vision will be carried out for distance measurement and speed measurement of vehicles and pedestrians.

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Wu, Jianqing, Hao Xu, Yuan Sun, Jianying Zheng, and Rui Yue. "Automatic Background Filtering Method for Roadside LiDAR Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (June 17, 2018): 106–14. http://dx.doi.org/10.1177/0361198118775841.

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The high-resolution micro traffic data (HRMTD) of all roadway users is important for serving the connected-vehicle system in mixed traffic situations. The roadside LiDAR sensor gives a solution to providing HRMTD from real-time 3D point clouds of its scanned objects. Background filtering is the preprocessing step to obtain the HRMTD of different roadway users from roadside LiDAR data. It can significantly reduce the data processing time and improve the vehicle/pedestrian identification accuracy. An algorithm is proposed in this paper, based on the spatial distribution of laser points, which filters both static and moving background efficiently. Various thresholds of point density are applied in this algorithm to exclude background at different distances from the roadside sensor. The case study shows that the algorithm can filter background LiDAR points in different situations (different road geometries, different traffic demands, day/night time, different speed limits). Vehicle and pedestrian shape can be retained well after background filtering. The low computational load guarantees this method can be applied for real-time data processing such as vehicle monitoring and pedestrian tracking.

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II Kim, Kwang, Keon Myung Lee, and Jang Young Ahn. "Methods of ship trajectory data processing for applying artificial neural network in port area." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 145. http://dx.doi.org/10.14419/ijet.v7i2.12.11112.

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Background/Objectives: In Vessel Traffic Service (VTS), prediction of the flow of vessel traffic is essential to serve safety information and control ship traffic. However, it is difficult to predict a ship’s speed due to many external forces and environmental conditions. This study proposes a data processing method to convert ship speed data to categorical data by dividing ship navigating routes into several gate lines.Methods/Statistical analysis: A ship’s trajectory is converted to each route’s gate line speed. To determine the gate line speed, we convertedthe previous and subsequent gate line speeds into category data. The input and output category data were applied to a multilayer perceptron network using as input variablesthe previous speed variance category, ship type, and ship length, and as output variable the subsequent speed variance.Findings: These results are useful because categorical data can be applied to various neural network models. As a result of the conducted experiments, the accuracy of the model improved when many gate lines are included.Improvements/Applications: The study results can be applied topredict ship traffic flow for VTS operators.

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Chindanur, Narendra Babu, and Pallaviram Sure. "Low-Dimensional Models for Traffic Data Processing Using Graph Fourier Transform." Computing in Science & Engineering 20, no. 2 (March 2018): 24–37. http://dx.doi.org/10.1109/mcse.2018.110111913.

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Li, Shuo, Tommy Nantung, and Yi Jiang. "Assessing Issues, Technologies, and Data Needs to Meet Traffic Input Requirements by Mechanistic–Empirical Pavement Design Guide." Transportation Research Record: Journal of the Transportation Research Board 1917, no. 1 (January 2005): 141–48. http://dx.doi.org/10.1177/0361198105191700116.

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As part of the implementation initiatives undertaken by the Indiana Department of Transportation Research Division, this paper presents the effort made to identify potential issues arising from traffic data processing and to assess technologies and data needs to meet the requirements of traffic design inputs in the Mechanistic–Empirical Pavement Design Guide. Global Positioning Systems (GPSs) and geographical information system (GIS) technologies were proposed to manage weigh-in-motion (WIM) and automatic vehicle classification site information and manipulate the traffic design input database. Computer programs were developed to process the raw data ASCII files generated from a WIM vendor's software. A platform was developed to combine GPS coordinates, GIS base maps, data processing programs, and the traffic database into an integral unit. Three WIM sites were selected for trial study. It was demonstrated that, with this platform, the WIM sites and database can be accessed visually and more efficiently. In addition, the computer programs can save significant data processing time. Other issues, such as the possible effect of unclassified vehicle count, were identified. On the basis of findings from the implementation initiatives, necessary efforts and future implementation activities are outlined.

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Sheikh, Mr Mohammad Shabbir. "Traffic Sign Detection and Recognition using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1059–63. http://dx.doi.org/10.22214/ijraset.2021.38192.

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Abstract: Now a days, automobiles became most convenient mode of transportation for everyone. As we know one of the most important functions, TSDR has become a popular research . It primarily involves the use of vehicle cameras to collect real- time road pictures and then recognize and identify traffic signs seen on the road, therefore delivering correct data to the driving system. With the advancement of science and technology, an increasing number of scholars are turning to deep learning technology to save time in traditional processes. From the training samples, this model can learn the deep features inside the autonomously. The accuracy and great efficiency of detection and identification are the subject of this essay. A deep convolution neural network algorithm is proposed to train traffic sign training sets using Caffe[3], an open-source framework, in order to obtain a model that can classify traffic signs and learn and identify the most critical of these traffic sign features, in order to achieve the goal of identifying traffic signs in the real world. Keywords: Traffic sign, Segmentation, Gabor filter, Traffic Sign Detection and Recognition (TSDR)

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Sun, Hong Feng, Ying Li, and Hong Lv. "Statistical Analysis of the Massive Traffic Data Based on Cloud Platform." Advanced Materials Research 717 (July 2013): 662–66. http://dx.doi.org/10.4028/www.scientific.net/amr.717.662.

