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Добірка наукової літератури з теми "Packet traffic"

Автор: Grafiati

Опубліковано: 28 травня 2022

Оновлено: 31 липня 2025

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Статті в журналах з теми "Packet traffic"

1

Chen, Xu-Yang, Lu Han, De-Chuan Zhan, and Han-Jia Ye. "MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic Classification." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 15922–29. https://doi.org/10.1609/aaai.v39i15.33748.

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Анотація:

Network traffic includes data transmitted across a network, such as web browsing and file transfers, and is organized into packets (small units of data) and flows (sequences of packets exchanged between two endpoints). Classifying encrypted traffic is essential for detecting security threats and optimizing network management. Recent advancements have highlighted the superiority of foundation models in this task, particularly for their ability to leverage large amounts of unlabeled data and demonstrate strong generalization to unseen data. However, existing methods that focus on token-level rel

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2

Dr., Paravathi C., D. Roshini, and S. Nayak Shwetha. "Packet Sniffing." International Journal of Engineering and Management Research 14, no. 1 (2024): 71–76. https://doi.org/10.5281/zenodo.10686707.

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Анотація:

Packet sniffing is a technique used to monitor network traffic by intercepting each packet that flows across the network. It can be used as a helpful administrative tool or for malicious purposes. With the advancement of technology, the network is growing rapidly, resulting in an increase in network traffic. Therefore, it is crucial to monitor both the network traffic and the activities of its users in order to maintain a smooth and efficient network. However, monitoring a large network can be a complex task due to the large number of packets involved. This is where packet sniffing comes in ha

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3

Shafiq, Muhammad, and Xiangzhan Yu. "Effective Packet Number for 5G IM WeChat Application at Early Stage Traffic Classification." Mobile Information Systems 2017 (2017): 1–22. http://dx.doi.org/10.1155/2017/3146868.

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Анотація:

Accurate network traffic classification at early stage is very important for 5G network applications. During the last few years, researchers endeavored hard to propose effective machine learning model for classification of Internet traffic applications at early stage with few packets. Nevertheless, this essential problem still needs to be studied profoundly to find out effective packet number as well as effective machine learning (ML) model. In this paper, we tried to solve the above-mentioned problem. For this purpose, five Internet traffic datasets are utilized. Initially, we extract packet

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4

Nisar, Kashif. "Fourth Stage of Voice Priority Queue for VoIP over WLANs." International Journal of Interdisciplinary Telecommunications and Networking 4, no. 2 (2012): 48–63. http://dx.doi.org/10.4018/jitn.2012040104.

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Voice over Internet Protocol (VoIP) is growing rapidly during this decade. VoIP is seen as a short-term and long-term transmission for voice and audio traffic and is moving on Wireless Local Area Networks (WLANs) based on IEEE 802.11 standards. Currently, packet scheduling algorithms like Weighted Fair Queuing (WFQ), was mainly designed to provide the bandwidth reservation. The Strict Priority (SP) is low-cost to maintain the delay sensitive voice traffic. Also, a number of research scheduling solutions have been proposed like General processor sharing (GPS), Deficit Round Robin (DRR), Content

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5

Hadap, Prof Manish, Chanchal Halwe, Sakshi Wase, et al. "Network Packet Sniffing and Monitoring." International Journal of Advances in Engineering and Management 6, no. 10 (2024): 260–66. https://doi.org/10.35629/5252-0610260266.

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Анотація:

The daily increase in the number of users on the network is causing a surge in network traffic, highlighting the importance of monitoring both user activity and network traffic to ensure smooth network operations. Monitoring network activity is challenging due to the large volume of packets on the network. Packet sniffing is a technique used to observe various network activities and identify vulnerabilities, enabling network administrators to bolster network security. This process involves inspecting data transmitted by other users on the network and is applicable to both switched and non-swit

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6

Ogbimi, Voke, and Emuejevoke Francis. "An Improved Error Control Model in Packet Switched Wide Area Networks." Transactions on Networks and Communications 7, no. 4 (2019): 01–18. http://dx.doi.org/10.14738/tnc.74.7263.

