Traffic behavior in a mixture of different vehicles

2000 ◽  
Vol 284 (1-4) ◽  
pp. 405-420 ◽  
Author(s):  
Takashi Nagatani
Keyword(s):  
Traffic Behavior

scholarly journals Identification of a Longitudinal Human Driving Model for Adaptive Cruise Control Performance Assessment

2002 ◽  
Author(s):  
Kangwon Lee ◽  
Huei Peng
Keyword(s):  
Adaptive Cruise Control ◽  
University Of Michigan ◽  
Cruise Control ◽  
Modified Model ◽  
Driving Model ◽  
Vehicle Motion ◽  
Transportation Research ◽  
The University ◽  
Traffic Behavior ◽  
Control Performance Assessment

The main purpose of this paper is to develop a longitudinal human driving model that is accurate enough for the performance evaluation of adaptive cruise control systems. Six driver models were evaluated based on selected data from two vehicle motion databases—the SAVME database and the ICCFOT database, both created at the University of Michigan Transportation Research Institute (UMTRI). Among the models we evaluated, the Gipps’ model was found to be the most promising and was further analyzed. A modified version of the model was suggested and evaluated. The modified model was implemented in a microscopic traffic simulator and was found to produce results that agree with macroscopic traffic behavior very well.

scholarly journals Research on QoS Classification of Network Encrypted Traffic Behavior Based on Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1376
Author(s):  
Yung-Fa Huang ◽  
Chuan-Bi Lin ◽  
Chien-Min Chung ◽  
Ching-Mu Chen
Keyword(s):  
Machine Learning ◽  
Majority Voting ◽  
Deep Packet Inspection ◽  
Traffic Classification ◽  
K 13 ◽  
Packet Inspection ◽  
Majority Voting Method ◽  
Encrypted Traffic ◽  
Traffic Behavior

In recent years, privacy awareness is concerned due to many Internet services have chosen to use encrypted agreements. In order to improve the quality of service (QoS), the network encrypted traffic behaviors are classified based on machine learning discussed in this paper. However, the traditional traffic classification methods, such as IP/ASN (Autonomous System Number) analysis, Port-based and deep packet inspection, etc., can classify traffic behavior, but cannot effectively handle encrypted traffic. Thus, this paper proposed a hybrid traffic classification (HTC) method based on machine learning and combined with IP/ASN analysis with deep packet inspection. Moreover, the majority voting method was also used to quickly classify different QoS traffic accurately. Experimental results show that the proposed HTC method can effectively classify different encrypted traffic. The classification accuracy can be further improved by 10% with majority voting as K = 13. Especially when the networking data are using the same protocol, the proposed HTC can effectively classify the traffic data with different behaviors with the differentiated services code point (DSCP) mark.

Traffic Behavior and Freeway Ramp Design

1963 ◽  
Vol 128 (4) ◽  
pp. 1-19
Author(s):  
Charles Pinnell ◽  
Charles J. Keese
Keyword(s):  
Traffic Behavior

scholarly journals ANALISIS KAPASITAS DAN KINERJA LALU LINTAS PADA RUAS JALAN JENDERAL SUDIRMAN JAKARTA

2021 ◽  
Vol 4 (1) ◽  
pp. 95
Author(s):  
Sarah Haryati ◽  
Najid Najid
Keyword(s):  
Flow Speed ◽  
Traffic Volume ◽  
Capital City ◽  
Free Flow ◽  
Degree Of Saturation ◽  
Level Of Service ◽  
Traffic Jam ◽  
Traffic Capacity ◽  
Traffic Behavior

