Journal articles on the topic 'Transportation engineering – Namibia – Data processing'

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1

Howell, Tommie F. "Automation for Transportation—More Than Data Processing." Journal of Transportation Engineering 116, no. 6 (1990): 831–35. http://dx.doi.org/10.1061/(asce)0733-947x(1990)116:6(831).

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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 (1997): 298–307. http://dx.doi.org/10.1061/(asce)0733-947x(1997)123:4(298).

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3

Robinson, Steve, Baskaran Narayanan, Nelson Toh, and Francisco Pereira. "Methods for pre-processing smartcard data to improve data quality." Transportation Research Part C: Emerging Technologies 49 (December 2014): 43–58. http://dx.doi.org/10.1016/j.trc.2014.10.006.

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4

Roberts, Nathan, and Jerry Cap. "Automated Sorting of Mixed Mode Environment's Data." Journal of the IEST 49, no. 2 (2006): 84–92. http://dx.doi.org/10.17764/jiet.49.2.hp21k142573u0011.

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Transportation of sensitive flight hardware requires information about the expected transportation environment as well as the actual transportation environment during the part's movement—typically vibration with superimposed intermittent shocks. Each data type has different sampling, processing, and specification requirements. Analyzing shock data requires high sampling rates and leads to large file sizes. A barrier to analyzing data has been the vast quantity of information acquired. Previous approaches have focused either on manually separating data or on selectively recording extreme data. The use of an automated approach allows for quickly verifying vibration and shock levels while retaining the robustness of the underlying data set. Further, the automated approach allows the environments engineer to select criteria for shock/vibration sorting, which removes the subjectivity associated with visual differentiation. This automated technique evaluated several vehicles over four different road conditions in the same time that one data set could have been processed using visual discrimination. Automated processing of satellite shipment vibration and shock data is made thoroughly and objectively vs. traditional shock and tilt indicators. The automated technique could also be useful in processing large amounts of on-orbit data for changes in vibration signature.
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5

Hao, Qian, and Lele Qin. "The Design of Intelligent Transportation Video Processing System in Big Data Environment." IEEE Access 8 (2020): 13769–80. http://dx.doi.org/10.1109/access.2020.2964314.

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6

Niture, Deeplaxmi V., Vivekanand Dhakane, Piyush Jawalkar, and Ankit Bamnote. "Smart Transportation System using IOT." International Journal of Engineering and Advanced Technology 10, no. 5 (2021): 434–38. http://dx.doi.org/10.35940/ijeat.e2870.0610521.

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In this paper a Smart Vehicle Assistance and Monitoring system (SVAMS) is presented. SVAMS is an intelligent transportation system (ITS), developed to tackle various traffic related issues. It is a traffic management, monitoring and optimization solution in which all the vehicles are interconnected through Zigbee and are monitored and assisted centrally, by a data center. The system has two parts; one part is mounted in/on the vehicle and the other part is at the data centre. Part one collects data from various sensors and transmits it to central data centre. All the data will be stored on cloud for further analysis, processing and future use. SVAMS is relatively low-cost, compact and has various functionalities such as emergency response, pollution level monitoring, automatic toll collection, traffic rule violation detection, vehicle tracking, etc. The use of SVAMS will help to build up Clean, Corruption free and Crime free (C-3) cities.
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7

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 (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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Narasimhamurthy, S. B., P. C. Gurumohan, S. Sreenivasamurthy, and J. Y. Hui. "Quanta data storage: an information processing and transportation architecture for storage area networks." IEEE Journal on Selected Areas in Communications 23, no. 10 (2005): 2032–40. http://dx.doi.org/10.1109/jsac.2005.854128.

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9

Bassani, Marco, and Lorenzo Mussone. "Experimental analysis of operational data for roundabouts through advanced image processing." Journal of Traffic and Transportation Engineering (English Edition) 7, no. 4 (2020): 482–97. http://dx.doi.org/10.1016/j.jtte.2019.01.005.

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10

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 (2009): 174–82. http://dx.doi.org/10.1061/(asce)0733-947x(2009)135:4(174).

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11

Wang, Feilong, and Cynthia Chen. "On data processing required to derive mobility patterns from passively-generated mobile phone data." Transportation Research Part C: Emerging Technologies 87 (February 2018): 58–74. http://dx.doi.org/10.1016/j.trc.2017.12.003.

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12

Liu, Guo Xiang. "Applications of Cloud Calculation in the Intelligence Transportation System." Advanced Materials Research 651 (January 2013): 917–21. http://dx.doi.org/10.4028/www.scientific.net/amr.651.917.