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Currently, with the rapid development of various geographic data acquisition technologies, the data-intensive geographic calculation is becoming more and more important. The urban motor vehicles loaded with GPS, namely the transport vehicles, can real-timely collect a large number of urban traffic information. If these massive transportation vehicle data can be real-timely collected and analyzed, the real-time and accurate basic information will be provided for monitoring the large area of traffic status as well as the intelligent traffic management. Based on the requirements of the organization, the processing, the statistics and the analysis of the massive urban traffic data, the new framework of the massive data-intensive calculation under the environment of cloud platform has been proposed through employing Bigtable, Mapreduce and other technologies.

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Suo, Long, Lijun Qi, and Li Wang. "Link Load Correlation-Based Blocking Performance Analysis for Tree-Type Data Center Networks." Applied Sciences 12, no. 12 (June 19, 2022): 6235. http://dx.doi.org/10.3390/app12126235.

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With the explosive growth of cloud computing applications, the east-west traffic among servers has come to occupy the dominant proportion of the traffic in data center networks (DCNs). Cloud computing tasks need to be executed in a distributed manner on multiple servers, which exchange large amounts of intermediate data between the adjacent stages of each multi-stage task. Therefore, the congestion in DCNs can reduce the processing performance when conducting multi-stage tasks. To address this, the relationship between the blocking performance and the traffic load can be adopted as a theoretical basis for network planning and traffic engineering. In this paper, the traffic load correlation between edge links and aggregation links is considered, and an iterative blocking performance analysis method is proposed for two-layer tree-type DCNs. The simulation results show the good accuracy of the proposed method with respect to the theoretical results especially in the blocking rate range below 4% and with over-subscription ratio 1.5.

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Xiao, Jianli, Hang Li, Xiang Wang, and Shangcao Yuan. "Traffic Peak Period Detection from an Image Processing View." Journal of Advanced Transportation 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/2097932.

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Traffic peak period detection is very important for the guidance and control of traffic flow. Most common methods for traffic peak period detection are based on data analysis. They have achieved good performance. However, the detection processes are not intuitional enough. Besides that, the accuracy of these methods needs to be improved further. From an image processing view, we introduce a concept in corner detection, sharpness, to detect the traffic peak periods in this paper. The proposed method takes the traffic peak period detection problem as a salient point detection problem and uses the image processing strategies to solve this problem. Firstly, it generates a speed curve image with the speed data. With this image, the method for detection of salient points is adopted to obtain the peak point candidates. If one candidate has the lowest speed value, this candidate is the peak point. Finally, the peak period is gotten by moving forward and backward the corresponding time of the peak point with a time interval. Experimental results show that the proposed method has achieved higher accuracy. More importantly, as the proposed method solves the traffic peak period detection problem from an image processing view, it has more intuition.

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Liu, Zhouzhou, Xu Cheng, Yangmei Zhang, and Han Peng. "Data Collection Method of Large Scale WSNS Mobile Node Based on Compressed Sensing and Intelligent Optimization." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 2 (April 2020): 333–40. http://dx.doi.org/10.1051/jnwpu/20203820333.

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Aiming at the defects of large-scale large scale wireless sensor network data processing network traffic and high task latency, a data collection scheme of mobile node based on discrete elastic collision optimization algorithm and adaptive block compression sensing is proposed. Firstly, by analyzing the relationship between the network partitioning and the node deployment, an adaptive block compressed sensing data collection strategy is proposed to realize sensor node based on adaptive network block compressed sensing data collection. Designing mobile node data acquisition path planning strategy and multiple mobile nodes The collaborative computer system adopts the fitness value constraint transformation processing technology and the parallel discrete elastic collision optimization algorithm to achieve the purpose of balancing network node energy consumption and reducing data processing task delay. Finally, the simulation results show that the data collection scheme can effectively realize high-efficiency processing of large-scale sensor network data, and reduce network traffic and network task delay, and better balance network node energy consumption.

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Wang, Xingmin, Shengyin Shen, Debra Bezzina, James R. Sayer, Henry X. Liu, and Yiheng Feng. "Data Infrastructure for Connected Vehicle Applications." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 5 (April 9, 2020): 85–96. http://dx.doi.org/10.1177/0361198120912424.