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Анотація:

Error is an important problem in communication that occurs in shared networks when a packet fails to arrive at the destination or it arrives at the destination but some of the bits are in error or have been altered. In typical packet switched wide area networks, this can occur quite easily when output links are slower than inputs and multiple traffic sources competing for same output link at the same time. Typical for packet switched WAN, the packet transmit input/output buffer and queue of the network devices in their way towards the destination. Moreover, these networks are characterized by

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7

Pustovoitov, Pavlo, Vitalii Voronets, Oleksandr Voronets, Halyna Sokol, and Maksym Okhrymenko. "Assessment of QOS indicators of a network with UDP and TCP traffic under a node peak load mode." Eastern-European Journal of Enterprise Technologies 1, no. 4 (127) (2024): 23–31. http://dx.doi.org/10.15587/1729-4061.2024.299124.

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Анотація:

The object of research is Markov models of network nodes with UDP (User Datagram Protocol) and TCP (Transmission Control Protocol) traffic and their differences. The task solved is the lack of Markov models of network nodes describing the behavior of TCP traffic from the point of view of packet retransmissions and packet delivery guarantees. Markov models of network nodes describing traffic behavior with guaranteed packet delivery have been further advanced. Given the comparison of the models, the differences from the classic models serving TCP traffic were shown, for each packet flow, an addi

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8

Berqia, Amine, Mohamed Hanini, and Abdelkrim Haqiq. "Combined Queue Management and Scheduling Mechanism to Improve Intra-User Multi-Flow QoS in a Beyond 3,5G Network." International Journal of Mobile Computing and Multimedia Communications 4, no. 1 (2012): 57–68. http://dx.doi.org/10.4018/jmcmc.2012010105.

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Packet scheduling and buffer management are the two important functions adopted in networks design to ensure the Quality of Service (QoS) when different types of packets with different needs of quality share the same network resources. The Packet scheduling policy determines packet service priorities at the output link, it can reduce packet delay and delay jitter for high-priority traffic. The buffer management involves packet dropping and buffer allocation. The overall goal of such schemes proposed in High Speed Downlink Packet Access (HSDPA) is to take advantage of the channel variations bet

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9

Ermakov, R. N. "CLASSIFICATION OF NETWORK PROTOCOLS WITH APPLICATION OF MACHINE LEARNING METHODS AND FUZZY LOGIC ALGORITHMS IN TRAFFIC ANALYSIS SYSTEMS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 189 (March 2020): 37–48. http://dx.doi.org/10.14489/vkit.2020.03.pp.037-048.

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Анотація:

This paper presents a new effective approach to analyzing network traffic in order to determine the protocol of information exchange. A brief description of the structure of the algorithm for classifying network packets by belonging to one of the known network protocols is given. To define the protocol, the principle of high-speed one-packet classification is used, which consists in analyzing the information transmitted in each particular packet. Elements of behavioral analysis are used, namely, the transition states of information exchange protocols are classified, which allows to achieve a h

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10

Lozhkovskyi, A. G., V. A. Turchyn, and V. S. Andriiaka. "INFLUENCE OF CALCULATION ACCURACY OF TRAFFIC SELF-SIMILARITY EXPONENT ON A QUALITY OF SERVICE CHARACTERISTICS." Proceedings of the O.S. Popov ОNAT 1, no. 1 (2020): 88–94. http://dx.doi.org/10.33243/2518-7139-2020-1-1-88-94.