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Intelligence transportation system is a new kind of information, intelligence and socialization modern transportation system. Cloud computing is a shared data infrastructure method emerged in recent years, which is able to combine the large procedure pool and the data pool to provide a variety of IT application services. The intelligent transportation systems using cloud calculation technology, on the one hand, can integrate multiple heterogeneous, distributed data sources; on the other hand it is formed by multiple server clusters, providing the basic framework of storage, transmission, processing, integration of massive data. In this paper, by studying the applications of cloud calculation technology in the field of intelligence transportation, the author proposed the ITS platform based on cloud calculation.
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13

Roman Čerešňák, Karol Matiaško, and Adam Dudáš. "Various Approaches Proposed for Eliminating Duplicate Data in a System." Communications - Scientific letters of the University of Zilina 23, no. 4 (2021): A223—A232. http://dx.doi.org/10.26552/com.c.2021.4.a223-a232.

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The growth of big data processing market led to an increase in the overload of computation data centers, change of methods used in storing the data, communication between the computing units and computational time needed to process or edit the data. Methods of distributed or parallel data processing brought new problems related to computations with data which need to be examined. Unlike the conventional cloud services, a tight connection between the data and the computations is one of the main characteristics of the big data services. The computational tasks can be done only if relevant data are available. Three factors, which influence the speed and efficiency of data processing are - data duplicity, data integrity and data security. We are motivated to study the problems related to the growing time needed for data processing by optimizing these three factors in geographically distributed data centers.
 
 
 
 
 
 
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14

Bertini, Robert L., Steve Hansen, Andrew Byrd, and Thareth Yin. "Experience Implementing a User Service for Archived Intelligent Transportation Systems Data." Transportation Research Record: Journal of the Transportation Research Board 1917, no. 1 (2005): 90–99. http://dx.doi.org/10.1177/0361198105191700111.

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In cooperation with the Oregon Department of Transportation (ODOT) and other regional partners, the Portland regional intelligent transportation systems (ITSs) data archive was recently inaugurated via a direct fiber-optic connection between ODOT and Portland State University (PSU). In July 2004, the Portland Regional Transportation Archive Listing was activated; it received 20-s data from the 436 inductive loop detectors composing the Portland area's advanced traffic management system. PSU is designated as the region's official data archiving entity, consistent with the ITS architecture being developed. This paper discusses the steps taken for successful implementation of the Portland region's functional ITS data archive and plans for development and expansion. Included is a discussion of the archive structure, data storage, data processing, and user interface. An experiment involving Metro, the Portland region's metropolitan planning organization, demonstrates that archived loop detector data can be used to improve travel demand forecasts for the Portland region. The data archive will expand to include transit data, freeway incident data, city traffic signal data, and truck weigh-in-motion data.
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15

Zhang, Xiang. "The Rules of Beijing Metro Passenger Flow Based on AFC Data." Advanced Materials Research 962-965 (June 2014): 2823–26. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.2823.

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With the development of rail transportation in many cities, how to build efficient, safe and comfortable operation network of urban development has become a major problem to be solved in rail transportation. Beijing subway network has been relatively mature, and the Beijing subway passenger traffic inherent rules has an important significance in other cities in the development of rail transit.Based on the Beijing subway AFC data, this paper is around the Beijing subway passenger laws and researches on the systematic work, aiming at finding its inherent laws with the adoption of data mining. This paper analyses the results in order to ensure the accuracy of early access to a large number of relevant documents, and makes adequate preparations on data processing
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16

Charles, Darrel. "Derivation of Environment Descriptions and Test Severities from Measured Road Transportation Data." Journal of the IEST 36, no. 1 (1993): 37–42. http://dx.doi.org/10.17764/jiet.2.36.1.k285j24551552377.

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This paper discusses the vibration and shock environments perceived by equipment when it is transported by wheeled vehicles. Methodologies are presented for deriving environment descriptions and test severities from measured road transportation dynamic response data. Measured responses from a data-gathering trial are used to illustrate a number of useful signal-processing techniques and the compilation of environment descriptions that quantify the severity and character of measured data. The environment description is used as the basis for the compilation of a test severity definition in terms of a random vibration spectrum and duration.
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17

Zhao, Pengxiang, He Haitao, Aoyong Li, and Ali Mansourian. "Impact of data processing on deriving micro-mobility patterns from vehicle availability data." Transportation Research Part D: Transport and Environment 97 (August 2021): 102913. http://dx.doi.org/10.1016/j.trd.2021.102913.

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18

Litvinenko, Michail, and Ramūnas Palšaitis. "METHODOLOGICAL ASPECTS OF A QUESTIONNAIRE RESEARCHING TRANSPORTATION LINKS." TRANSPORT 20, no. 2 (2005): 78–82. http://dx.doi.org/10.3846/16484142.2005.9638000.

