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Articoli di riviste sul tema "Traffic engineering – Data processing"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 30 gennaio 2023

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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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2

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. T

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5

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

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6

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 includin

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7

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. Firs

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8

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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9

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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10

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 traff

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11

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 m

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13

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

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15

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 fro

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16

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

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17

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 fi

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18

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 l

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19

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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20

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.

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21

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 dee

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22

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 organizati

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23

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 theoretic

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24

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 th

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25

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 strate

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26

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 re

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27

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 ana

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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 pa

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30

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 develop

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31

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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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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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

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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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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 stud

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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 effecti

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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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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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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

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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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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) regis

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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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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 pr

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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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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 applica

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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 al

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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

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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 envi

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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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45

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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Abstract (sommario):

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 SHR

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46

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

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47

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 fre

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48

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

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49

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 equat

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50

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 managem

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