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Анотація:

The quality of service (QoS) characteristics in any telecommunication system depend on its scheme, requirements servicing rules and, to the greatest extent, on the type of traffic generated by the system requirements flow. In packet communication networks, a mathematical model of self-similar traffic is used, where the time interval between packets is described by Pareto or Weibull distributions. With an increase in the degree of self-similarity of packet traffic, the QoS characteristics in the system significantly deteriorate compared to servicing, for example, Poisson traffic. But for such t

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Дисертації з теми "Packet traffic"

1

Barenco, Martino. "Packet traffic in computer networks." Thesis, Queen Mary, University of London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399750.

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2

Mehrabian, Maryam. "Data reduction in modeled packet traffic." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-87684.

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Within Ericsson there is a continuous activity of traffic modeling. Traffic modeling is a practice to analyze traffic patterns and determine necessary resources to handle it optimally. This activity focuses on gathering and analyzing live network measurements, implementing and presenting traffic models. One example of concept in packet traffic modeling is transmission object log which is an aggregation of packet data traces from a measured network over a transmission period. These trace logs that are simple list of all transmission objects contain a vast number of data. When the amount of data

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3

Ribeiro, M. R. N. "Traffic prioritisation in photonic packet switching." Thesis, University of Essex, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395100.

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4

Farrera, Marcos Paredes. "Packet-by-packet analysis of video traffic dynamics on IP networks." Thesis, University of Essex, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.422235.

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5

Ma, Ho I. "Accelerated simulation of power-law traffic in packet networks." Thesis, Queen Mary, University of London, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407630.

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6

Goldberg, Benjamin. "Benchmarking Traffic Control Algorithms on a Packet Switched Network." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/cmc_theses/1192.

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Traffic congestion has tremendous economic and environmental costs. One way to reduce this congestion is to implement more intelligent traffic light systems. There is significant existing research into different algorithms for controlling traffic lights, but they all use separate systems for performance testing. This paper presents the Rush Hour system, which models a network of roadways and traffic lights as a network of connected routers and endnodes. Several traffic switching algorithms are then tested on the Rush Hour system. As expected, we found that the more intelligent systems were eff

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7

Shaker, Maher. "A Dataplane Programmable Traffic Marker using Packet Value Concept." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-85825.

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Анотація:

Real-time sensitive network applications are emerging and require ultra-low latency to reach the desired QoS. A main issue that contributes to latency is excessive buffering at intermediate switches and routers. Existing queuing strategies that aim to reduce buffering induced latency typically apply a single queue AQM that does not support service differentiation and treats all packets equally. The recently proposed per packet value framework utilizes a packet value marker and a packet value aware AQM to solve this issue by supporting service differentiation in a single queue and introducing m

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8

Carvalho, João Pedro Martins de. "High Performance shallow packet inspection system for traffic identification." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/18455.

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Mestrado em Engenharia de Computadores e Telemática<br>The evolution and growth of the Internet has led to a growing preoccupation regarding dynamic allocation of resources in large networks, as well as to an unprecedented growing adoption of security policies based on tra c classi cation. This phenomenon triggered the creation of deep inspection mechanisms for packets where we can see a cross-access that is based on the retrieval of speci c strings present in packet's Payload. This event raises a number of technical, ethical, and potentially legal limitations. With the increasing need

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9

Kelley, Simon Richard. "Congestion control for unreserved traffic in ATM networks." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319763.

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10

Taebi, Harandi Sareh. "Self-similar traffic modeling and Clos-like packet switch architectures." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/27052.

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Анотація:

Two methods of generating self-similar Internet traffic are reported in this document. The first method uses Heavy-tailed Pareto distribution and generates fixed length packets, while the second method focuses on Fractional Gaussian Noise as means of generating variable-length packets. Various testing have been performed on the traffic to corroborate its self-similarity and measure relevant statistics. Using the self-similar traffic environment, the performance of a sectored three-stage packet switch architecture is evaluated which benefits from both photonic and electronic technologies and pr

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Книги з теми "Packet traffic"

1

United States. National Aeronautics and Space Administration., ed. MMPP Traffic Generator for the testing of the SCAR 2 Fast Packet Switch. National Aeronautics and Space Administration, 1995.