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Changes of foreign trade conditions also bring changes of the main transport routes and directions. Lithuanian transport companies must perfect their ability to adapt in changing environment, to predict and reorient to the possible changes in the flow of goods, to observe changes and tendencies, in short to build a new foundation and appliance of transportation technologies. To aid this a questionnaire researching transport must be made. This article covers the analysis of the planning and conducting of the research and the data processing methodology used to determine the main directions of the flow of goods of Lithuanian transport companies as well as the criteria concerning competitiveness among the companies.
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19

Cottrell, Wayne D., and Dharminder Pal. "Evaluation of Pedestrian Data Needs and Collection Efforts." Transportation Research Record: Journal of the Transportation Research Board 1828, no. 1 (2003): 12–19. http://dx.doi.org/10.3141/1828-02.

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Research was done to evaluate the extent to which the pedestrian data collection efforts of transportation agencies in the United States are addressing pedestrian safety factors. There was also consideration of how pedestrian data collection can be improved to facilitate the monitoring of these factors. Fifteen pedestrian safety issues are identified based on a literature review and examination of pedestrian–vehicle crashes in Utah. A 2001 survey of U.S. transportation agencies indicated that 45 (75%) of the 60 respondents were counting pedestrians at various locations. Hand counting, the recording of push-button use, and video cameras were methods used to collect data. Automated systems, such as position sensors and image processing, were not used to count pedestrians. The use of advanced data collection technologies is not critical to the resolution of pedestrian safety concerns, although permanent counting installations might increase data collection efficiency. Only 4 of the 15 pedestrian safety issues were specifically being addressed by the agencies’ data collection efforts. Their existing methods could, however, be used to target seven additional safety factors. The development of a pedestrian data monitoring guide is recommended; an outline is proposed. Several agencies admitted that pedestrian volumes did not affect their pedestrian treatments. Evidently, some transportation agencies could benefit from direction on how to relate pedestrian demand and behavior data to safety improvements.
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20

Grisa, Tomas, Daniel Sas, and Lubomir Gryc. "On a multidimensional data processing method for radiation portal monitors." Nuclear Technology and Radiation Protection 35, no. 3 (2020): 235–43. http://dx.doi.org/10.2298/ntrp2003235g.

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From a homeland security point of view, it is important to detect the transportation of radioactive materials or potential radioactive contamination. The most commonly used devices are radiation portal monitors with plastic scintillation detectors. A signal from such detectors is processed by an amplitude analyser which can separate pulses into several mutually independent energy windows (representing energy intervals of gamma radiation). Therefore, the most appropriate method of evaluation is by the use of algorithms for multidimensional processing. This article describes a novel generalised approach designed with respect to the properties of radiation portal monitors. It includes a description of formulas and a whole algorithm as well as the procedure for determining the appropriate critical and detection limits. The predicted probability distribution for the proposed method of calculation was verified by simulations and experimental measurements. The algorithm was also compared with a commonly used gross counting algorithm.
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21

French, L. James, David R. Martinelli, Ronald W. Eck, and Jack Pascoli. "Specifications for Automated License Plate Reading Equipment for Transportation Planning." Transportation Research Record: Journal of the Transportation Research Board 1625, no. 1 (1998): 50–56. http://dx.doi.org/10.3141/1625-07.

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Recent technological advances in computer hardware, software, and image processing have led to the development of an automated license plate reader (ALPR). This equipment was developed primarily for enforcement and security applications, such as monitoring parking garages or border crossings. Because license plate data are used in several transportation planning studies, ALPRs have the potential to increase the quality and efficiency of many typical activities of transportation planning agencies. The key performance attributes of an ALPR with respect to the specific needs of transportation planning are determined. The following general needs are investigated: ( a) the specific license plate data requirements of transportation planning studies; ( b) the effect of the equipment on traffic operations and safety; and ( c) special equipment characteristics required because of the temporary nature of transportation planning and the constraints of transportation planning agencies. In addition, an existing ALPR was tested in situations likely to be encountered in transportation planning applications. Technical specifications for an ALPR for transportation planning are developed. These specifications can be used to provide motivation and direction for the future development of an ALPR for transportation planning. Finally, the technical challenges to developing the ALPR are discussed.
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22

Han, Zhi Yong, and Zhi Gang Han. "Data Processing with Transportation Cost Allocation Method of Formwork and Scaffold Leasing Companies Based on Supply Chain." Advanced Materials Research 977 (June 2014): 520–24. http://dx.doi.org/10.4028/www.scientific.net/amr.977.520.