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Ann Arbor Connected Vehicle Test Environment (AACVTE) is the world’s largest operational, real-world deployment of connected vehicles (CVs) and connected infrastructure, with over 2,500 vehicles and 74 infrastructure sites, including intersections, midblocks, and highway ramps. The AACVTE generates a massive amount of data on a scale not seen in the traditional transportation systems, which provides a unique opportunity for developing a wide range of connected vehicle (CV) applications. This paper introduces a data infrastructure that processes the CV data and provides interfaces to support real-time or near real-time CV applications. There are three major components of the data infrastructure: data receiving, data pre-processing, and visualization including the performance measurements generation. The data processing algorithms include signal phasing and timing (SPaT) data compression, lane phase mapping identification, trajectory data map matching, and global positioning system (GPS) coordinates conversion. Simple performance measures are derived from the processed data, including the time–space diagram, vehicle delay, and observed queue length. Finally, a web-based interface is designed to visualize the data. A list of potential CV applications including traffic state estimation, traffic control, and safety, which can be built on this connected data infrastructure is discussed.

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Siyal, M. Y., and M. Fathy. "Image Processing Techniques For Real-Time Qualitative Road Traffic Data Analysis." Real-Time Imaging 5, no. 4 (August 1999): 271–78. http://dx.doi.org/10.1006/rtim.1998.0140.

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Zhang, Zhaoyue, An Zhang, Cong Sun, Shuaida Xiang, and Shanmei Li. "Data-Driven Analysis of the Chaotic Characteristics of Air Traffic Flow." Journal of Advanced Transportation 2020 (September 18, 2020): 1–17. http://dx.doi.org/10.1155/2020/8830731.

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Understanding the chaos of air traffic flow is significant to the achievement of advanced air traffic management, and trajectory data are the basic material for studying the chaotic characteristics. However, at present, there are two main obstacles to this task, namely, large amounts of noise in the measured data and the tedium of existing data processing methods. This paper improves the incorrect trajectory processing method based on ADS-B trajectory data and proposes a method by which to quickly extract the traffic flow through a certain waypoint. Currently, the commonly used theoretical analysis tools for nonlinear complex systems include the classical nonlinear dynamics analysis method and the newly developed complex network-based analysis method. The latter is currently in an exploratory stage because it has just been introduced into the study of air traffic flow. From these two perspectives, the chaotic characteristics of air traffic flow are studied in the present work. From the perspective of nonlinear dynamics, the improved C-C method is used to calculate the reliability parameters, namely, the time delay τ and embedding dimension m, of phase-space reconstruction, and the maximum Lyapunov index is calculated by using the small data volume method to prove the existence of chaos in the system. From the perspective of complex networks, the construction of a visibility graph and horizontal visibility graph is used to prove the existence of chaos in the system, and the goodness-of-fit parameters of the degree distributions of two fitting methods under different time scales are evaluated, which provides support for the air traffic flow theory.

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Rao, G. Madhukar, and Dharavath Ramesh. "Parallel CNN based big data visualization for traffic monitoring." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 2679–91. http://dx.doi.org/10.3233/jifs-190601.

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In a real-time application such as traffic monitoring, it is required to process the enormous amount of data. Traffic prediction is essential for intelligent transportation systems (ITSs), traffic management authorities, and travelers. Traffic prediction has become a challenging task due to various non-linear temporal dynamics at different locations, complicated underlying spatial dependencies, and more extended step forecasting. To accommodate these instances, efficient visualization and data mining techniques are required to predict and analyze the massive amount of traffic big data. This paper presents a deep learning-based parallel convolutional neural network (Parallel-CNN) methodology to predict the traffic conditions of a specific region. The methodology of deep learning contains multiple processing layers and performs various computational strategies, which is used to learn representations of data with multilevel abstraction. The data has captured from the department of transportation; thus, the size of data is vast, and it can be analyzed to get the behavior of the traffic condition. The purpose of this paper is to monitor traffic behavior, which enables the user to make decisions to build the traffic-free cities. Experimental results show that the proposed methodology outperforms other existing methods such as KNN, CNN, and FIMT-DD.

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Wu, Jianqing, Hao Xu, Bin Lv, Rui Yue, and Yang Li. "Automatic Ground Points Identification Method for Roadside LiDAR Data." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (May 8, 2019): 140–52. http://dx.doi.org/10.1177/0361198119843869.

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Roadside light detection and ranging (LiDAR) provides a solution to fill the data gap under mixed traffic situations. The real-time high-resolution micro traffic data (HRMTD) of all road users from the roadside LiDAR sensor provides a new opportunity to serve the connected-vehicle system during the transition period from unconnected vehicles to connected vehicles. Ground surface identification is the basic data processing step for HRMTD collection. The current ground points identification algorithms based on airborne and mobile LiDAR do not work for roadside LiDAR. A novel algorithm is developed in this paper to identify and exclude ground points based on the features of LiDAR, terrain, and point density in the space. The scan feature of different beams is used to search ground points. The whole procedure can be divided into four major parts: points clustering in each beam, slope-based filtering, shape-based filtering, and ground points matrix extraction. The proposed algorithm was evaluated using the real-world LiDAR data collected at different scenarios. The results showed that this algorithm can be used for ground points exclusion under different situations (differing terrain types, weather situations, and traffic volumes) with high accuracy. This algorithm was compared with previously developed algorithms. The overall performance of the proposed algorithm is superior. The low computational load guarantees this method may be applied for real-time data processing.

