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Journal articles on the topic 'Traffic source'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Gomes, João V. P., Pedro R. M. Inácio, Branka Lakic, Mário M. Freire, Henrique J. A. Da Silva, and Paulo P. Monteiro. "Source traffic analysis." ACM Transactions on Multimedia Computing, Communications, and Applications 6, no. 3 (2010): 1–23. http://dx.doi.org/10.1145/1823746.1823755.

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2

HOSAMO, MOHSEN. "Source Traffic Modeling Using Pareto Traffic Generator." Journal of Computer Networks 4, no. 1 (2017): 11–19. http://dx.doi.org/10.12691/jcn-4-1-2.

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3

Jelušić, Niko, Mario Anžek, and Božidar Ivanković. "Information Source Quality in Intelligent Transport Systems." PROMET - Traffic&Transportation 22, no. 2 (2012): 125–31. http://dx.doi.org/10.7307/ptt.v22i2.171.

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Advanced automatic traffic control systems and various other ITS (Intelligent Transport Systems) applications and services rely on real-time information from the traffic system. This paper presents the overview and general functions of different information sources which provide real-time information that are used or could be used in ITS. The objective is to formally define the quality of information sources suitable for ITS based on formal models of the traffic system and information sources. The definition of quality encompasses these essential factors: traffic system information that exists

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4

XU, Yiquan, Jinyu GUO, Rui LUO, et al. "Refinement method of equivalent source power level determination for traffic noise in urban noise mapping." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 270, no. 10 (2024): 1925–36. http://dx.doi.org/10.3397/in_2024_3105.

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For traffic noise prediction, the predictive models of source power level determination commonly in use are the Technical Guidelines for Noise Impact Assessment in China or the Calculation of Road Traffic Noise model in the United Kingdom, which incorporate traffic flow and car speeds. However, the accuracy of these models is often compromised due to urban traffic's complexity, vehicle diversity, and unforeseen events. This study investigates a refinement method based on noise on-site measurement and aforementioned approaches for equivalent source power level determination for traffic noise in

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5

Györfi, László, András Rácz, Ken Duffy, John T. Lewis, and Fergal Toomey. "Distribution-free confidence intervals for measurement of effective bandwidth." Journal of Applied Probability 37, no. 1 (2000): 224–35. http://dx.doi.org/10.1239/jap/1014842279.

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Hoeffding's inequality can be used in conjunction with the declared parameters of a traffic source, such as its peak rate, to obtain confidence intervals for measurements of the traffic's effective bandwidth. We describe a variety of interval-estimation procedures based on this idea, designed to provide differing degrees of robustness against non-stationarity. We also discuss how to compute confidence intervals for the effective bandwidth of an aggregate of traffic sources.

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Györfi, László, András Rácz, Ken Duffy, John T. Lewis, and Fergal Toomey. "Distribution-free confidence intervals for measurement of effective bandwidth." Journal of Applied Probability 37, no. 01 (2000): 224–35. http://dx.doi.org/10.1017/s0021900200015369.

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Hoeffding's inequality can be used in conjunction with the declared parameters of a traffic source, such as its peak rate, to obtain confidence intervals for measurements of the traffic's effective bandwidth. We describe a variety of interval-estimation procedures based on this idea, designed to provide differing degrees of robustness against non-stationarity. We also discuss how to compute confidence intervals for the effective bandwidth of an aggregate of traffic sources.

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7

Liu, Suping, Dongbo Zhang, and Jialin Li. "Study on Traffic Multi-Source Data Fusion." International Journal of Cognitive Informatics and Natural Intelligence 13, no. 2 (2019): 63–75. http://dx.doi.org/10.4018/ijcini.2019040105.

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In order to alleviate urban traffic congestion, it is necessary to obtain roadway network traffic flow parameters to estimate the traffic conditions. Single-detector data may not be sufficient to obtain a comprehensive, effective, accurate and high-quality traffic flow data. Neural networks and regression analysis data fusion methods are employed to expand data sources as well as for improving data quality. The multi-source detector data can provide fundamental support for traffic management. An empirical analysis was conducted using acquisition technology employed by the Beijing urban express

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8

Wang, Ruoyu, Zhen Liu, Ming Tao, and Ling Zhang. "Identifying Internet background radiation traffic based on traffic source distribution." Journal of High Speed Networks 21, no. 2 (2015): 107–20. http://dx.doi.org/10.3233/jhs-150512.