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2

Callinan, Phyllis. Provision of service guarantees to real-time traffic in packet switched networks. Oxford Brookes University, 2000.

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3

Black, Uyless D. MPLS and label switching networks. Prentice Hall, 2001.

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4

Salmelin, Juha, and Esa Markus Metsala. Mobile backhaul. Wiley, 2012.

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5

Kohei, Shiomoto, and Oki Eiji 1969-, eds. GMPLS technologies: Broadband backbone networks and systems. Taylor & Francis, 2005.

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6

Kohei, Shiomoto, and Oki Eiji 1969-, eds. GMPLS technologies: Broadband backbone networks and systems. Taylor & Francis, 2005.

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7

Minei, Ina. MPLS-Enabled Applications. John Wiley & Sons, Ltd., 2006.

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Minei, Ina. MPLS-Enabled Applications. John Wiley & Sons, Ltd., 2008.

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9

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

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10

Rodríguez-Dagnino, Ramón Martín. Packet video traffic modeling and prediction in broadband networks. 1993.

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Частини книг з теми "Packet traffic"

1

Callegati, Franco, Walter Cerroni, and Carla Raffaelli. "Engineering Packet-Switched Networks." In Traffic Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09589-4_5.

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2

Finamore, Alessandro, Marco Mellia, Michela Meo, and Dario Rossi. "KISS: Stochastic Packet Inspection." In Traffic Monitoring and Analysis. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01645-5_14.

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3

Lee, Yeonhee, Wonchul Kang, and Youngseok Lee. "A Hadoop-Based Packet Trace Processing Tool." In Traffic Monitoring and Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20305-3_5.

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4

Iacovazzi, Alfonso, and Andrea Baiocchi. "Padding and Fragmentation for Masking Packet Length Statistics." In Traffic Monitoring and Analysis. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28534-9_9.

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5

Bregni, Stefano, Giacomo Guerra, and Achille Pattavina. "Optical Packet Switching of IP Traffic." In Next Generation Optical Network Design and Modelling. Springer US, 2003. http://dx.doi.org/10.1007/978-0-387-35670-9_9.

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6

Friedl, Aleš, Sven Ubik, Alexandros Kapravelos, Michalis Polychronakis, and Evangelos P. Markatos. "Realistic Passive Packet Loss Measurement for High-Speed Networks." In Traffic Monitoring and Analysis. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01645-5_1.

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7

Huisinga, T., R. Barlovic, W. Knospe, A. Schadschneider, and M. Schreckenberg. "Microscopic Modeling of Packet Transport in the Internet." In Traffic and Granular Flow’01. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-10583-2_40.

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8

Uceda, Víctor, Miguel Rodríguez, Javier Ramos, José Luis García-Dorado, and Javier Aracil. "Selective Capping of Packet Payloads for Network Analysis and Management." In Traffic Monitoring and Analysis. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17172-2_1.

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9

Sliwinski, Jaroslaw, Andrzej Beben, and Piotr Krawiec. "EmPath: Tool to Emulate Packet Transfer Characteristics in IP Network." In Traffic Monitoring and Analysis. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12365-8_4.

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10

Tadaki, Shin-ichi. "Packet Flow and its Temporal Properties in the Internet." In Traffic and Granular Flow ’07. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-77074-9_84.

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Тези доповідей конференцій з теми "Packet traffic"

1

Soper, Jacob, Yue Xu, Ernest Foo, Zahra Jadidi, and Kien Nguyen Thanh. "Improved Packet-Level Synthetic Network Traffic Generation." In 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2024. https://doi.org/10.1109/trustcom63139.2024.00267.

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2

de Souza, Críston P., Antonio J. Pinheiro, Jeandro M. Bezerra, and Michele Nogueira. "Optimal Packet Padding for IoT Traffic Obfuscation." In GLOBECOM 2024 - 2024 IEEE Global Communications Conference. IEEE, 2024. https://doi.org/10.1109/globecom52923.2024.10901754.

