Relevant bibliographies by topics / Data Traffic Management System (Computer system)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Traffic Management System (Computer system)'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2025

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Journal articles on the topic "Data Traffic Management System (Computer system)"

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Hizriadi, Ainul, Radea Shiddiq, Ivan Jaya, and Santi Prayudani. "Network Device Monitoring System based on Geographic Information System dan Simple Network Management Protocol." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 3, no. 2 (2020): 216–23. http://dx.doi.org/10.31289/jite.v3i2.3187.

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Network infrastructure monitoring is an important part of an institute to maintain the stability of computer network devices. One of the functions of computer network monitoring is to find out the data traffic generated in network application. Simple Network Management Protocol is one of protocols for monitoring the data traffic in network device. However, network device administrators still have problems when they want to monitor their network infrastructure, such as device location and data traffic information of network device that is only temporarily stored in the monitoring system, and ph
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Sakhuja, Ansh. "Intelligent Traffic Management System using Computer Vision and Machine Learning." Innovative Research Thoughts 9, no. 5 (2023): 1–10. http://dx.doi.org/10.36676/irt.2023-v9i5-001.

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With urban traffic management becoming more and more complex, a ground-breaking system called "Intelligent Traffic Management System using Computer Vision and Machine Learning" was created to address these issues. It makes use of the strength of two innovative technologies: computer vision and machine learning. The system can receive and analyze visual data from cameras placed across roads thanks to computer vision, and machine learning gives it the ability to learn from this data and come to wise conclusions without explicit programming.
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Basha, MD Chan. "AI Based Traffic Control System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48317.

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This project explores the development of an AI-based traffic control system aimed at enhancing urban traffic management. By utilizing machine learning algorithms and real-time data from various sources, including traffic cameras, sensors, and GPS devices, the system analyzes traffic patterns and predicts congestion. We are using several AI and ML models, including Reinforcement Learning, Deep Learning, Neural Network and also Computer Vision. The integration of vehicle to infrastructure communication further enhances responsiveness, allowing for timely updates to drivers. Simulation results in
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Journal, IJSREM. "Advanced Drone Surveillance System for Traffic Management." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28534.

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The rapid urbanization and increasing vehicle traffic in metropolitan areas have given rise to a significant challenges in traffic management. Traditional methods of a traffic monitoring and control often struggle to cope with the dynamic and complex nature of urban traffic patterns. In this context, we propose an innovative Drone Surveillance System for Traffic Management to revolutionize the way traffic is monitored, analyzed, and optimized in urban environments. Our system leverages state-of-the-art drone technology equipped with advanced sensors, high-resolution cameras, and intelligent al
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S, Pavithra. "Smart Traffic Management and Detection System Using AI and Computer Vision." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47175.

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ABSTRACT The Smart Traffic Management and Violation Detection System integrates AI and Computer Vision to optimize traffic control and detect violations. By analyzing real-time video feeds from surveillance cameras, it adjusts traffic signal timings based on vehicle density, reducing congestion and delays. The system automatically detects violations such as red light jumping, helmetless riding, over speeding, and wrong-lane driving using object detection and OCR techniques. Violators’ license plates are recorded, and penalty notices are generated automatically. This solution enhances road safe
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Sowmya, B. "Adaptive Traffic Management System using CNN (YOLO)." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3726–32. http://dx.doi.org/10.22214/ijraset.2021.35768.

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The huge number of vehicles on the roadways is making congestion a significant problem. The line longitudinal vehicle waiting to be processed at the crossroads increases quickly, and the traditionally used traffic signals are not able to program it properly. Manual traffic monitoring may be an onerous job since a number of cameras are deployed over the network in traffic management centers. The proactive decision-making of human operators, which would decrease the effect of events and recurring road congestion, might contribute to the easing of the strain of automation.The traffic control fram
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Zhang, Jinxi, Wenying Zhu, Xueying Wu, and Tianshan Ma. "Traffic State Detection Based on Multidimensional Data Fusion System of Internet of Things." Wireless Communications and Mobile Computing 2021 (July 2, 2021): 1–12. http://dx.doi.org/10.1155/2021/1374186.

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In recent years, the rapid development of cloud computing, mobile Internet, Internet of Things, and other technologies has accelerated the explosive growth of the number and types of data in various industries. As one of the current hot research fields, the multidimensional data fusion system has received widespread attention all over the world. Road traffic and traffic management involve a wide range of participants; a variety of traffic behavior and traffic management means have produced a huge volume of traffic management data and have a very high application value. Therefore, it is of grea
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Yuan, Yuan, Zhi Yong Liu, Huai Kun Xiang, and Ze Feng Ding. "Traffic Survey System Based on GPS, GPRS and GIS." Advanced Materials Research 328-330 (September 2011): 989–97. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.989.