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9

Mijic, Zoran, Andreja Stojic, Mirjana Perisic, Slavica Rajsic, and Mirjana Tasic. "Receptor modeling studies for the characterization of PM10 pollution sources in Belgrade." Chemical Industry and Chemical Engineering Quarterly 18, no. 4-2 (2012): 623–34. http://dx.doi.org/10.2298/ciceq120104108m.

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The objective of this study is to determine the major sources and potential source regions of PM10 over Belgrade, Serbia. The PM10 samples were collected from July 2003 to December 2006 in very urban area of Belgrade and concentrations of Al, V, Cr, Mn, Fe, Ni, Cu, Zn, Cd and Pb were analyzed by atomic absorption spectrometry. The analysis of seasonal variations of PM10 mass and some element concentrations reported relatively higher concentrations in winter, what underlined the importance of local emission sources. The Unmix model was used for source apportionment purpose and the four main sou

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10

Menth, M., A. Binzenhofer, and S. Muhleck. "Source Models for Speech Traffic Revisited." IEEE/ACM Transactions on Networking 17, no. 4 (2009): 1042–51. http://dx.doi.org/10.1109/tnet.2008.2006222.

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11

Treiber, M., and A. Kesting. "An Open-Source Microscopic Traffic Simulator." IEEE Intelligent Transportation Systems Magazine 2, no. 3 (2010): 6–13. http://dx.doi.org/10.1109/mits.2010.939208.

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12

Borella, M. S. "Source models of network game traffic." Computer Communications 23, no. 4 (2000): 403–10. http://dx.doi.org/10.1016/s0140-3664(99)00197-8.

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13

Leduc, G., H. Abrahamsson, S. Balon, et al. "An open source traffic engineering toolbox." Computer Communications 29, no. 5 (2006): 593–610. http://dx.doi.org/10.1016/j.comcom.2005.06.010.

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14

Tran-Gia, Phuoc, Dirk Staehle, and Kenji Leibnitz. "Source Traffic Modeling of Wireless Applications." AEU - International Journal of Electronics and Communications 55, no. 1 (2001): 27–36. http://dx.doi.org/10.1078/1434-8411-00005.

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15

Fang, Ziquan, Lu Pan, Lu Chen, Yuntao Du, and Yunjun Gao. "MDTP." Proceedings of the VLDB Endowment 14, no. 8 (2021): 1289–97. http://dx.doi.org/10.14778/3457390.3457394.

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Traffic prediction has drawn increasing attention for its ubiquitous real-life applications in traffic management, urban computing, public safety, and so on. Recently, the availability of massive trajectory data and the success of deep learning motivate a plethora of deep traffic prediction studies. However, the existing neural-network-based approaches tend to ignore the correlations between multiple types of moving objects located in the same spatio-temporal traffic area, which is suboptimal for traffic prediction analytics. In this paper, we propose a multi-source deep traffic prediction fra

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16

Antoniou, Constantinos, Basil Psarianos, and Werner Brilon. "Induced traffic prediction inaccuracies as a source of traffic forecasting failure." Transportation Letters 3, no. 4 (2011): 253–64. http://dx.doi.org/10.3328/tl.2011.03.04.253-264.

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17

Jitendra, Patil, Tokekar Vrinda, and Rajan Alpana. "IP Source Lockdown to Detect and Mitigate Multi-Destination, Multi-Port, Multi-Protocol DDoS Attacks in SDN." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 11, no. 11 (2022): 29–40. https://doi.org/10.35940/ijitee.K9295.10111122.