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The formwork and scaffold leasing companies become one of important links in the industry supply chain. The sustainable development of the company requires the alliance and the further cooperation between the leasing companies. This thesis attempts to study how to decrease the alliance cost and to achieve reasonable cost allocation through linear programming and cooperative games in a sense of transportation cost allocation. Furthermore, the thesis puts forward the data processing method of minimizing the ratio difference of cost saving. Based on this study, it can be concluded that the method is more rational for cost allocation.
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23

Cristóbal, Padrón, Quesada-Arencibia, Alayón, and García. "Data Framework for Road-Based Mass Transit Systems Data Mining Project." Proceedings 31, no. 1 (2019): 25. http://dx.doi.org/10.3390/proceedings2019031025.

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The current paradigm of intelligent transport systems (ITS) is based on the continuous observation of what is happening in the transport network and the continuous processing of data coming from these observations. This implies the handling and processing of a massive amount of data, and for this reason, data mining and big data are fields increasingly used in transportation engineering. A framework to facilitate the phases of data preparation and knowledge modeling in the context of data mining projects for road-based mass transit systems is presented in this paper. To illustrate the utility of the framework, its utilization in the analysis of travel time in a road-based mass transit system is presented as a use case.
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24

Chen, Shao Hui, and Yan Yan Chen. "An Approach on Bus Temporal Character Calculation Based on Real-Time Information Processing." Advanced Materials Research 268-270 (July 2011): 2196–200. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.2196.

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The article studies the methods of processing bus real-time information (GPS data) and calculating the temporal indexes for decision making in bus dispatching. The kalman filter algorithm is used to predict the bus arrival time. The result shows the prediction error is within 1.3 minutes and the precision is 71% by calculating the real-time information of Beijing bus intelligent transportation system.
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25

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 (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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26

Sipetas, Charalampos, Andronikos Keklikoglou, and Eric J. Gonzales. "Estimation of left behind subway passengers through archived data and video image processing." Transportation Research Part C: Emerging Technologies 118 (September 2020): 102727. http://dx.doi.org/10.1016/j.trc.2020.102727.

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27

Sadek, Adel W., Gary Spring, and Brian L. Smith. "Toward More Effective Transportation Applications of Computational Intelligence Paradigms." Transportation Research Record: Journal of the Transportation Research Board 1836, no. 1 (2003): 57–63. http://dx.doi.org/10.3141/1836-08.

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While information technology has facilitated the collection of neverbefore-seen quantities of data, these data have not always provided the information needed by transportation professionals to support sound decision making. Computational intelligence (CI) has great potential to support the needs of transportation professionals. CI is a result of synergy among information processing technologies such as artificial neural networks (ANNs), fuzzy sets, and genetic algorithms. As the number of CI applications to transportation problems grows, so does the need to evaluate these systems. The issue of validating and evaluating transportation CI applications is addressed. A case study that evaluates the effectiveness of two CI paradigms, case-based reasoning and ANNs, for estimating the benefits of real-time traffic diversion is presented. The case study illustrates the need for regarding validation and evaluation as a part of the development effort and the need for tuning the design parameters of CI paradigms.
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28

Wu, Jianqing, Hao Xu, Yuan Tian, Rendong Pi, and Rui Yue. "Vehicle Detection under Adverse Weather from Roadside LiDAR Data." Sensors 20, no. 12 (2020): 3433. http://dx.doi.org/10.3390/s20123433.

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Roadside light detection and ranging (LiDAR) is an emerging traffic data collection device and has recently been deployed in different transportation areas. The current data processing algorithms for roadside LiDAR are usually developed assuming normal weather conditions. Adverse weather conditions, such as windy and snowy conditions, could be challenges for data processing. This paper examines the performance of the state-of-the-art data processing algorithms developed for roadside LiDAR under adverse weather and then composed an improved background filtering and object clustering method in order to process the roadside LiDAR data, which was proven to perform better under windy and snowy weather. The testing results showed that the accuracy of the background filtering and point clustering was greatly improved compared to the state-of-the-art methods. With this new approach, vehicles can be identified with relatively high accuracy under windy and snowy weather.
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Czerepicki, Andrzej. "Study on effectiveness of using column-oriented databases in the processing of measurement characteristics of an electric vehicle." Archives of Transport 51, no. 3 (2019): 77–84. http://dx.doi.org/10.5604/01.3001.0013.6164.