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Referring to research findings of existing traffic survey instrument, this paper bases on electronic traffic survey counter to insert GPRS module, micro-program controller, vehicle type selection keyboard and direction selection keyboard, and utilizes computer technology to establish data processing platform, studies and designs the traffic survey system on the basis of GPS, GPRS and GIS. This system can realize real-time monitoring on surveyors, accurate and timely transmission and processing of survey data, and automatic analysis and management of survey data.
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Wei, Xin Hua, Xian Xing Duan, and Xiao Kan Wang. "Application of PLC in the Expressway Intelligent Traffic Control System." Applied Mechanics and Materials 361-363 (August 2013): 2219–23. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.2219.

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The expressway intelligent traffic control system based on S7-200 series Programmable Logic Controller (PLC) was introduced in this paper.PLC has strong adaptability in the complex environment and rich internal timer resources, it is easily to realize accuracy controlling the traffic lights, specially for multi-crossroads.PLC analyzed and processed the signals of the body flow, speed, vehicle size and other data by the sense coil, then transmitting the information to the host computer. The host computer might automatically adjust the length of time from the final signal to achieve intelligent
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Pedraza-Farías, Sulma-Rocío, Gustavo Cáceres-Castellanos, and Jorge-Enrique Quevedo-Reyes. "Smart Mobility: Road Incident Monitoring System for Urban Traffic Management." Revista Facultad de Ingeniería 34, no. 71 (2025): e19015. https://doi.org/10.19053/uptc.01211129.v34.n71.2025.19015.

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This article describes the process, tools, and technological platforms used to implement a computer system that collects, processes, transforms, stores, and analyzes real-time traffic incidents. This system, fed by data from the Waze application accessed through the Waze for Cities program, aims to support decision-making in urban traffic management. Its implementation uses a spiral methodology that generates evolutionary deliveries, progressing towards more complete versions. The architecture, components, and data flow are detailed, allowing to get interactive and summarized views of the info
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Dissertations / Theses on the topic "Data Traffic Management System (Computer system)"

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Tacic, Ivan. "Efficient Synchronized Data Distribution Management in Distributed Simulations." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6822.

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Data distribution management (DDM) is a mechanism to interconnect data producers and data consumers in a distributed application. Data producers provide useful data to consumers in the form of messages. For each message produced, DDM determines the set of data consumers interested in receiving the message and delivers it to those consumers. We are particularly interested in DDM techniques for parallel and distributed discrete event simulations. Thus far, researchers have treated synchronization of events (i.e. time management) and DDM independent of each other. This research focuses on how t
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Rodarte, Christopher Gabriel 1975. "Knowledge management in the enhanced traffic management system." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9659.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.<br>Includes bibliographical references (leaves 91-94).<br>The Enhanced Traffic Management System (ETMS) functions as the primary Database Management System (DBMS) for real-time flight information administered by the Federal Aviation Administration. The ETMS is a mission critical system responsible for facilitating air traffic control throughout the United States. The design of the ETMS represents a pinnacle achievement of modern data management. This thesis project will investiga
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Lin, Joyce C. (Joyce Chaisin) 1979. "VisualFlight : the air traffic control data analysis system." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87266.

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Zhang, Tong. "Improving the performance of a traffic data management system." Ohio : Ohio University, 1999. http://www.ohiolink.edu/etd/view.cgi?ohiou1175198741.

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Anumalla, Kalyani. "DATA PREPROCESSING MANAGEMENT SYSTEM." University of Akron / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1196650015.

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Choi, Myung Cheon. "Traffic flow management for RSVP/ATM edge devices." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15520.

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Ma, Xuesong 1975. "Data mining using relational database management system." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98757.

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With the wide availability of huge amounts of data and the imminent demands to transform the raw data into useful information and knowledge, data mining has become an important research field both in the database area and the machine learning areas. Data mining is defined as the process to solve problems by analyzing data already present in the database and discovering knowledge in the data. Database systems provide efficient data storage, fast access structures and a wide variety of indexing methods to speed up data retrieval. Machine learning provides theory support for most of the popular d
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Huml, Kathy Pederson. "Intelligent Data Object Management System (IDOMS)." Thesis, Kansas State University, 1986. http://hdl.handle.net/2097/9918.

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Quintero, Michael C. "Constructing a Clinical Research Data Management System." Thesis, University of South Florida, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10640886.