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<strong>Abstract: </strong>Distributed Denial of Service (DDoS) attack is not a new attack and remains a challenging task. It has already been addressed by researchers and a lot of work has been done in this direction. Most of the work in Software-Defined-Network (SDN) environment focused on legacy DDoS attacks where targets are end servers. Legacy DDoS attack traffics are associated with a single destination and mostly the solutions are around this characteristic. In the case of SDN, the target is SDN controller plane whose overcharging brings the network to a complete halt. An attacker can a

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18

Can, Arnaud, Ludovic Leclercq, and Joël Lelong. "Selecting Noise Source and Traffic Representations that Capture Road Traffic Noise Dynamics Near Traffic Signals." Acta Acustica united with Acustica 95, no. 2 (2009): 259–69. http://dx.doi.org/10.3813/aaa.918148.

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19

Weber, Samuël, Gaëlle Uzu, Olivier Favez, et al. "Source apportionment of atmospheric PM<sub>10</sub> oxidative potential: synthesis of 15 year-round urban datasets in France." Atmospheric Chemistry and Physics 21, no. 14 (2021): 11353–78. http://dx.doi.org/10.5194/acp-21-11353-2021.

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Abstract. Reactive oxygen species (ROS) carried or induced by particulate matter (PM) are suspected of inducing oxidative stress in vivo, leading to adverse health impacts such as respiratory or cardiovascular diseases. The oxidative potential (OP) of PM, displaying the ability of PM to oxidize the lung environment, is gaining strong interest in examining health risks associated with PM exposure. In this study, OP was measured by two different acellular assays (dithiothreitol, DTT, and ascorbic acid, AA) on PM10 filter samples from 15 yearly time series of filters collected at 14 different loc

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20

Mi, Binbin, and Jianghai Xia. "Surface-Wave Imaging with Nonrandom Traffic Noise Sources." Journal of Physics: Conference Series 2651, no. 1 (2023): 012026. http://dx.doi.org/10.1088/1742-6596/2651/1/012026.

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Abstract We analyze the influence of nonrandom traffic noise sources on surface wave imaging. With nonrandom traffic noise sources in time (correlated sources), spurious signals are produced in the crosscorrelation functions. With nonrandom traffic noise sources in space (directional sources), phase velocities are overestimated from the retrieved surface waves. The retrieval of surface waves can be improved by stationary-phase segment selection, cross-coherence and stacking of the results from multiple traffic noise sources. Synthetic results demonstrate that cross-coherence and stacking of mu

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21

Baza-Solares, Nelson, Ruben Velasquez-Martínez, Cristian Torres-Bohórquez, Yerly Martínez-Estupiñán, and Cristian Poliziani. "Traffic Simulation with Open-Source and Commercial Traffic Microsimulators: A Case Study." Communications - Scientific letters of the University of Zilina 24, no. 2 (2022): E49—E62. http://dx.doi.org/10.26552/com.c.2022.2.e49-e62.

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22

Goo, Young-Hoon, Sun-Oh Choi, Su-Kang Lee, Sung-Min Kim, and Myung-Sup Kim. "Tracking the Source of Cascading Cyber Attack Traffic Using Network Traffic Analysis." Journal of Korean Institute of Communications and Information Sciences 41, no. 12 (2016): 1771–79. http://dx.doi.org/10.7840/kics.2016.41.12.1771.

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23

Hong, Rongrong, Huan Liu, Chengchuan An, Bing Wang, Zhenbo Lu, and Jingxin Xia. "An MFD Construction Method Considering Multi-Source Data Reliability for Urban Road Networks." Sustainability 14, no. 10 (2022): 6188. http://dx.doi.org/10.3390/su14106188.

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Road network traffic management and control are the key mechanisms to alleviate urban traffic congestion. With this study, we aimed to characterize the traffic flow state of urban road networks using the Macroscopic Fundamental Diagram (MFD) to support area traffic control. The core property of an MFD is that the network flow is maximized when network traffic stays at an optimal accumulation state. The property can be used to optimize the temporal and spatial distribution of traffic flow with applications such as gating control. MFD construction is the basis of these MFD-based applications. Al

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24

Shvetsov, Alexey. "ASPECTS OF TRAFFIC NOISE REDUCTION." VOLUME 39, VOLUME 39 (2021): 25. http://dx.doi.org/10.36336/akustika20213925.

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The level of noise in modern cities is growing every year, the main source of it is motor transport, the number of which is constantly increasing. This study examines the main sources of noise from a running car, as well as methods for reducing it. The results of the study can be applied in the design of gas exchange and cooling systems of cars, in addition, they can be used in urban planning to reduce traffic noise in the cities.

