TPF-IEHO: Tuning phantom features on traffic flow network behavioral conditions to detected DDos based on improved elephant herding optimization neural classification

Author(s):  
N. Umamaheswari ◽  
R. Renuga Devi
2016 ◽  
Vol 135 (0) ◽  
pp. 108-114 ◽  
Author(s):  
Tomoko SHIRAI ◽  
Nobuaki KUBO ◽  
Kenji INADA ◽  
Hitoi TAMARU

2007 ◽  
Author(s):  
Sihong Jiao ◽  
Yonghua Qu ◽  
Zhigang Liu ◽  
Quanxian Feng ◽  
Jie Ren ◽  
...  

2020 ◽  
Vol 13 (1) ◽  
pp. 266
Author(s):  
Jiayu Qin ◽  
Gang Mei ◽  
Lei Xiao

Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems.


2021 ◽  
Vol 10 (4) ◽  
pp. 227
Author(s):  
Yan Zhang ◽  
Xiang Zheng ◽  
Min Chen ◽  
Yingbing Li ◽  
Yingxue Yan ◽  
...  

The urban structure is the spatial reflection of various economic and cultural factors acting on the urban territory. Different from the physical structure, urban structure is closely related to the population mobility. Taxi trajectories are widely distributed, completely spontaneous, closely related to travel needs, and massive in data volume. Mining it not only can help us better understand the flow pattern of a city, but also provides a new perspective for interpreting the urban structure. On the basis of massive taxi trajectory data in Chengdu, we introduce a network science approach to analysis, propose a new framework for interaction analysis, and model the intrinsic connections within cities. The spatial grid of fine particles and the trajectory connections between them are used to resolve the urban structure. The results show that: (1) Based on 200,000 taxi trajectories, we constructed a spatial network of traffic flow using the interaction analysis framework and extracted the cold hot spots among them. (2) We divide the 400 traffic flow network nodes into 6 communities. Community 2 has high centrality and density, and belongs to the core built-up area of the city. (3) A traffic direction field is proposed to describe the direction of the traffic flow network, and the direction of traffic flow roughly presents an inflow from northeast to southwest and an outflow from southeast to northwest of the study area. The interaction analysis framework proposed in this study can be applied to other cities or other research areas (e.g., population migration), and it could extract the directional nature of the network as well as the hierarchical structure of the city.


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