Impact of heavy vehicles on surrounding traffic characteristics

2014 ◽  
Vol 49 (4) ◽  
pp. 535-552 ◽  
Author(s):  
Sara Moridpour ◽  
Ehsan Mazloumi ◽  
Mahmoud Mesbah
Keyword(s):  
Heavy Vehicles ◽  
Traffic Characteristics

Analysing the Performance of a Fuzzy Lane Changing Model Using Data Mining

2015 ◽  
pp. 1540-1566
Author(s):  
Sara Moridpour
Keyword(s):  
Traffic Safety ◽  
Traffic Simulation ◽  
Heavy Traffic ◽  
Heavy Vehicle ◽  
Heavy Vehicles ◽  
Passenger Cars ◽  
Lane Changing ◽  
Microscopic Traffic Simulation ◽  
Traffic Characteristics ◽  
Using Data

Heavy vehicles have substantial impact on traffic flow particularly during heavy traffic conditions. Large amount of heavy vehicle lane changing manoeuvres may increase the number of traffic accidents and therefore reduce the freeway safety. Improving road capacity and enhancing traffic safety on freeways has been the motivation to establish heavy vehicle lane restriction strategies to reduce the interaction between heavy vehicles and passenger cars. In previous studies, different heavy vehicle lane restriction strategies have been evaluated using microscopic traffic simulation packages. Microscopic traffic simulation packages generally use a common model to estimate the lane changing of heavy vehicles and passenger cars. The common lane changing models ignore the differences exist in the lane changing behaviour of heavy vehicle and passenger car drivers. An exclusive fuzzy lane changing model for heavy vehicles is developed and presented in this chapter. This fuzzy model can increase the accuracy of simulation models in estimating the macroscopic and microscopic traffic characteristics. The results of this chapter shows that using an exclusive lane changing model for heavy vehicles, results in more reliable evaluation of lane restriction strategies.

Analysing the Performance of a Fuzzy Lane Changing Model Using Data Mining

2013 ◽  
pp. 289-315 ◽  
Author(s):  
Sara Moridpour
Keyword(s):  
Traffic Safety ◽  
Traffic Simulation ◽  
Heavy Traffic ◽  
Heavy Vehicle ◽  
Heavy Vehicles ◽  
Passenger Cars ◽  
Lane Changing ◽  
Microscopic Traffic Simulation ◽  
Traffic Characteristics ◽  
Using Data

Heavy vehicles have substantial impact on traffic flow particularly during heavy traffic conditions. Large amount of heavy vehicle lane changing manoeuvres may increase the number of traffic accidents and therefore reduce the freeway safety. Improving road capacity and enhancing traffic safety on freeways has been the motivation to establish heavy vehicle lane restriction strategies to reduce the interaction between heavy vehicles and passenger cars. In previous studies, different heavy vehicle lane restriction strategies have been evaluated using microscopic traffic simulation packages. Microscopic traffic simulation packages generally use a common model to estimate the lane changing of heavy vehicles and passenger cars. The common lane changing models ignore the differences exist in the lane changing behaviour of heavy vehicle and passenger car drivers. An exclusive fuzzy lane changing model for heavy vehicles is developed and presented in this chapter. This fuzzy model can increase the accuracy of simulation models in estimating the macroscopic and microscopic traffic characteristics. The results of this chapter shows that using an exclusive lane changing model for heavy vehicles, results in more reliable evaluation of lane restriction strategies.

scholarly journals EFFECT OF THREE-WHEELED VEHICLES ON THE CAPACITY OF A TRAFFIC STREAM

2021 ◽  
Vol 25 (Special) ◽  
pp. 3-165-3-173
Author(s):  
Sarah H. Abdulamer ◽  
◽  
Hamid A. Eedan ◽  
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Flow Density ◽  
Heavy Vehicles ◽  
Small Space ◽  
Traffic Characteristics ◽  
Velocity Flow ◽  
Velocity Relationship ◽  
Traffic Stream ◽  
Wheeled Vehicles