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25

Lechner, Schnaiter, and Bose-O’Reilly. "Combined Effects of Aircraft, Rail, and Road Traffic Noise on Total Noise Annoyance—A Cross-Sectional Study in Innsbruck." International Journal of Environmental Research and Public Health 16, no. 18 (2019): 3504. http://dx.doi.org/10.3390/ijerph16183504.

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Noise legislation in Austria does not provide an assessment of the cumulative effect of noise from different sources. The desire of citizens for a total noise assessment is getting stronger. Within the pilot project “Gesamtlärmbetrachtung” (Total Noise Investigation) Innsbruck, data from 1031 face-to-face interviews were correlated with exposure data from road, rail and air traffic noise. The interviews were selected in clusters according to the exposure combinations of these three sources. In addition to exposure-response relationships, it has also been found that the annoyance response to ai

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26

Soldo, Fabio, Katerina Argyraki, and Athina Markopoulou. "Optimal Source-Based Filtering of Malicious Traffic." IEEE/ACM Transactions on Networking 20, no. 2 (2012): 381–95. http://dx.doi.org/10.1109/tnet.2011.2161615.

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27

Tregybov, R. B., and A. N. Oreshin. "Traffic Source Model with Changeable Transmission Rate." Informatsionno-upravliaiushchie sistemy (Information and Control Systems) 4, no. 83 (2016): 51–59. http://dx.doi.org/10.15217/issn1684-8853.2016.4.51.

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28

Anagnostou, M. E., J. A. Sanchez-P., and I. S. Venieris. "A multiservice user descriptive traffic source model." IEEE Transactions on Communications 44, no. 10 (1996): 1243–46. http://dx.doi.org/10.1109/26.539763.

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29

Heyman, D. P., and T. V. Lakshman. "Source models for VBR broadcast-video traffic." IEEE/ACM Transactions on Networking 4, no. 1 (1996): 40–48. http://dx.doi.org/10.1109/90.503760.

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30

HE, Meiling, Guangrong MENG, Xiaohui WU, Xun HAN, and Jiangyang FAN. "Road Traffic Accident Prediction Based on Multi-Source Data – A Systematic Review." Promet - Traffic&Transportation 37, no. 2 (2025): 499–522. https://doi.org/10.7307/ptt.v37i2.763.

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With the acceleration of urbanisation and the rapid increase in road traffic volume, the scientific prediction of traffic accidents has become crucial for improving road safety and enhancing traffic efficiency. However, traffic accident prediction is a complex and multifaceted problem that requires the comprehensive consideration of multiple factors, including people, vehicles, roads and the environment. This paper provides a detailed analysis of traffic accident prediction based on multi-source data. By thoroughly considering data sources, data processing and prediction methods, this paper in

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31

He, Siyi, Kuiliang Jiang, and Jinghui Li. "Application of Multi-Source Remote Sensing Technology in Smart Transportation." Transactions on Environment, Energy and Earth Sciences 3 (November 26, 2024): 144–50. https://doi.org/10.62051/thzdyg80.

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Under the background of deepening urbanization, the rapid growth of traffic flow poses a great challenge to the traffic monitoring system. However, the development of intelligent transportation systems (ITS) integrates advanced information technology and multiple remote sensing technologies, thus opening up a new path for efficient urban traffic management. This study explores in depth the applications of unmanned aerial vehicle remote sensing technology, LiDAR technology, and airborne remote sensing technology in autonomous driving technology, traffic flow monitoring, road condition assessmen

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32

Musayev, Janat, and Algazy Zhauyt. "Analysis of Disturbing Influence of Traffic Load on Soil Body." Advances in Materials Science and Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/318289.