Jakarta as the capital city of Indonesia is the center of economy, culture, and politics. Jenderal Sudirman street always crowded with passing vehicles, traffic snarls up everyday. The causes of these traffic jam is an increase the number of vehicles and cause a change in traffic behavior. Theoretically there is a fudamental relationship between flow, speed, & density, so the purpose of these research are to analyze and evaluate performance of traffic capacity in various conditions based on Manual Kapasitas Jalan Indonesia 1997 and Greenshields model. Conclusion of the analysis are, after compared with traffic volume, capacity and speed based on MKJI are 3.127,6 pcu/hour and 55,7 km/hour, but the capacity of the model are selected because it’s largest, for sudirman – thamrin it’s 8.272,5 pcu/hour, and for thamrin – sudirman it’s 8.067,9 pcu/hour, While the calculation of free flow for sudirman – thamrin it’s 41.2 km/hour the lowest occurs in  evening, and for thamrin – sudirman it’s 43,9 km/hour the lowest occurs in  afternoon. The largest capacity it’s used for the next analysis, the next analysis are calculating degree of saturation and level of service, the result  shows that the roads are at C and D.ABSTRAKJakarta ibu kota negara Indonesia merupakan pusat ekonomi, budaya, dan politik. Sebuah jalan di Jakarta yaitu Jenderal Sudirman selalu dipadati kendaraan. Lalu lintas di Jalan Jenderal Sudirman setiap hari mengalami kemacetan penyebabnya adalah peningkatan jumlah kendaraan di dalam kota dan menyebabkan perubahan perilaku lalu lintas, secara teoritis terdapat hubungan yang mendasar antara arus, kecepatan, dan kepadatan. Tujuan penelitian ini adalah untuk menganalisis, mengevaluasi kinerja dan kapasitas lalu lintas di berbagai macam kondisi, tentu berdasarkan pedoman Manual Kapasitas Jalan Indonesia dan kapasitas model Greenshields. Dari hasil analisis hasil perhitungan kapasitas dan kecepatan arus bebas berdasarkan MKJI sebesar 3.127,6 smp/jam dan 55,7 km/jam setelah dibandingkan dengan volume lalu lintas dipilih kapasitas model yang terbesar yaitu sebesar 8.272,5 smp/jam pada sudirman - thamrin & 8.067,9 smp/jam pada thamrin - sudirman, dan hasil perhitungan kecepatan arus bebas terendah sebesar 41,2 km/jam di sore hari untuk sudirman - thamrin, sebaliknya thamrin - sudirman terendah sebesar 43,9 km/jam di siang hari. Gunakan kapasitas yang terpilih tersebut untuk analisis berikutnya yaitu perhitungan ratio perbandingan arus dan kapasitas (DS) dan tingkat pelayanan yan berada pada tingkat pelayanan huruf C dan D di kedua arahnya.

scholarly journals Energy Efficient Technique for Concealing Sink Location in WSN

2020 ◽  
Vol 8 (10S2) ◽  
pp. 72-78
Keyword(s):  
Sensor Network ◽  
Energy Efficient ◽  
Location Privacy ◽  
Life Time ◽  
Traffic Analysis ◽  
Energy Utilization ◽  
Efficient Technique ◽  
Critical Location ◽  
Traffic Behavior ◽  
Sink Location

In a wireless sensor network, concealing the location of the sink is critical. Location of the sink can be revealed (or at least guessed with a high probability of success) through traffic analysis. In this paper, we proposed an energy efficient technique for concealing sink location named EESLP (Energy Efficient Sink Location Privacy) scheme. Here we proposed an approach, in which we are concealing the sink location in such a way so that node energy utilization while securing sink in network can be minimum, to defending sink’s location privacy and identity when the network is subjected to multiple traffic analysis attack. EESLP designs the network area of coverage with multiple spots generating fake message traffic for fake sink location creation that resembles the traffic behavior that is expected to be observed in the area where the sink is located. To achieve this we select some sensors away from the actual sink location which act as fake sinks by generating dummy or fake packet. The simulation results prove that EESLP can improve network life time and QOS (congestion, throughput, packet delivery rate) of sensor network while protecting sinks Location privacy.

On Traffic Behavior of Persons of Advanced Age

1983 ◽  
Vol 18 (0) ◽  
pp. 421-426
Author(s):  
Koshiro Shimizu ◽  
Masanao Motoki
Keyword(s):  
Advanced Age ◽  
Traffic Behavior

Heterogeneous traffic flow model under connected vehicles environment considering cyberattacks

2021 ◽  
Author(s):  
Lizhen Lin ◽  
Hongxia Ge ◽  
Rongjun Cheng
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Heterogeneous Traffic ◽  
Connected Vehicles ◽  
Vehicle To Vehicle ◽  
Car Following Model ◽  
Time Extension ◽  
Potential Impact ◽  
The Stability ◽  
Traffic Behavior

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.

Hybrid Intelligence for Smarter Networking Operations

2017 ◽  
pp. 238-270
Author(s):  
Bassem Mahmoud Mokhtar ◽  
Mohamed Eltoweissy
Keyword(s):  
Anomalous Behavior ◽  
High Dimensionality ◽  
Network Data ◽  
Hybrid Intelligence ◽  
Future Events ◽  
Network Operations ◽  
Multiple Media ◽  
High Level ◽  
Traffic Behavior ◽  
Different Sources

The ever-growing and ever-evolved Internet targets supporting billions of networked entities to provide a wide variety of services and resources. Such complexity results in network-data from different sources with special characteristics, such as widely diverse users, multiple media, high-dimensionality and various dynamic concerns. With huge amounts of network-data with such characteristics, there are significant challenges to a) recognize emergent and anomalous behavior in network-traffic and b) make intelligent decisions for efficient network operations. Endowing the semantically-oblivious Internet with Intelligence would advance the Internet capability to learn traffic behavior and to predict future events. In this chapter, the authors discuss and evaluate the hybridization of monolithic intelligence techniques in order to achieve smarter and enhanced networking operations. Additionally, the authors provide systematic application-agnostic semantics management methodology with efficient processes for extracting and classifying high-level features and reasoning about rich semantics.

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