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Electric vehicles are increasingly popular means of transport. One of the most important problems of their operation is to optimize the use of a battery pack. It requires to analyze the operational characteristics of a vehicle in motion, which are stored in a database. If the measurement data are collected from many vehicles, the efficiency of their analysis is important. The objective of this article is to study the possibilities of using modern column-oriented databases in order to increase the efficiency of the analysis of selected operational characteristics of an electric vehicle. The research problem is a comparative analysis of the processing efficiency of selected measurement characteristics of an electric vehicle in relational and column-oriented data structures. Important analytical functions were formulated and recorded in the form of database queries. An experiment consisting in multiple execution of functions packages on various database structures, including a column-oriented one, was carried out. The execution time of packages and the IT system load were collected and analyzed. The analysis of the experiment results allows to conclude that the use of the column-oriented data structures made it possible to shorten the time of executing the functions analyzing the energy consumption by the electric vehicle’s drive system. Depending on the type of the analyzed characteristics of the vehicle and its method of representation in the database, a significant reduction of the analysis time compared to the relational structure was obtained. Also, a decrease in the load on the computer system during data processing on the column-oriented structures was noted. The use of the column-oriented databases in the processing and analysis of measurement operational characteristics of electric vehicles is justified and it can bring measurable effects. It should be considered that the effective-ness of solving depends on the number of the analyzed characteristics and the format of their representation in the computer.
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Kim, Yonghoon, Jun-Ho Huh, and Mokdong Chung. "Traffic Inference System Using Correlation Analysis with Various Predicted Big Data." Electronics 10, no. 3 (2021): 354. http://dx.doi.org/10.3390/electronics10030354.

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Currently, most of the transportation systems require changes to intelligent transportation systems, but most of them focus on efficient transportation rather than on improvement in human life. Sometimes, traffic systems are designed for economic value, and safety-related issues are neglected. A traffic information system that reflects various kinds of environmental information related to people’s safety must be able to reflect not only the existing economic goals but also a safe traffic environment. The traffic environment can be thought of as safety and direct information such as rainfall, including information on specific days when many people are scheduled to be gathered for certain events nearby. Intelligent transportation systems using this information can provide safety-related information for traveling to a specific area or for business trips. In addition, traffic congestion is a social problem and is directly related to a comfort life for individuals. Therefore, addressing various social and environmental factors could make human life more stable and reduce stress as a result. To do that, we need to estimate the impact on traffic based on environmental Big Data. The data can generally be divided into structured data and unstructured data. In inference, structured data analysis is relatively easy due to the precise meaning of the data. Nonetheless, it can be very difficult to predict environmentally sensitive data, such as traffic volume in intelligent transportation systems. To cope with this problem, there are a few systems for handling unstructured data to find out specific events that affect the traffic volume and improve its reliability. This paper shows that it is possible to estimate the exact volume of traffic using correlation analysis with various predicted data. Thus, we may apply this technique to the existing intelligent transportation system to predict the exact volume of traffic with environmentally sensitive data including various unstructured data.
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Jia, Furong, Lin Sun, Jiaxin Yuan, Yongping Li, and Qiang Huang. "The Business Process Reconstruction of Railway-River Combined Transportation Cloud Platform Taking China Container Export as an Example." Journal of Advanced Transportation 2021 (September 1, 2021): 1–20. http://dx.doi.org/10.1155/2021/9946458.

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In recent years, major large ports in China have realized the business informatization of rail-water intermodal transportation. However, the overall development level of intermodal transportation informatization has been restricted to a great extent due to the unbalanced development of intermodal transportation informatization in different regions, the rigid system architecture, the low degree of information sharing, and the lack of data management and analysis methods. Combined with the structure and business characteristics of intermodal transportation information systems, adopting cloud computing and Big Data technology, we propose an intermodal transportation information process with waybill as the information carrier and FPMS as the service fulcrum in this paper. Illustrated by the example of China’s container export process, this paper explores a series of key technical issues in the cloud environment, such as application management, business information sharing, and Big Data processing, at different levels of the construction of the rail water transport cloud platform, combined with its business characteristics, and makes experimental analysis on the relevant models to verify the feasibility of the reconstruction of the rail water transport cloud platform. It can provide theoretical and practical support for the development of rail water intermodal informatization in China.
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32

Ahas, Rein, Jukka M. Krisp, and Tuuli Toivonen. "Methodological aspects of using geocoded data from mobile devices in transportation research." Journal of Location Based Services 11, no. 2 (2017): 75–77. http://dx.doi.org/10.1080/17489725.2017.1427020.

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33

Meribout, M. "A New Distributed and Scalable Network Protocol Targeting Intelligent Transportation Systems." International Journal of Vehicular Technology 2011 (June 22, 2011): 1–7. http://dx.doi.org/10.1155/2011/120194.