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<p> Clinical study data is usually collected without knowing what kind of data is going to be collected in advance. In addition, all of the possible data points that can apply to a patient in any given clinical study is almost always a superset of the data points that are actually recorded for a given patient. As a result of this, clinical data resembles a set of sparse data with an evolving data schema. To help researchers at the Moffitt Cancer Center better manage clinical data, a tool was developed called GURU that uses the Entity Attribute Value model to handle sparse data and allow users
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Wang, Yanchao. "Protein Structure Data Management System." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/20.

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With advancement in the development of the new laboratory instruments and experimental techniques, the protein data has an explosive increasing rate. Therefore how to efficiently store, retrieve and modify protein data is becoming a challenging issue that most biological scientists have to face and solve. Traditional data models such as relational database lack of support for complex data types, which is a big issue for protein data application. Hence many scientists switch to the object-oriented databases since object-oriented nature of life science data perfectly matches the architecture of
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Books on the topic "Data Traffic Management System (Computer system)"

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Adiguzel, Rahim Ilker. Prototype report generation facility in the Data Traffic Management System. US Army Corps of Engineers, Construction Engineering Research Laboratory, 1987.

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Leo, James. A evaluation of two data dictionary directories. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1985.

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Office, General Accounting. Air traffic control: Status of FAA's effort to modernize the system : fact sheet for congressional requesters. U.S. General Accounting Office, 1990.

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Montana. Department of Transportation. Data and Statistics Bureau. Congestion Management System. Montana Dept. of Transportation, Transportation Planning Division, Data & Statistics Bureau, 1998.

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Office, General Accounting. Air traffic control: Ineffective management plagues $1.7-billion radar program : report to congressional requesters. U.S. General Accounting Office, 1990.

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1968-, Rovithakis George A., ed. End to end adaptive congestion control in TCP/IP networks. Taylor & Francis, 2012.

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Transport, European Commission Directorate-General, ed. European rail traffic management system: ERTMS. Office for Official Publications of the European Communities, 1996.

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Office, General Accounting. Air traffic control: Inadequate planning increases risk of computer failures in Los Angeles : report to congressional requesters. The Office, 1990.

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Office, General Accounting. Air traffic control: Inadequate planning increases risk of computer failures in Los Angeles : report to congressional requesters. U.S. General Accounting Office, 1990.

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Office, General Accounting. Air traffic control: Good progress on interim replacement for outage-plagued system, but risks can be further reduced : report to the Secretary of Transportation. The Office, 1996.

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Book chapters on the topic "Data Traffic Management System (Computer system)"

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Hua, Xingcheng, Jierui Wang, Li Lei, Bin Zhou, Xiaolin Zhang, and Peng Liu. "H-TDMS: A System for Traffic Big Data Management." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2209-8_8.

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Liu, Yiming, Zhen Cheng, Ziyu Wang, Shihan Chen, and Xin Su. "Fine-Grained Data Traffic Management System Based on VPN Technology." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98752-1_3.

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Zhang, Yuan, Qing Wang, Jiahao Li, and Zhijun Wu. "The Storage and Sharing of Big Data in Air Traffic Management System." In Lecture Notes in Computer Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2233-8_16.

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Weik, Martin H. "data management system." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_4319.

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Lalitha, K., and M. Pounambal. "IoT-Based Traffic Management." In Emerging Research in Data Engineering Systems and Computer Communications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0135-7_14.

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Jeslin Shanthamalar, J., P. Yuthika, R. Kannan, J. Albert Mayan, and R. Velvizhi. "Traffic Management System Using Computer Vision." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8865-1_23.

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Rapant, Lukáš, Kateřina Slaninová, Jan Martinovič, Marek Ščerba, and Martin Hájek. "Comparison of ASIM Traffic Profile Detectors and Floating Car Data During Traffic Incidents." In Computer Information Systems and Industrial Management. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24369-6_10.

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Shastry, K. Aditya, H. A. Sanjay, and M. Lakshmi. "Intelligent Framework for Smart Traffic Management System." In IoT and Big Data Analytics for Smart Cities. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003217404-5.

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Khanna, Abhirup, Rohit Goyal, Manju Verma, and Deepika Joshi. "Intelligent Traffic Management System for Smart Cities." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3804-5_12.

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Maisonobe, Jean-Christophe, Jean Daniel Demond, Giannicola Marengo, Dolores Adamski, Diego Albesano, and Olivier Latouille. "SYNCRO - An Innovative Public Procurement of an Advanced Data Gathering System for Interurban Roads Based on its Technologies." In Traffic Management. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119307822.ch2.

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Conference papers on the topic "Data Traffic Management System (Computer system)"

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Reddy, P. Sai Srujan, B. U. Naveen Raj, Nikhil Tom Jose, and Meena Belwal. "Advanced Adaptive Traffic Management System for Urban Environments." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914977.