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Stress waves propagate in soil in case of earthquake and man-made effects (traffic flow, buried explosions, shield-driven pipes and tunnels, etc.). The wave point-sources are those located at the distances equal to more than two waves lengths, which significantly simplifies solving of a problem of these waves’ strength evaluation. Distribution of stress and displacement by the stress waves propagation in elastic medium is a complex pattern. The stress distribution in propagating waves depends on the type and form of source, conditions of the source contact with medium, and properties of medium

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33

Patil, Jitendra, Vrinda Tokekar, and Alpana Rajan. "IP Source Lockdown to Detect and Mitigate Multi-Destination, Multi-Port, Multi-Protocol DDoS Attacks in SDN." International Journal of Innovative Technology and Exploring Engineering 11, no. 11 (2022): 29–40. http://dx.doi.org/10.35940/ijitee.k9295.10111122.

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Abstract:

Distributed Denial of Service (DDoS) attack is not a new attack and remains a challenging task. It has already been addressed by researchers and a lot of work has been done in this direction. Most of the work in Software-Defined-Network (SDN) environment focused on legacy DDoS attacks where targets are end servers. Legacy DDoS attack traffics are associated with a single destination and mostly the solutions are around this characteristic. In the case of SDN, the target is SDN controller plane whose overcharging brings the network to a complete halt. An attacker can achieve this by customizing

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34

Mutalemwa, Lilian C., and Seokjoo Shin. "Secure Routing Protocols for Source Node Privacy Protection in Multi-Hop Communication Wireless Networks." Energies 13, no. 2 (2020): 292. http://dx.doi.org/10.3390/en13020292.

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Traffic analysis attacks are common in monitoring wireless sensor networks (WSNs). In the attacks, adversaries analyze the traffic pattern to obtain critical information such as the location information of a source node. Fake source packet routing protocols are often used to ensure source location privacy (SLP) protection. The protocols rely on broadcasting fake packets from fake sources concurrently with the transmission of real packets from the real source nodes to obfuscate the adversaries. However, fake source packet routing protocols have demonstrated some performance limitations includin

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35

Li, Minglun, Hu Yang, Bao Guo, Jianjun Dai, and Pu Wang. "Driver Source-Based Traffic Control Approach for Mitigating Congestion in Freeway Bottlenecks." Journal of Advanced Transportation 2022 (May 26, 2022): 1–13. http://dx.doi.org/10.1155/2022/3536979.

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On-ramp control is an effective way to mitigate traffic congestion in freeways. In this study, a traffic control approach is developed based on the OD data of a regional freeway to alleviate the traffic overload in freeway bottlenecks. We first locate the major driver sources of the freeway bottlenecks and identify the on-ramps for implementing the traffic control schemes. Next, the differential evolution algorithm is employed to calculate the optimal control time at each traffic control on-ramp. The results indicate that the major driver sources of the freeway bottlenecks are limited. Traffic

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36

Zheng, Lingyu, Hao Ma, and Zhongyu Wang. "Travel Time Estimation for Urban Arterials Based on the Multi-Source Data." Sustainability 16, no. 17 (2024): 7845. http://dx.doi.org/10.3390/su16177845.

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Accurate traffic information, such as travel time, becomes more important since it could help provide more efficient traffic management strategies. This paper presents a method for estimating the travel time of segments on urban arterials by leveraging multi-source data from loop detectors and probe vehicles. Travel time is defined into three distinct sections based on floating car trajectories, i.e., accelerating, constant speed, and decelerating. Considering the traffic flow characteristics, different methods are developed using various data for each section. The proposed methodology is vali

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37

Jeong, ManYong, Toru Yamazaki, Kai Kurihara, and Yoshihiro Shirahashi. "A study on source separation of traffic vehicle noise using deep learning." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 2 (2023): 6302–10. http://dx.doi.org/10.3397/in_2023_0931.

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In order to reduce the environmental damage caused by road traffic noise, it is not only necessary to measure the amount of traffic by vehicle type and the resulting noise level but also convert them into big data for monitoring them. To achieve this, a monitoring system needs to be developed that can more accurately classify vehicle types and evaluate the noise level of each vehicle in motion. In recent years, the development of AI has significantly improved the performance of vehicle counting systems, and these systems are already being used in traffic volume surveys. However, a system that

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38

Li, Tingting, Jun Li, Hongxing Jiang, et al. "Source Apportionment of PM2.5 in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon." Atmosphere 11, no. 5 (2020): 512. http://dx.doi.org/10.3390/atmos11050512.