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3

Rajan, Manju, Arya Phadke, Syam Sankar, and John Jose. "Handling Packet Blocking Attack in NoC Using Traffic Snooping." In 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS). IEEE, 2024. http://dx.doi.org/10.1109/coins61597.2024.10622599.

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4

Feng, Beibei, Qi Wang, Wang Yijing, et al. "Enhanced Encrypted Traffic Classification Through Packet-Flow Dual Views." In 2025 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2025. https://doi.org/10.1109/wcnc61545.2025.10978498.

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5

Moreira, Rodrigo, Larissa Rodrigues, Pedro Rosa, and Flávio Silva. "Improving the network traffic classification using the Packet Vision approach." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13496.

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Анотація:

The network traffic classification allows improving the management, and the network services offer taking into account the kind of application. The future network architectures, mainly mobile networks, foresee intelligent mechanisms in their architectural frameworks to deliver application-aware network requirements. The potential of convolutional neural networks capabilities, widely exploited in several contexts, can be used in network traffic classification. Thus, it is necessary to develop methods based on the content of packets transforming it into a suitable input for CNN technologies. Hen

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6

Paredes-Farrera, M., M. Fleury, and M. Ghanbari. "Precision and accuracy of network traffic generators for packet-by-packet traffic analysis." In 2006 2nd International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities. IEEE, 2006. http://dx.doi.org/10.1109/tridnt.2006.1649124.

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7

S. P, Sujini, AnbuShamini G. N, and Prija J. S. "Deep Intrusion Detection for DOS and DDOS Attacks Using LSTM and Deep Autoencoder Neural Network." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/qkfn6548/ngcesi23p93.

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Early detection of network intrusions is a very important factor in network security. However, most studies of network intrusion detection systems utilize features for full sessions, making it difficult to detect intrusions before a session ends. To solve this problem, the proposed method uses packet data for features to determine if packets are malicious traffic. Such an approach inevitably increases the probability of falsely detecting normal packets as an intrusion or an intrusion as normal traffic for the initial session. As a solution, the proposed method learns the patterns of packets th

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8

MB, Anushlin Leena, Jegana R, and Abitha Rose P. "Network Traffic Identification Based On Machine Learning and Deep Packet Inspection." In 7th International Conference on Recent Innovations in Computer and Communication (ICRICC 23). International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/bdfm3626/icricc23p27.

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Accurate network traffic identification is an important basic for network traffic monitoring and data analysis and is the key to improve the quality of user service. In this project, through the analysis of two network traffic identification methods based on machine learning and deep packet inspection, a network traffic identification method based on machine learning and deep packet inspection is proposed. The deep packet inspection based on the feature library RuleLib, conducts in depth analysis of data traffic through pattern matching and identifies specific application traffic. Machine lear

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9

Haris, S. H. C., R. B. Ahmad, M. A. H. A. Ghani, and Ghossoon M. Waleed. "Packet analysis using packet filtering and traffic monitoring techniques." In 2010 International Conference on Computer Applications and Industrial Electronics (ICCAIE). IEEE, 2010. http://dx.doi.org/10.1109/iccaie.2010.5735088.

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10

Li, Kunhao, Bo Lang, Hongyu Liu, and Shaojie Chen. "SSL/TLS Encrypted Traffic Application Layer Protocol and Service Classification." In 11th International Conference on Embedded Systems and Applications (EMSA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120621.

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Анотація:

Network traffic protocols and service classification are the foundations of network quality of service (QoS) and security technologies, which have attracted increasing attention in recent years. At present, encryption technologies, such as SSL/TLS, are widely used in network transmission, so traditional traffic classification technologies cannot analyze encrypted packet payload. This paper first proposes a two-level application layer protocol classification model that combines packets and sessions information to address this problem. The first level extracts packet features, such as entropy an

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Звіти організацій з теми "Packet traffic"

1

Namazi, Ali, Babak Azimi-Sadjadi, and Chujen Lin. CAGE100: Real-Time Multi-Port Packet Capture System for 100 Gigabit Ethernet Traffic. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1069239.

