Traffic Behavior Analysis During the Transition from One Level of the Network Hierarchy to Another

The hierarchical network model is the most preferable in the design of computer networks, as it allows you to create a more stable structure of network, rationally allocate available resources, and also provide a higher degree of data protection. In this work, the study of the behavior of the traffic during the transition from one level of the network hierarchy to another, based on the study of the values of the traffic self-similarity degree during this transition. For the study, a simulation model of a computer network with a hierarchical topology was developed using the NS-3 simulator. Also, a window application was developed in the Visual C# programming language. With the help of this application the degree of self-similarity of the traffic was investigated using the files obtained as a result of processing the trace file. Thus, as a result of the study, it can be stated that any changes in the degree of self-similarity of the network traffic when this traffic moves from one level of the hierarchy to another level depends on such a condition as the direction of traffic movement. The initial degree of selfsimilarity of network traffic also effects on the network traffic self-similarity degree.

On detecting and identifying faulty internet of things devices and outages

As internet of things (IoT) devices play an integral role in our everyday life, it is critical to monitor the health of the IoT devices. However, fault detection in IoT is much more challenging compared with that in traditional wired networks. Traditional observing and polling are not appropriate for detecting faults in resource-constrained IoT devices. Because of the dynamic feature of IoT devices, these detection methods are inadequate for IoT fault detection. In this paper, we propose two methods that can monitor the health status of IoT devices through monitoring the network traffic of these devices. Based on the collected traffic or flow entropy, these methods can determine the health status of IoT devices by comparing captured traffic behavior with normal traffic patterns. Our measurements show that the two methods can effectively detect and identify malfunctioned or defective IoT devices.

Analyzing the delays of target lane vehicles caused by vehicle lane-changing operation

Vehicle lane-changing on urban roads is the most common traffic behavior, in which the driver changes the direction or increases the speed of the vehicle by changing its trajectory. However, in high-density traffic flow, when a vehicle changes lanes, a series of vehicles following the target vehicle in the target lane will be delayed. In this study, DJI Phantom 4 drones were used to vertically record the traffic on a road section. Tracker software was then used to extract vehicle information from the video taken by the drones, including the vehicle operating speeds, etc. SPSS 22 and Origin analysis software were then employed to analyze the correlations between different vehicle operating parameters. It was found that the operating speed of the first vehicle following the target vehicle in the target lane is related to the speeds and positions of both the target vehicle and the vehicle preceding it. Under the condition of high-density traffic flow, when the target vehicle is inserted into the target lane, the speed of the vehicles following the target vehicle in the target lane will change. To model this process, the corresponding Sine and DoseResp models were constructed. By calculating the delays of vehicles following the target vehicle in the target lane, it was concluded that the overall delay of the fleet is 3.9–9.5 s.

Heterogeneous traffic flow model under connected vehicles environment considering cyberattacks

Under the Vehicle-to-Vehicle (V2V) environment, connected vehicles (CVs) can share the traveling information with each other to keep the traffic flow stable. However, the open network cooperation environment makes CVs vulnerable to cyberattacks, which leads to changes in driving behavior. The existing theories divide cyberattacks into three types: bogus information, replay/delay and collusion cyberattacks. In addition, the mixed flow consisting of truck and car is a common form of road traffic. In order to clarify the potential impact of cyberattacks on mixed traffic flow, this paper proposes an extended car-following model considering cyberattacks under CVs environment. Subsequently, the stability of the model is analyzed theoretically, and the stability condition of the model is obtained. The numerical simulation is carried out and the result shows that the cyberattacks lead to different degrees of traffic behavior hazards such as queue time extension, congestion and even rear end collision. Among them, cooperative attack is the most serious.

Nothing but the truth? Effects of faking on the validity of the crosswise model

In self-reports, socially desirable responding threatens the validity of prevalence estimates for sensitive personal attitudes and behaviors. Indirect questioning techniques such as the crosswise model attempt to control for the influence of social desirability bias. The crosswise model has repeatedly been found to provide more valid prevalence estimates than direct questions. We investigated whether crosswise model estimates are also less susceptible to deliberate faking than direct questions. To this end, we investigated the effect of “fake good” instructions on responses to direct and crosswise model questions. In a sample of 1,946 university students, 12-month prevalence estimates for a sensitive road traffic behavior were higher and thus presumably more valid in the crosswise model than in a direct question. Moreover, “fake good” instructions severely impaired the validity of the direct questioning estimates, whereas the crosswise model estimates were unaffected by deliberate faking. Participants also reported higher levels of perceived confidentiality and a lower perceived ease of faking in the crosswise model compared to direct questions. Our results corroborate previous studies finding the crosswise model to be an effective tool for counteracting the detrimental effects of positive self-presentation in surveys on sensitive issues.

Traffic Compliance Index of Acehnese Community

The purpose of this article is to analyze and discuss the correlation between the behavior of the Acehnese and the culture of traffic discipline. Every road user is obliged to obey the traffic rules, but the number of accidents is increasing every year, resulting in numerous deaths, injuries and property damage. This is an empirical legal research using a survey method with a qualitative approach. Interviews, observations, and file review is also used in this research. The results show that the traffic behavior of Acehnese remains poor, with the observation-based traffic compliance index having a value of 5.41 on a scale of 1-10. Most violations were committed by male drivers with 2 (two) wheels. The most common violations are not wearing a helmet, not turning on the main lights during the day, and not having a driver's license (SIM). Most traffic violations are committed by individuals between the ages of 17 and 40, most of whom have a high school education or equivalent. It is necessary to optimize the socialization of traffic discipline and the arrangement of traffic fiqh by the Aceh Provincial Police of Traffic Department (Ditlantas Polda Aceh), local authorities and related agencies.