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To accurately apportion the sources of aerosols, a combined method of positive matrix factorization (PMF) and the Bayesian mixing model was applied in this study. The PMF model was conducted to identify the sources of PM2.5 in Guangzhou. The secondary inorganic aerosol source was one of the seven main sources in Guangzhou. Based on stable isotopes of oxygen and nitrogen (δ15N-NO3− and δ18O-NO3−), the Bayesian mixing model was performed to apportion the source of NO3− to coal combustion, traffic emission and biogenic source. Then the secondary aerosol source was subdivided into three sources ac

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39

Shi, Yan, Arun Ross, and Subir Biswas. "Source identification of encrypted video traffic in the presence of heterogeneous network traffic." Computer Communications 129 (September 2018): 101–10. http://dx.doi.org/10.1016/j.comcom.2018.07.019.

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40

Can, Arnaud, Ludovic Leclercq, and Joël Lelong. "Dynamic estimation of urban traffic noise: Influence of traffic and noise source representations." Applied Acoustics 69, no. 10 (2008): 858–67. http://dx.doi.org/10.1016/j.apacoust.2007.05.014.

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41

Rantala, Pekka, Leena Järvi, Risto Taipale, et al. "Anthropogenic and biogenic influence on VOC fluxes at an urban background site in Helsinki, Finland." Atmospheric Chemistry and Physics 16, no. 12 (2016): 7981–8007. http://dx.doi.org/10.5194/acp-16-7981-2016.

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Abstract. We measured volatile organic compounds (VOCs), carbon dioxide (CO2) and carbon monoxide (CO) at an urban background site near the city centre of Helsinki, Finland, northern Europe. The VOC and CO2 measurements were obtained between January 2013 and September 2014 whereas for CO a shorter measurement campaign in April–May 2014 was conducted. Both anthropogenic and biogenic sources were identified for VOCs in the study. Strong correlations between VOC fluxes and CO fluxes and traffic rates indicated anthropogenic source of many VOCs. The VOC with the highest emission rate to the atmosp

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42

Zhou, Xu, Zhao Liu, Xiao Xiao Zhao, and Jian Hua Guo. "Framework for Dynamic OD Matrix Estimation Based on Multi-Source Traffic Data Fusion." Applied Mechanics and Materials 505-506 (January 2014): 1153–56. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.1153.

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The advanced transportation management and information systems (ATMIS) are strengthening the capability of collecting multi-source traffic data constantly from the road networks. Considering the fundamental role of dynamic Origin-Destination data for many advanced traffic management systems, it is promising to apply the multi-source traffic data to improve the dynamic OD estimation. Targeting dynamic OD data estimation, the classical OD data estimation approaches are discussed, and a framework of dynamic OD estimation based on multi-source traffic data is proposed and analyzed. Future research

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43

Koole, Ger, and Zhen Liu. "Stochastic Bounds for Queueing Systems with Multiple On–Off Sources." Probability in the Engineering and Informational Sciences 12, no. 1 (1998): 25–48. http://dx.doi.org/10.1017/s0269964800005040.

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Consider a queueing system where the input traffic consists of background traffic, modeled by a Markov Arrival Process, and foreground traffic modeled by N ≥ 1 homogeneous on–off sources. The queueing system has an increasing and concave service rate, which includes as a particular case multiserver queueing systems. Both the infinite-capacity and the finite-capacity buffer cases are analyzed. We show that the queue length in the infinite-capacity buffer system (respectively, the number of losses in the finite-capacity buffer system) is larger in the increasing convex order sense (respectively,

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44

Ding, Fan, Xiaoxuan Chen, Shanglu He, Guangming Shou, Zhen Zhang, and Yang Zhou. "Evaluation of a Wi-Fi Signal Based System for Freeway Traffic States Monitoring: An Exploratory Field Test." Sensors 19, no. 2 (2019): 409. http://dx.doi.org/10.3390/s19020409.