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Vehicular networks are the major ingredients of the envisioned Intelligent Transportation Systems (ITS) concept. An important component of ITS which is currently attracting wider research focus is road traffic monitoring. The actual approaches for traffic road monitoring are characterized by longer response times and are also subject to higher processing requirements and possess high deployment costs. In this paper, we propose a completely distributed and scalable mechanism for wireless sensor network-based road traffic monitoring. The approach relies on the distributed and bidirectional exchange of traffic information between the vehicles traversing the routes and a miniature cluster head and takes into consideration both the security and reliability of data communication. In addition, the communication between nodes is collision-free since the underlined data link layer protocol relies on a heuristic time multiplexed-based protocol. The performance analysis shows that the proposed mechanism usually outperforms other algorithms for different traffic densities.
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34

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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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 (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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Han, Chunqing, and Lin Li. "Design of Wireless Sensing Virtual Cloud Desktop System Based on ARM-Linux." International Journal of Online Engineering (iJOE) 13, no. 10 (2017): 123. http://dx.doi.org/10.3991/ijoe.v13i10.7743.

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Aiming at exploring the wireless sensing network with mass processing in data collection, an embedded wireless sensor system based on ARM-Linux is put forward and designed. The whole system is mainly divided into wireless ZigBee sensing node group part, ARM data processing part, and virtual cloud desktop terminal part. Users’ terminal can be connected to the cloud system server through wired or wireless ways. And it is possible to view user system data and wireless terminal data by logging in to an individual account. The results showed that the system can be applied in a lot of fields, such as intelligent transportation, health care, smart home and so on. Based on the above findings, it is concluded that it is important for information aggregation and remote fast monitoring.
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37

Jackson, Joshua J., and Michael D. Montross. "Case Study of Transportation Benefits Using GIS in Distributed Preprocessing of Corn Stover into Crude Biobutanol." Transactions of the ASABE 64, no. 1 (2021): 161–75. http://dx.doi.org/10.13031/trans.13896.

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HighlightsTotal transportation costs were reduced by 32% to 63% with distributed biobutanol depots.An 8 km distance to the depot manifested the most desirable transportation costs.Across regions, biomass transport costs from field to depot were similar at equivalent distances.Abstract. The transportation efficiencies of centralized biomass processing facilities were compared to a proposed distributed preprocessing network with centralized refining facilities. Centralized biomass processing was defined as transport of baled corn stover directly from the field to the refinery. Distributed preprocessing with centralized refining was defined as transport of baled corn stover from the field to a biobutanol preprocessing depot and transport of completely dewatered crude biobutanol solution from the depot to a centralized refinery. For both systems, the locations of the corn fields, as identified through the cropland data layer, and of the refinery were fixed. For the distributed system, the biobutanol depot locations were variable and depended on different maximum transport distances (8 to 80 km) from the field to the depot. In this case study, site-specific transportation costs and biobutanol production capacities were developed for different agricultural regions in Kentucky. The distributed system produced a 32% to 63% reduction in total transportation cost with decreased (50% to 90%) fuel use as compared to the centralized system. The GIS transportation model demonstrated that on-farm biofuel production could be an effective means of producing biofuel and reducing transportation costs. Keywords: Biomass transport, Depot, Distributed biomass collection, GIS location-allocation, Minimize facilities, Satellite facilities.
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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 (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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Apffel, Charles, Jay Jayawardana, Asaf Ashar, Kevin Horn, Robert McLaughlin, and Anatoly Hochstein. "Freight Components in Louisiana's Statewide Intermodal Transportation Plan." Transportation Research Record: Journal of the Transportation Research Board 1552, no. 1 (1996): 32–41. http://dx.doi.org/10.1177/0361198196155200105.

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Planning procedures used in addressing freight components for Louisiana's statewide intermodal transportation plan are described. The effort was unprecedented in Louisiana, and the experience can be applied in other states. User and provider involvement, demand analysis, network analysis, and recommended policies and programs for the water, rail, and intermodal freight components are described. Although an assessment of system capacity remained at the core of the effort, low cost improvements in system performance were also addressed. Analysis of future demand for facility capacity was performed in three steps. First, baseline historical flow patterns, including volume and modal orientation of inbound, outbound, intrastate, and transhipped movements, were established through extensive processing of data obtained. Second, volume forecasts for 11 major commodity groups were made for each of three possible growth scenarios. Third, a strategic outlook was developed for selected commodity types to examine market structure, productivity trends, and the competitive position of transportation providers in the state. Network capacity analysis identified few weak links or bottlenecks in the state's main line waterway and railroad networks. A stock and flow approach was taken to measure the capacity of various types of cargo transfer terminals. A comparative analysis of maritime terminal productivity and cost was performed to assess the competitiveness of ports in Louisiana. An extensive survey of the physical, operating, and institutional characteristics of terminal roadway and railroad access links was made.
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Zhang, Xuanyu, Yining Gao, Guangyi Xiao, Bo Feng, and Wenshu Chen. "A Real-Time Garbage Truck Supervision and Data Statistics Method Based on Object Detection." Wireless Communications and Mobile Computing 2020 (October 10, 2020): 1–9. http://dx.doi.org/10.1155/2020/8827310.