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Jayapradha, J., R. Sathishkumar, R. Swathi, S. Tejaswini, and J. R. Rinjima. "Computer Vision Based Enhanced Traffic Management System Using YOLO." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894375.

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Isack, Enock, Mawanda Vincent, Prasant Kumar Dash, Kwizera Jean Bosco, Mingma Kanchhi Sherpa, and Ujjwal Kumar Bhadani. "Inclusive and Intelligent Traffic Management System for the Physically Impaired Pedestrians." In 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT). IEEE, 2025. https://doi.org/10.1109/incacct65424.2025.11011317.

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Bahadure, Nilesh Bhaskarrao, Deepak Parashar, Jagdish Chandra Patni, et al. "Computer vision-based real-time vehicle and traffic management and classification system." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11071088.

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Zhou, Xiaoli, and Chao Bei. "Data collection and target tracking using event cameras in space traffic management." In Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), edited by Xiantao Xiao and Jia Yao. SPIE, 2024. https://doi.org/10.1117/12.3054687.

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Chen, Chunmei. "Intelligent Traffic Monitoring and Management Based on Computer Vision Technology." In 2024 Second International Conference on Data Science and Information System (ICDSIS). IEEE, 2024. http://dx.doi.org/10.1109/icdsis61070.2024.10594478.

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Hong, Weihao, and Xiangyu Zhang. "Intelligent traffic management system model based on LSTM-TOPSIS-Dijkstra." In International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), edited by Harris Wu and Haiwu Li. SPIE, 2024. http://dx.doi.org/10.1117/12.3026628.

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BalaMurugan, G., N. Palanivel, and A. John. "Application Reliable Traffic Control Method for Efficient Data Management in Wireless-Aided Computer Applications." In 2023 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2023. http://dx.doi.org/10.1109/icscan58655.2023.10394734.

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Valuev, Andrey M. "Computer Simulation of a Stochastic Traffic Flow through an Intersection Based on Monitoring Data." In 2023 16th International Conference Management of large-scale system development (MLSD). IEEE, 2023. http://dx.doi.org/10.1109/mlsd58227.2023.10303834.

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Gao, Zhuomiao, Xianghui Song, Yameng Li, Liyang Lu, Dongzhu Wang, and Na Li. "Research on the Evaluation Index System of Freeway Traffic Management and Control Strategies." In BDICN 2025: 2025 4th International Conference on Big Data, Information and Computer Network. ACM, 2025. https://doi.org/10.1145/3727353.3727440.

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Reports on the topic "Data Traffic Management System (Computer system)"

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Chien, Stanley, Lauren Christopher, Yaobin Chen, Mei Qiu, and Wei Lin. Integration of Lane-Specific Traffic Data Generated from Real-Time CCTV Videos into INDOT's Traffic Management System. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317400.

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The Indiana Department of Transportation (INDOT) uses about 600 digital cameras along populated Indiana highways in order to monitor highway traffic conditions. The videos from these cameras are currently observed by human operators looking for traffic conditions and incidents. However, it is time-consuming for the operators to scan through all video data from all the cameras in real-time. The main objective of this research was to develop an automatic and real-time system and implement the system at INDOT to monitor traffic conditions and detect incidents automatically. The Transportation and
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Christopher, Lauren, Stanley Chien, Yaobin Chen, Mei Qiu, William Reindl, and Liya Koshy. Anomaly Detection in Traffic Patterns Using the INDOT Camera System. Purdue University, 2025. https://doi.org/10.5703/1288284317778.

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The Transportation and Autonomous Systems Institute (TASI) of Purdue University Indianapolis (PUI) and the INDOT Traffic Management Center worked together to develop a system that monitors traffic conditions using INDOT CCTV video feeds. Computer vision-based traffic anomaly detection has been studied for the past 20 years, and a thorough state-of-the-art analysis was produced in a recent survey paper. Although AI has contributed to improving anomaly detection, several major challenges remain, such as tracking errors, illumination, weather, occlusion handling, camera pose, and perspective. In
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Pineda-Mendez, Raul, Qiming Guo, Noshin Ahmad, Mario A. Romero, and Andrew P. Tarko. Incorporating Time-Dependent Data for Proactive Safety Management. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317700.

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This study proposed a risk-based safety management framework to supplement the current crash-based safety management system. The proposed tool considers time-dependent factors (e.g., hourly traffic, speed features, weather conditions, signal controls) to help justify operational measures for safety improvements (e.g., variable message signs, variable speed limits, warnings). These selected temporal factors subsequently were included in the developed sequential logit models; and those models, applied hour by hour, were then used to estimate the crash probability and severity level. Two typical
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Yang, Yu, Hen-Geul Yeh, and Cesar Ortiz. Battery Management System Development for Electric Vehicles and Fast Charging Infrastructure Improvement. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2325.