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Zhang, Jia-Dong, Jin Xu, and Stephen Shaoyi Liao. "Aggregating and Sampling Methods for Processing GPS Data Streams for Traffic State Estimation." IEEE Transactions on Intelligent Transportation Systems 14, no. 4 (December 2013): 1629–41. http://dx.doi.org/10.1109/tits.2013.2264753.

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32

Khan, Muhammad Arsalan, Wim Ectors, Tom Bellemans, Davy Janssens, and Geert Wets. "Unmanned Aerial Vehicle–Based Traffic Analysis: Methodological Framework for Automated Multivehicle Trajectory Extraction." Transportation Research Record: Journal of the Transportation Research Board 2626, no. 1 (January 2017): 25–33. http://dx.doi.org/10.3141/2626-04.

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Abstract (sommario):

Unmanned aerial vehicles (UAVs), commonly referred to as drones, are one of the most dynamic and multidimensional emerging technologies of the modern era. This technology has recently found multiple potential applications within the transportation field, ranging from traffic surveillance applications to traffic network analysis. To conduct a UAV-based traffic study, extremely diligent planning and execution are required followed by an optimal data analysis and interpretation procedure. In this study, however, the main focus was on the processing and analysis of UAV-acquired traffic footage. A detailed methodological framework for automated UAV video processing is proposed to extract the trajectories of multiple vehicles at a particular road segment. Such trajectories can be used either to extract various traffic parameters or to analyze traffic safety situations. The proposed framework, which provides comprehensive guidelines for an efficient processing and analysis of a UAV-based traffic study, comprises five components: preprocessing, stabilization, georegistration, vehicle detection and tracking, and trajectory management. Until recently, most traffic-focused UAV studies have employed either manual or semiautomatic processing techniques. In contrast, this paper presents an in-depth description of the proposed automated framework followed by a description of a field experiment conducted in the city of Sint-Truiden, Belgium. Future research will mainly focus on the extension of the applications of the proposed framework in the context of UAV-based traffic monitoring and analysis.

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Altintasi, Oruc, Hediye Tuydes-Yaman, and Kagan Tuncay. "A METHOD TO ESTIMATE TRAFFIC PENETRATION RATES OF COMMERCIAL FLOATING CAR DATA USING SPEED INFORMATION." Transport 37, no. 3 (August 5, 2022): 161–76. http://dx.doi.org/10.3846/transport.2022.17069.

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Abstract (sommario):

Floating Car Data (FCD) are being increasingly used as an alternative traffic data source due to its lower cost and high coverage area. FCD can be obtained by tracking vehicle trajectories individually or by processing multiple tracks anonymously to produce average speed information commercially. For commercial FCD, the spatio-temporal distribution of these vehicles in actual traffic, traffic Penetration Rate (PR) is the most important factor affecting the accuracy of speed estimations, despite the high number of registered vehicles feeding to an FCD provider, denoting the market PR. This study proposes a method for assessing the traffic PR of commercial FCD by evaluating its speed estimation quality compared to Ground Truth (GT) data. GT speed data were employed to generate different levels of traffic PR using Monte Carlo (MC) simulations, which resulted in the development of Quality-PR (Q-PR) relations for Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) as selected Measures of Effectiveness (MoE). Simulation-based FCD results at an urban road segment in Ankara (Turkey) showed that a quality of FCD with traffic PR of 15% or more would improve significantly. Use of the developed Q-PR relations suggested an approximately 5% traffic PR for the commercial FCD speeds at the location.

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Dabhade, Kiran Bhimrao, and C. M. Mankar. "An Optimization Framework of Adaptive Computing-plus-Communication for Multimedia Processing in Cloud: A Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2872–76. http://dx.doi.org/10.22214/ijraset.2022.42887.

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Abstract: Clear trend within the evolution of network-based services is that the ever-increasing amount of multimedia system data concerned. This trend towards big-data multimedia system process finds its natural placement at the side of the adoption of the cloud computing paradigm, that looks the most effective solution to the strain of a extremely fluctuating work that characterizes this sort of services. However, as cloud data centers become a lot of and a lot of powerful, energy consumption becomes a significant challenge each for environmental concerns and for economic reasons. An effective approach to improve energy efficiency in cloud data centers is to rely on traffic engineering techniques to dynamically adapt the number of active servers to the current workload. Towards this aim, we propose a joint computing-plus-communication improvement framework exploiting virtualization technologies. Our proposal specifically addresses the everyday situation of data processing processing with computationally intensive tasks and exchange of a giant volume of data. The proposed framework not only ensures users the Quality of Service, but also achieves maximum energy saving and attains green cloud computing goals in a fully distributed fashion by utilizing the DVFS-based CPU frequencies Keywords: Energy efficiency, Multimedia data processing, Cloud resource management, Load balancing, Dynamic voltage and frequency scaling (DVFS), Traffic engineering

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35

Xu, Jie, Dingxiong Deng, Ugur Demiryurek, Cyrus Shahabi, and Mihaela van der Schaar. "Mining the Situation: Spatiotemporal Traffic Prediction With Big Data." IEEE Journal of Selected Topics in Signal Processing 9, no. 4 (June 2015): 702–15. http://dx.doi.org/10.1109/jstsp.2015.2389196.