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Garbage classification is difficult to supervise in the stage of collection and transportation. This paper proposes a computer vision-based method for intelligent supervision and workload statistics of garbage trucks. In terms of hardware, this paper deploys a camera and an image processing unit with NPU based on the original on-board computing and communication equipment. In terms of software, this paper uses the YOLOv3-tiny algorithm on the image processing unit to perform real-time target detection on garbage truck work, collects statistics on the color, specifications, and quantity of garbage bins cleaned by the garbage truck, and uploads the results to the server for recording and display. The proposed method has low deployment and maintenance costs while maintaining excellent accuracy and real-time performance, which makes it have good commercial application value.
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41

Bell, S. R., M. Gupta, and L. A. Greening. "Full-Fuel-Cycle Modeling for Alternative Transportation Fuels." Journal of Energy Resources Technology 117, no. 4 (1995): 297–306. http://dx.doi.org/10.1115/1.2835427.

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Utilization of alternative fuels in the transportation sector has been identified as a potential method for mitigation of petroleum-based energy dependence and pollutant emissions from mobile sources. Traditionally, vehicle tailpipe emissions have served as sole data when evaluating environmental impact. However, considerable differences in extraction and processing requirements for alternative fuels makes evident the need to consider the complete fuel production and use cycle for each fuel scenario. The work presented here provides a case study applied to the southeastern region of the United States for conventional gasoline, reformulated gasoline, natural gas, and methanol vehicle fueling. Results of the study demonstrate the significance of the nonvehicle processes, such as fuel refining, in terms of energy expenditure and emissions production. Unique to this work is the application of the MOBILE5 mobile emissions model in the full-fuel-cycle analysis. Estimates of direct and indirect green-house gas production are also presented and discussed using the full-cycle-analysis method.
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Maskeliūnaitė, Lijana, Henrikas Sivilevičius, and Valentinas Podvezko. "RESEARCH ON THE QUALITY OF PASSENGER TRANSPORTATION BY RAILWAY." TRANSPORT 24, no. 2 (2009): 100–112. http://dx.doi.org/10.3846/1648-4142.2009.24.100-112.

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The transportation of passengers by railroads is a dynamic process because setting, wherein the process takes place, undergoes continuous changes observed at technical, technological and transportation process planning levels. Every passenger has different needs and aims and can afford different possibilities. Therefore, the conception of quality differs in each individual case. In order to be able to plan a railway trip process in a proper manner, it is important to become familiarized with passenger needs and preferences and eliminate any existing deficiencies. To this end, it is reasonable to conduct passenger surveys. The paper provides an affinity diagram of railway transportation quality factors, the elements whereof correspond to the structure of the survey questionnaire. For determining the level of the quantitative significance (weight) of indicators, there was applied the method of Analytic Hierarchy Process (AHP) developed and introduced by the American scientist T. Saaty. The scope of research work carried out by the authors covered the determination of the highest eigenvalue, the compatibility ratio, the concordance correlation coefficient, the estimated and critical value of chi‐square values (the examples of estimations are presented in the paper) as well as the classification and processing of the surveyed data on passenger questionnaire and the analysis of respondents’ opinion. The closing part of the paper presents conclusions drawn based on the findings of the survey and respective recommendations for quality improvements in passenger transportation by Lithuanian railways.
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Zhang, Yang, Yuandong Liu, and Lee D. Han. "Real-Time Piecewise Regression." Transportation Research Record: Journal of the Transportation Research Board 2643, no. 1 (2017): 9–18. http://dx.doi.org/10.3141/2643-02.

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Ubiquitous sensing technologies make big data a trendy topic and a favored approach in transportation studies and applications, but the increasing volumes of data sets present remarkable challenges to data collection, storage, transfer, visualization, and processing. Fundamental aspects of big data in transportation are discussed, including how many data to collect and how to collect data effectively and economically. The focus is GPS trajectory data, which are used widely in this domain. An incremental piecewise regression algorithm is used to evaluate and compress GPS locations as they are produced. Row-wise QR decomposition and singular value decomposition are shown to be valid numerical algorithms for incremental regression. Sliding window–based piecewise regression can subsample the GPS streaming data instantaneously to preserve only the points of interest. Algorithm performance is evaluated completely as accuracy and compression power. A procedure is presented for users to choose the best parameter value for their GPS devices. Results of experiments with real-world trajectory data indicate that when the proper parameter value is selected, the proposed method achieves significant compression power (more than 10 times), maintains acceptable accuracy (less than 5 m), and always outperforms the fixed-rate sampling approach.
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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 (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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Aciman, Saki. "Data processing and telematic work and projects in Western Mediterranean ports." Maritime Policy & Management 17, no. 2 (1990): 87–97. http://dx.doi.org/10.1080/03088839000000058.