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Monitoring traffic states from the road is arousing increasing concern from traffic management authorities. To complete the picture of real-time traffic states, novel data sources have been introduced and studied in the transportation community for decades. This paper explores a supplementary and novel data source, Wi-Fi signal data, to extract traffic information through a well-designed system. An IoT (Internet of Things)-based Wi-Fi signal detector consisting of a solar power module, high capacity module, and IoT functioning module was constructed to collect Wi-Fi signal data. On this basis,

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45

Matsuda, Toru, and Ryuta Tomita. "Study on vibration measurement and sensory evaluation of combined vibration for detached houses." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 8 (2023): 904–13. http://dx.doi.org/10.3397/in_2023_0143.

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Sensory and psychological content accounts for 90% of all vibration complaints in Japan. Therefore, evaluation based on vibration sensation is very important. Traffic vibration is the most common source of vibration that causes complaints, when limited to detached houses. If traffic vibration is the source of complaints, occupants will suffer from vibration for a long period of time. Vibration transmitted by traffic to housing floors may not be either vertical or horizontal, but rather a combination of both vertical and horizontal vibration that exceed perceptual thresholds. Japanese standards

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46

Mei, Yuyang, Shenmin Wang, Mengjie Gong, and Jiazheng Chen. "Urban Traffic Dominance: A Dynamic Assessment Using Multi-Source Data in Shanghai." Sustainability 16, no. 12 (2024): 4956. http://dx.doi.org/10.3390/su16124956.

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This study redefines the evaluation of urban traffic dominance by integrating complex network theory with multi-source spatiotemporal trajectory data, addressing the dynamic nature of various transportation modes, including public transit and shared mobility. Traditional traffic studies, which focus predominantly on static road traffic characteristics, overlook the fluid dynamics integral to urban transport systems. We introduce Relative Weighted Centrality (RWC) as a novel metric for quantifying dynamic traffic dominance, combining it with traditional static metrics to forge a comprehensive t

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Benton, Kevin, L. Jean Camp, Tim Kelley, and Martin Swany. "Filtering Source-Spoofed IP Traffic Using Feasible Path Reverse Path Forwarding with SDN." International Journal of Computer and Communication Engineering 5, no. 6 (2016): 441–54. http://dx.doi.org/10.17706/ijcce.2016.5.6.441-454.

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S. Satish, S. Satish, and A. GowriDurga A. GowriDurga. "Multi-Path Source Routing Using Portfolio Selection For Traffic Allocation in Lossy Network." International Journal of Scientific Research 3, no. 1 (2012): 478–80. http://dx.doi.org/10.15373/22778179/jan2014/162.

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Beddows, D. C. S., R. M. Harrison, D. C. Green, and G. W. Fuller. "Receptor modelling of both particle composition and size distribution from a background site in London, UK." Atmospheric Chemistry and Physics 15, no. 17 (2015): 10107–25. http://dx.doi.org/10.5194/acp-15-10107-2015.

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Abstract. Positive matrix factorisation (PMF) analysis was applied to PM10 chemical composition and particle number size distribution (NSD) data measured at an urban background site (North Kensington) in London, UK, for the whole of 2011 and 2012. The PMF analyses for these 2 years revealed six and four factors respectively which described seven sources or aerosol types. These included nucleation, traffic, urban background, secondary, fuel oil, marine and non-exhaust/crustal sources. Urban background, secondary and traffic sources were identified by both the chemical composition and particle N

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Sarkar, Chinmoy, Vinayak Sinha, Baerbel Sinha, Arnico K. Panday, Maheswar Rupakheti, and Mark G. Lawrence. "Source apportionment of NMVOCs in the Kathmandu Valley during the SusKat-ABC international field campaign using positive matrix factorization." Atmospheric Chemistry and Physics 17, no. 13 (2017): 8129–56. http://dx.doi.org/10.5194/acp-17-8129-2017.

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Abstract. A positive matrix factorization model (US EPA PMF version 5.0) was applied for the source apportionment of the dataset of 37 non-methane volatile organic compounds (NMVOCs) measured from 19 December 2012 to 30 January 2013 during the SusKat-ABC international air pollution measurement campaign using a proton-transfer-reaction time-of-flight mass spectrometer in the Kathmandu Valley. In all, eight source categories were identified with the PMF model using the new constrained model operation mode. Unresolved industrial emissions and traffic source factors were the major contributors to

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