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36

Raju, Narayana, Pallav Kumar, Aayush Jain, Shriniwas S. Arkatkar, and Gaurang Joshi. "Application of Trajectory Data for Investigating Vehicle Behavior in Mixed Traffic Environment." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 43 (July 31, 2018): 122–33. http://dx.doi.org/10.1177/0361198118787364.

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Abstract (sommario):

The research work reported here investigates driving behavior under mixed traffic conditions on high-speed, multilane highways. With the involvement of multiple vehicle classes, high-resolution trajectory data is necessary for exploring vehicle-following, lateral movement, and seeping behavior under varying traffic flow states. An access-controlled, mid-block road section was selected for video data collection under varying traffic flow conditions. Using a semi-automated image processing tool, vehicular trajectory data was developed for three different traffic states. Micro-level behavior such as lateral placement of vehicles as a function of speed, instant responses, vehicle-following behavior, and hysteresis phenomenon were evaluated under different traffic flow states. It was found that lane-wise behavior degraded with increase in traffic volume and vehicles showed a propensity to move towards the median at low flow and towards the curb-side at moderate and heavy flows. Further, vehicle-following behavior was also investigated and it was found that with increase in flow level, vehicles are more inclined to mimic the leader vehicle’s behavior. In addition to following time, perceiving time of subject vehicle for different leading vehicles was also evaluated for different vehicle classes. From the analysis, it was inferred that smaller vehicles are switching their leader vehicles more often to escape from delay, resulting in less following and perceiving time and aggressive gap acceptance. The present research work reveals the need for high-quality, micro-level data for calibrating driving behavior models under mixed traffic conditions.

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Zhang, Zheyuan, Jianying Zheng, Yanyun Tao, Yang Xiao, Shumei Yu, Sultan Asiri, Jiacheng Li, and Tieshan Li. "Traffic Sign Based Point Cloud Data Registration with Roadside LiDARs in Complex Traffic Environments." Electronics 11, no. 10 (May 13, 2022): 1559. http://dx.doi.org/10.3390/electronics11101559.

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Abstract (sommario):

The intelligent road is an important component of the intelligent vehicle infrastructure cooperative system, the latest development of intelligent transportation systems. As an advanced sensor, Light Detection and Ranging (LiDAR) has gradually been used to collect high-resolution micro-traffic data on the roadside of intelligent roads. Furthermore, a fusion of multiple LiDARs has become a current hot spot to extend the data collection range and improve detection accuracy. This paper focuses on point cloud registration in a complex traffic environment and proposes a three-dimensional (3D) registration method based on traffic signs and prior knowledge of traffic scenes. Traffic signs with their reflective films are used as reference targets to register 3D point cloud data from roadside LiDARs. The proposed method consists of a vertical registration and a horizontal registration. For the vertical registration, we propose a panel rotation algorithm to rotate the initial point cloud to register it vertically, converting the 3D point cloud registration into a two-dimensional (2D) rigid body transformation. For the vertical registration, our system registers traffic signs from different LiDARs. Our method has been verified in some actual scenarios. Compared with previous methods, the proposed method is automatic and does not need to search reference targets manually. Furthermore, it is suitable for actual engineering use and can be applied to sparse point cloud data from LiDAR with few beams, realizing point cloud registration of large disparity.

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Burinskienė, Marija, Denis Kapski, Valery Kasyanik, Anton Pashkevich, Aleksandra Volynets, and Oleg Kaptsevich. "Estimating Parameters for Traffic Flow Using Navigation Data on Vehicles." Baltic Journal of Road and Bridge Engineering 15, no. 4 (September 28, 2020): 1–21. http://dx.doi.org/10.7250/bjrbe.2020-15.492.

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The article describes the method for estimating transport flow parameters using the two-fluid Herman-Prigogine mathematical model developed considering the proposed method of estimating parameters for the system based on the passive processing of navigation data on the movement of vehicles. The efficiency of the suggested algorithms and mathematical models for estimating road traffic flow parameters and the system as a whole was confirmed performing tests using a set of tracks on the main highways of Belarus.

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Kim, Jong Kwan. "Semi-Continuous Spatial Statistical Analysis Using AIS Data for Vessel Traffic Flow Characteristics in Fairway." Journal of Marine Science and Engineering 9, no. 4 (April 2, 2021): 378. http://dx.doi.org/10.3390/jmse9040378.