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Lwin, Ko Ko, Yoshihide Sekimoto, and Wataru Takeuchi. "Development of GIS Integrated Big Data Research Toolbox (BigGIS-RTX) for Mobile CDR Data Processing in Disasters Management." Journal of Disaster Research 13, no. 2 (2018): 380–86. http://dx.doi.org/10.20965/jdr.2018.p0380.

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This article reports the development of a geographical information system (GIS) embedded text-based geospatial Big Data research toolbox (BigGIS-RTX) designed especially for mobile CDR (Call Details Record) data processing in urban transport planning and disaster management. BigGIS-RTX is a standalone computer program that aims to provide a bridge between geospatial Big Data and end users (i.e. students and researchers) by reducing difficulties in handling geospatial Big Data processing and analysis tasks. This research toolbox makes it possible to handle text-based geospatial Big Data cleaning, formatting, subsetting, and extraction by keywords or structured query language (SQL), CDR data aggregation by base transceiver stations (BTSs), generation of origin–destination (OD) trips, OD matrices, and OD routes, and computation of OD links. Moreover, this research toolbox can be integrated with current commercial GIS software to perform further geospatial analysis functions to improve spatial decision making in urban and transport planning and disaster management. In this report, we discuss two current research outputs using BigGIS-RTX: first, multitemporal grid square population estimation and second, human mobility studies in transportation planning. These research outputs are essential for disaster management and emergency preparedness in terms of providing knowledge and information about population distribution changes over space and time, human mobility flow by a user defined time frame, and travel volume or link count information for individual road segments. This research is part of the core project “Development of a Comprehensive Disaster Resilience System and Collaboration Platform in Myanmar” in a research collaboration between Yangon Technological University, Myanmar, and The University of Tokyo, Japan, sponsored by the Japan Science and Technology Agency (JST) and the Japan International Cooperation Agency (JICA).
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Lucjan Setlak and Rafał Kowalik. "E1 Signal Processing of the Galileo System in the Navigation Receiver." Communications - Scientific letters of the University of Zilina 23, no. 3 (2021): E46—E55. http://dx.doi.org/10.26552/com.c.2021.3.e46-e55.

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The subject of this article are issues related to the navigation system in the field of analyzing the processed signal in the GNSS system receiver. The main purpose of the work is to discuss the Galileo E1 signal processing methods in the GNSS navigation system receiver, supported by adapted research tools in terms of solving the research problem (analysis, model, simulation tests) and the mathematical apparatus used. Key studies are concentrated around the process of generating the navigation data, dispersing sequences and signal modulation. Thus, when designing a receiver, it is better to use the simulation signals than the real ones, since one can get more control over the properties of the received signal. In the final part of the work, in accordance with the subject of research, based on the developed appropriate research tools, observations and final conclusions were formulated, which have practical applications.
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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 (2005): 28–36. http://dx.doi.org/10.1177/0361198105193500104.

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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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Yu, Jiangbo Gabe, Brent Selby, Nicholas Vlahos, Vivek Yadav, and Jason Lemp. "A feature-oriented vehicle trajectory data processing scheme for data mining: A case study for Statewide truck parking behaviors." Transportation Research Interdisciplinary Perspectives 11 (September 2021): 100401. http://dx.doi.org/10.1016/j.trip.2021.100401.

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Wei, Chien-Hung, Cheng-Chih Chang, and Sheng-Shih Wang. "Vehicle Classification Using Advanced Technologies." Transportation Research Record: Journal of the Transportation Research Board 1551, no. 1 (1996): 45–50. http://dx.doi.org/10.1177/0361198196155100106.

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Applying advanced technologies to existing problem domains is a highly desirable approach in many research areas. Among these techniques, image processing has been shown useful in transportation fields for such tasks as traffic pattern recognition, data collection, accident detection, and pavement evaluation. The integrated model with artificial neural networks (ANNs) has promising potential applications. The image processing and ANN model are combined to explore the feasibility of vehicle classification in real-world situations. Three methods were developed during the research process: ground segmentation, background subtraction, and window segmentation. The first two methods were used to separate the objects of scene and nonscene from the actual traffic image. To reduce the complexity of neural networks, the image was divided into 16 windows and three characteristics (occupation rates of vehicles, of horizontal image lines, and of vertical image lines) of each window were extracted to generate 48 factors as the input units of the neural network. The backpropagation ANN model with one hidden layer is employed. The experiments show that the accurate recognition rates of heavy vehicles, small cars, and motorcycles are 98.5, 96.92, and 91.94 percent, respectively. The result implies the remarkable applicability of the proposed methods in transportation areas.
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