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The electric vehicle (EV) has become increasingly popular due to its being zero-emission. However, a significant challenge faced by EV drivers is the range anxiety associated with battery usage. Addressing this concern, this project develops a more efficient battery management system (BMS) for electric vehicles based on a real-time, state-of-charge (SOC) estimation. The proposed study delivers three modules: (1) a new equivalent circuit model (ECM) for lithium-ion batteries, (2) a new SOC estimator based on the moving horizon method, and (3) an on-board FPGA implementation of the classical Cou
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He, Zhitong, Abin Mathew, Abhijeet Ingale, Jue Zhou, Feng Li, and Yaobin Chen. Traffic Management Geocast Study with Connected Vehicles on Indiana Highways. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317753.

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Vehicular communication allows vehicles to interact with road users, roadside infrastructure, and cloud-connected devices. It holds a crucial position in modern transportation systems, impacting both fundamental and advanced aspects and enhancing traffic safety and efficiency. C-V2X is a wireless communication technology that uses cellular networks to enable communication between vehicles and infrastructure. C-V2X can be used for applications such as collision avoidance, traffic management, and remote vehicle diagnostics. This project conducted a feasibility study on the current position of C-
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Mathew, Jijo K., Haydn Malackowski, Yerassyl Koshan, et al. Development of Latitude/Longitude (and Route/Milepost) Model for Positioning Traffic Management Cameras. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317720.

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Traffic Incident Management (TIM) is a FHWA Every Day Counts initiative with the objective of reducing secondary crashes, improving travel reliability, and ensuring the safety of responders. Agency roadside cameras play a critical role in TIM by helping dispatchers quickly identify the precise location of incidents when receiving reports from motorists with varying levels of spatial accuracy. Reconciling position reports that are often mile-marker based with cameras that operate in a Pan-Tilt-Zoom (PTZ) coordinate system relies on dispatchers having detailed knowledge of hundreds of cameras an
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Yaseen, Lama, Nourah Al-Hosain, Ibrahem Shatnawi, and Abdelrahman Muhsen. Impact of Urban Traffic on Fuel Consumption Leveraging IoT Data: Case Study of Riyadh City. King Abdullah Petroleum Studies and Research Center, 2024. https://doi.org/10.30573/ks--2024-dp72.

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This study explores the rising trend of traffic congestion in Riyadh and its impact on fuel consumption for passenger cars amid the challenges of rapid urbanization and increasing vehicle use. By utilizing real-time floating car data (FCD) collected by vehicles equipped with Global Positioning System (GPS) technology and communication systems, this study illustrates the potential of the Internet of Things (IoT) and smart city technologies in developing intelligent transportation systems and improving urban mobility management. A spatial analysis of the traffic flow dynamics in Riyadh, focusing
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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.

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To improve traffic performance and safety, the ability to measure traffic accurately and effectively, including motorists and other vulnerable road users, at road intersections is needed. A past study conducted by the Center for Road Safety has demonstrated that it is feasible to detect and track various types of road users using a LiDAR-based system called TScan. This project aimed to progress towards a real-world implementation of TScan by building two trailer-based prototypes with full end-user documentation. The previously developed detection and tracking algorithms have been modified and
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Kong, Zhihao, and Na Lu. Determining Optimal Traffic Opening Time Through Concrete Strength Monitoring: Wireless Sensing. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317613.

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Construction and concrete production are time-sensitive and fast-paced; as such, it is crucial to monitor the in-place strength development of concrete structures in real-time. Existing concrete strength testing methods, such as the traditional hydraulic compression method specified by ASTM C 39 and the maturity method specified by ASTM C 1074, are labor-intensive, time consuming, and difficult to implement in the field. INDOT’s previous research (SPR-4210) on the electromechanical impedance (EMI) technique has established its feasibility for monitoring in-situ concrete strength to determine t
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Chien, Stanley, Yaobin Chen, Lauren Christopher, Mei Qiu, and Zhengming Ding. Road Condition Detection and Classification from Existing CCTV Feed. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317364.

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The Indiana Department of Transportation (INDOT) has approximately 500 digital cameras along highways in populated areas of Indiana. These cameras are used to monitor traffic conditions around the clock, all year round. Currently, the videos from these cameras are observed one-by-one by human operators looking for traffic conditions and incidents. The main objective of this research was to develop an automatic, real-time system to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Tec
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