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Abstract (sommario):

As high vessel traffic in fairways is likely to cause frequent marine accidents, understanding vessel traffic flow characteristics is necessary to prevent marine accidents in fairways. Therefore, this study conducted semi-continuous spatial statistical analysis tests (the normal distribution test, kurtosis test and skewness test) to understand vessel traffic flow characteristics. First, a vessel traffic survey was conducted in a designated area (Busan North Port) for seven days. The data were collected using an automatic identification system and subsequently converted using semi-continuous processing methods. Thereafter, the converted data were used to conduct three methods of spatial statistical analysis. The analysis results revealed the vessel traffic distribution and its characteristics, such as the degree of use and lateral positioning on the fairway based on the size of the vessel. In addition, the generalization of the results of this study along with that of further studies will aid in deriving the traffic characteristics of vessels on the fairway. Moreover, these characteristics will reduce maritime accidents on the fairway, in addition to establishing the foundation for research on autonomous ships.

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40

Wang, Hang, Yunfeng Chen, Rui Min, and Yangkang Chen. "Urban DAS Data Processing and Its Preliminary Application to City Traffic Monitoring." Sensors 22, no. 24 (December 18, 2022): 9976. http://dx.doi.org/10.3390/s22249976.

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Abstract (sommario):

Distributed acoustic sensing (DAS) is an emerging technology for recording vibration signals via the optical fibers buried in subsurface conduits. Its relatively easy-to-deploy and high spatial and temporal sampling characteristics make DAS an appealing tool to record seismic wavefields at higher quantity and quality than traditional geophones. Considering that the usage of optical fibers in the urban environment has drawn relatively less attention aside from its functionality as a telecommunication cable, we examine its ability to record seismic signals and investigate its preliminary application in city traffic monitoring. To solve the problems that DAS signals are prone to a variety of environmental noise and are generally of weak amplitude compared to noise, we propose a fast workflow for real-time DAS data processing, which can enhance the detection of regular car signals and suppress the other components. We conduct a DAS experiment in Hangzhou, China, a typical metropolitan area that can provide us with a rich data library to validate our DAS data-processing workflow. The well-processed data enable us to extract their slope and coherency attributes that can provide an estimate of real traffic situations. The one-minute (with video validations) and 24 h statistics of these attributes show that the speed and volume of car flow are well correlated demonstrates the robustness of the proposed data processing workflow and great potential of DAS for city traffic monitoring with high precision and convenience. However, challenges also exist in view that all the attributes are statistically analyzed based on the behaviors of a large number of cars, which is meaningful but lacking in precision. Therefore, we suggest developing more quantitative processing and analyzing methods to provide precise information on individual cars in future works.

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Jin, Shaojie, Ying Gao, Shoucai Jing, Fei Hui, Xiangmo Zhao, and Jianzhen Liu. "Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion." Journal of Advanced Transportation 2021 (August 15, 2021): 1–14. http://dx.doi.org/10.1155/2021/4592124.

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Abstract (sommario):

Accurate traffic flow parameters are the supporting data for analyzing traffic flow characteristics. Vehicle detection using traffic surveillance pictures is a typical method for gathering traffic flow characteristics in urban traffic scenes. In complicated lighting conditions at night, however, neither classical nor deep-learning-based image processing algorithms can provide adequate detection results. This study proposes a fusion technique combining millimeter-wave radar data with image data to compensate for the lack of image-based vehicle detection under complicated lighting to complete all-day parameters collection. The proposed method is based on an object detector named CenterNet. Taking this network as the cornerstone, we fused millimeter-wave radar data into it to improve the robustness of vehicle detection and reduce the time-consuming postcalculation of traffic flow parameters collection. We collected a new dataset to train the proposed method, which consists of 1000 natural daytime images and 1000 simulated nighttime images with a total of 23094 vehicles counted, where the simulated nighttime images are generated by a style translator named CycleGAN to reduce labeling workload. Another four datasets of 2400 images containing 20161 vehicles were collected to test the proposed method. The experimental results show that the method proposed has good adaptability and robustness at natural daytime and nighttime scenes.

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42

Oh, Cheol, Stephen G. Ritchie, and Jun-Seok Oh. "Exploring the Relationship between Data Aggregation and Predictability to Provide Better Predictive Traffic Information." Transportation Research Record: Journal of the Transportation Research Board 1935, no. 1 (January 2005): 28–36. http://dx.doi.org/10.1177/0361198105193500104.

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Abstract (sommario):

Providing reliable predictive traffic information is a crucial element for successful operation of intelligent transportation systems. However, there are difficulties in providing accurate predictions mainly because of limitations in processing data associated with existing traffic surveillance systems and the lack of suitable prediction techniques. This study examines different aggregation intervals to characterize various levels of traffic dynamic representations and to investigate their effects on prediction accuracy. The relationship between data aggregation and predictability is explored by predicting travel times obtained from the inductive signature–based vehicle reidentification system on the I-405 freeway detector test bed in Irvine, California. For travel time prediction, this study employs three techniques: adaptive exponential smoothing, adaptive autoregressive model using Kalman filtering, and recurrent neural network with genetically optimized parameters. Finally, findings are discussed on suggestions for applying prediction techniques effectively.