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2

De Lucia, Michael, Daniel Krych, Stephen Raio, and Jason Ellis. An Empirical Investigation of Packet Header-Only Network Traffic Anomaly Detection and Classification. DEVCOM Army Research Laboratory, 2023. http://dx.doi.org/10.21236/ad1194755.

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3

Tayeb, Shahab. Protecting Our Community from the Hidden Vulnerabilities of Today’s Intelligent Transportation Systems. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2132.

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The ever-evolving technology interwoven into the transportation industry leaves it frequently at risk for cyber-attacks. This study analyzes the security of a common in-vehicle network, the Controller Area Network (CAN), standard in most vehicles being manufactured today. Like many other networks, CAN comes with inherent vulnerabilities that leave CAN implementations at risk of being targeted by cybercriminals. Such vulnerabilities range from eavesdropping, where the attacker can read the raw data traversing the vehicle, to spoofing, where the attacker can place fabricated traffic on the netwo

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4

Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Xueqian Shi. Guidelines for Evaluating Safety Using Traffic Encounters: Proactive Crash Estimation on Roadways with Conventional and Autonomous Vehicle Scenarios. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317587.

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With the expected arrival of autonomous vehicles, and the ever-increasing levels of automation in today’s human driven vehicles, road safety is changing at a rapid pace. This project aimed to address the need for an efficient and rapid method of safety evaluation and countermeasure identification via traffic encounters, specifically traffic conflicts that are considered useful surrogates of crashes. Recent research-delivered methods for estimating crash frequencies based on these events were observed in the field. In this project we developed a method for observing traffic encounters with two

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5

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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6

Kong, Zhihao, and Na Lu. Field Implementation of Concrete Strength Sensor to Determine Optimal Traffic Opening Time. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317724.

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In the fast-paced and time-sensitive fields of construction and concrete production, real-time monitoring of concrete strength is crucial. Traditional testing methods, such as hydraulic compression (ASTM C 39) and maturity methods (ASTM C 1074), are often laborious and challenging to implement on-site. Building on prior research (SPR 4210 and SPR 4513), we have advanced the electromechanical impedance (EMI) technique for in-situ concrete strength monitoring, crucial for determining safe traffic opening times. These projects have made significant strides in technology, including the development

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7

bell, Matthew, Marcel P. Huijser, and David Kack. Exploring Apex Predator Effects on Wildlife-Vehicle Collisions: A Case Study on Wolf Reintroductions in Yellowstone. Western Transportation Institute, 2024. http://dx.doi.org/10.15788/1727735675.

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This study investigates the impact of wolf reintroduction on wildlife-vehicle collisions (WVCs) along a segment of US-191 bordering Yellowstone National Park. Wolves were reintroduced in 1995–1996, and subsequent wolf pack establishment may have influenced the behavior and population dynamics of prey species, potentially altering WVC patterns. Using carcass data collected from 1989 to 2021, the analysis was divided into two primary phases: before wolves (1989–1996) and after wolves (1997–2021). A series of linear mixed-effects models were developed to assess changes in WVCs across these time p

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8

Chalmers, Seth. The Challenges of Next-gen ADAS and ADS and Related Vehicle Safety Topics. SAE International, 2025. https://doi.org/10.4271/epr2025003.

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<div class="section abstract"><div class="htmlview paragraph">Advanced driver assistance systems (ADAS) and automated driving systems (ADS) continue to expand into the market at a rapid pace. As improved (i.e., next generation) versions of these systems become available, they will continue to face many challenges in their implementation and benefits for safety and driving operations. The solution will involve many parties, including road safety professionals and researchers who see the potential in these systems but may have difficulties keeping up with them, and safety advocates w

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