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Zhang, Yuanqiang, and Weifeng Li. "Dynamic Maritime Traffic Pattern Recognition with Online Cleaning, Compression, Partition, and Clustering of AIS Data." Sensors 22, no. 16 (August 22, 2022): 6307. http://dx.doi.org/10.3390/s22166307.

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Abstract (sommario):

Maritime traffic pattern recognition plays a major role in intelligent transportation services, ship monitoring, route planning, and other fields. Facilitated by the establishment of terrestrial networks and satellite constellations of the automatic identification system (AIS), large quantities of spatial and temporal information make ships’ paths trackable and are useful in maritime traffic pattern research. The maritime traffic pattern may vary with changes in the traffic environment, so the recognition method of the maritime traffic pattern should be adaptable to changes in the traffic environment. To achieve this goal, a dynamic maritime traffic pattern recognition method is presented using AIS data, which are cleaned, compressed, partitioned, and clustered online. Old patterns are removed as expired trajectories are deleted, and new patterns are created as new trajectories are added. This method is suitable for processing massive stream data. Experiments show that when the marine traffic route changes due to the navigation environment, the maritime traffic pattern adjusts automatically.

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Tang, Jing, Xueyan Tang, and Junsong Yuan. "Traffic-Optimized Data Placement for Social Media." IEEE Transactions on Multimedia 20, no. 4 (April 2018): 1008–23. http://dx.doi.org/10.1109/tmm.2017.2760627.

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Sun, Yuan, Hao Xu, Jianqing Wu, Elie Y. Hajj, and Xinli Geng. "Data Processing Framework for Development of Driving Cycles with Data from SHRP 2 Naturalistic Driving Study." Transportation Research Record: Journal of the Transportation Research Board 2645, no. 1 (January 2017): 50–56. http://dx.doi.org/10.3141/2645-06.

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In the modeling of vehicle operation costs, a driving cycle is a representative speed–time profile to describe the speed–acceleration pattern of a specific road scenario. Driving cycles are important input for estimation of fuel consumption and polluting emissions. Existing driving cycles are either from out-of-date driving data or without detailed consideration of influencing road properties because of the limitations of available data sets. As part of a project sponsored by FHWA, this research developed a data processing framework for development of driving cycles with data from both the SHRP 2 Naturalistic Driving Study (NDS) and the SHRP 2 Roadway Information Database (RID). The framework included data processing of NDS and RID data and a new synthetic optimization method to generate optimized representative driving cycles. The documented data processing framework was applied to develop the driving cycles of light-duty vehicles for 395 road scenarios with consideration of 10 road properties that could have influenced traffic speed patterns. The 4,400 NDS trips, each of which was at least 20 min long, were used for the development of driving cycles. This data processing frame can be applied for development of driving cycles for more road scenarios with data similar to those in the SHRP 2 database.

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Zang, Di, Yongjie Ding, Xiaoke Qu, Chenglin Miao, Xihao Chen, Junqi Zhang, and Keshuang Tang. "Traffic-Data Recovery Using Geometric-Algebra-Based Generative Adversarial Network." Sensors 22, no. 7 (April 2, 2022): 2744. http://dx.doi.org/10.3390/s22072744.

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Abstract (sommario):

Traffic-data recovery plays an important role in traffic prediction, congestion judgment, road network planning and other fields. Complete and accurate traffic data help to find the laws contained in the data more efficiently and effectively. However, existing methods still have problems to cope with the case when large amounts of traffic data are missed. As a generalization of vector algebra, geometric algebra has more powerful representation and processing capability for high-dimensional data. In this article, we are thus inspired to propose the geometric-algebra-based generative adversarial network to repair the missing traffic data by learning the correlation of multidimensional traffic parameters. The generator of the proposed model consists of a geometric algebra convolution module, an attention module and a deconvolution module. Global and local data mean squared errors are simultaneously applied to form the loss function of the generator. The discriminator is composed of a multichannel convolutional neural network which can continuously optimize the adversarial training process. Real traffic data from two elevated highways are used for experimental verification. Experimental results demonstrate that our method can effectively repair missing traffic data in a robust way and has better performance when compared with the state-of-the-art methods.

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Cai, Xiaoyu, Cailin Lei, Bo Peng, Xiaoyong Tang, and Zhigang Gao. "Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data." Journal of Advanced Transportation 2020 (February 26, 2020): 1–13. http://dx.doi.org/10.1155/2020/3024101.

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Abstract (sommario):

Currently, research on road traffic safety is mostly focused on traffic safety evaluations based on statistical indices for accidents. There is still a need for in-depth investigation on preaccident identification of safety risks. In this study, the correlations between high-incidence locations for aberrant driving behaviors and locations of road traffic accidents are analyzed based on vehicle OBD data. A road traffic safety risk estimation index system with road traffic safety entropy (RTSE) as the primary index and rapid acceleration frequency, rapid deceleration frequency, rapid turning frequency, speeding frequency, and high-speed neutral coasting frequency as secondary indices is established. A calculation method of RTSE is proposed based on an improved entropy weight method. This method involves three aspects, namely, optimization of the base of the logarithm, processing of zero-value secondary indices, and piecewise calculation of the weight of each index. Additionally, a safety risk level determination method based on two-step clustering (density and k-means clustering) is also proposed, which prevents isolated data points from affecting safety risk classification. A risk classification threshold calculation method is formulated based on k-mean clustering. The results show that high-incidence locations for aberrant driving behaviors are consistent with the locations of traffic accidents. The proposed methods are validated through a case study on four roads in Chongqing with a total length of approximately 38 km. The results show that the road traffic safety trends characterized by road safety entropy and traffic accidents are consistent.

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Pal, Dibyendu, and Mallikarjuna Chunchu. "Smoothing of vehicular trajectories under heterogeneous traffic conditions to extract microscopic data." Canadian Journal of Civil Engineering 45, no. 6 (June 2018): 435–45. http://dx.doi.org/10.1139/cjce-2017-0452.

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Abstract (sommario):

Trajectory data collected using video image processing techniques are prone to noise. Trajectory data extracted using commercially available video image processing software (TRAZER) contains the noise associated with the false detection in addition to the white noise. This paper proposes a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to smooth such trajectory data. In this approach, trajectory data are decomposed into a finite number of intrinsic modes and a unique residue is computed to obtain each mode. This monotonic residue gives the smoothed trajectory. The instantaneous speeds of the vehicles are then estimated using the method of continuous wavelet transforms, discrete wavelet transforms, and numerical differentiation. Internal consistency analyses show that the wavelet transforms methods are effective in reducing the noise amplification of the speed profile. It was also observed that the corrections applied on trajectory data have a significant effect on macroscopic traffic relations.

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Du, Yuchuan, Cong Zhao, Feng Li, and Xuefeng Yang. "An Open Data Platform for Traffic Parameters Measurement via Multirotor Unmanned Aerial Vehicles Video." Journal of Advanced Transportation 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/8324301.

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Abstract (sommario):

Multirotor unmanned aerial vehicle video observation can obtain accurate information about traffic flow of large areas over extended times. This paper aims to construct an open data test platform for updated traffic data accumulation and traffic simulation model verification by analyzing real time aerial video. Common calibration boards were used to calibrate internal camera parameters and image distortion correction was performed using a high-precision distortion model. To solve external parameters calibration problems, an existing algorithm was improved by adding two sets of orthogonal equations, achieving higher accuracy with only four calibrated points. A simplified algorithm is proposed to calibrate cameras by calculating the relationship between pixel and true length under the camera optical axis perpendicular to road conditions. Aerial video (160 min) from the Shanghai inner ring expressway was collected and real time traffic parameter values were obtained from analyzing and processing the aerial visual data containing spatial, time, velocity, and acceleration data. The results verify that the proposed platform provides a reasonable and objective approach to traffic simulation model verification and improvement. The proposed data platform also offers significant advantages over conventional methods that use historical and outdated data to run poorly calibrated traffic simulation models.

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Liu, Zhou-zhou, and Shi-ning Li. "Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing." International Journal of Distributed Sensor Networks 14, no. 9 (September 2018): 155014771880225. http://dx.doi.org/10.1177/1550147718802259.

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Abstract (sommario):

The emergence of sensor-cloud system has completely changed the one-to-one service mode of traditional wireless sensor networks, and it greatly expands the application field of wireless sensor networks. As the high delay of large-scale data processing tasks in sensor-cloud, a sensor-cloud data acquisition scheme based on fog computing and adaptive block compressive sensing is proposed. First, the sensor-cloud framework based on fog computing is constructed, and the fog computing layer includes many wireless mobile nodes, which helps to realize the implementation of information transfer management between lower wireless sensor networks layer and upper cloud computing layer. Second, in order to further reduce network traffic and improve data processing efficiency, an adaptive block compressed sensing data acquisition strategy is proposed in the lower wireless sensor networks layer. By dynamically adjusting the size of the network block and building block measurement matrix, the implementation of sensor compressed sensing data acquisition is achieved; in order to further balance the lower wireless sensor networks’ node energy consumption, reduce the time delay of data processing task in fog computing layer, the mobile node data acquisition path planning strategy and multi-mobile nodes collaborative computing system are proposed. Through the introduction of the fitness value constraint transformation processing technique and parallel discrete elastic collision optimization algorithm, the efficient processing of the fog computing layer data is realized. Finally, the simulation results show that the sensor-cloud data acquisition scheme can effectively achieve large-scale sensor data efficient processing. Moreover, compared with cloud computing, the network traffic is reduced by 20% and network task delay is reduced by 12.8%–20.1%.

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