Due to the tremendous development of the number and types of vehicles and a large number of traffic congestions, the phenomenon of the spread of three-wheeled vehicles, which is characterized by ease of movement, has recently appeared in Iraq because of a small space it occupies for its movement. The increase in their numbers exceeds the number of heavy vehicles in most urban areas in Iraq. Therefore, the current study has been devoted to studying the effect of those vehicles with three tires using a simulation program that has been previously developed and has been calibrated to suit the normal sections where the characteristics of these vehicles were included in terms of speed and length. Different ratios were used, ranging from (0-15) % for the first lane only. Some general traffic characteristics were examined, such as velocity-flow and flow-density and density-velocity relationship. The results showed that the presence of this type of vehicle has a significant impact on reducing road efficiency and increasing traffic congestion. The study recommends that a particular lane with a width of 2m on both directions of the roads be designated for the movement of these vehicles and keep them away from interference in the traffic flow of other cars.

scholarly journals Impact of Horizontal Curves and Percentage of Heavy Vehicles on Right Lane Capacity at Multi-lane Highways

2017 ◽  
Vol 29 (3) ◽  
pp. 299-309
Author(s):  
Ahmed Mohamed Semeida
Keyword(s):  
Heavy Vehicles ◽  
Highway Design ◽  
Horizontal Curves ◽  
Traffic Characteristics ◽  
Capacity Loss ◽  
Traditional Procedure ◽  
Lane Width ◽  
Road Geometry ◽  
Traffic Composition ◽  
The Right

In the present research, the influence of road geometric properties and traffic characteristics on the right lane capacity value is explored for horizontal curves. The non-traditional procedure (artificial neural networks - ANNs), is adopted for modelling. The research utilizes 78 horizontal curves that provide the traffic and road geometry data, of which55 curves are classified as four-lane and the rest as six-lane ones. Two types of models are introduced to explore the right lane capacity as capacity at curves, and the capacity loss between curves and tangents. The results show that, for horizontal curves, the most effective variables affecting both road types are the percentage of heavy vehicles in traffic composition (HV) followed by radius of curve (R), and the lane width (LW). Furthermore, the capacity loss is also highly affected by R followed by HV. The derived outcomes present a remarkable move towards the beginning of an Egyptian highway design guide.

scholarly journals Methodology For Determining Reliable Traffic Parameters For Current Analysis Of Performance Of Motorways And Expressways

2019 ◽  
Vol 14 (1) ◽  
pp. 104-123
Author(s):  
Malwina Spławińska
Keyword(s):  
Direct Conversion ◽  
Global Scale ◽  
Traffic Volume ◽  
Research Trend ◽  
Heavy Vehicles ◽  
Traffic Characteristics ◽  
Measurement Results ◽  
Random Measurements ◽  
Design Hourly Volume ◽  
Analysis Of Performance

In this paper, the results of analyses concerning selected traffic characteristics typical for Polish motorways and expressways are presented. The input data were collected automatically by stations located on various highways. In the first place, with the use of the coefficient of variability, periods with the lowest traffic volume variability in the year and the day were determined. On this basis, the most favourable time scope of random measurements was determined to allow reliable estimation of traffic parameters for road performance analyses. Then, based on model relationships between the characteristics of traffic volume variability over time and constant volume (regression relationships, a model of Artificial Neural Networks), correction factors were developed enabling direct conversion of the obtained measurement results into Design Hourly Volume. In addition, the rules for determining the share of heavy vehicles meeting the conditions at peak hours of the year were developed. The presented approach is in line with the current research trend on a global scale and allows for improving the accuracy of estimating Design Hourly Volume by 20 per cent concerning the method currently recommended in Poland.

High Speed Information Network Planning using Stochastic Optimization Method

2013 ◽  
Author(s):  
Ki-Sang Song ◽  
Arun K. Somani
Keyword(s):  
Stochastic Optimization ◽  
Network Design ◽  
Multimedia Information ◽  
High Speed ◽  
Minimum Cost ◽  
Optimization Method ◽  
Multimedia Communication ◽  
Network Design Problem ◽  
Information Network ◽  
Traffic Characteristics

From the 1994 CAIS Conference: The Information Industry in Transition McGill University, Montreal, Quebec. May 25 - 27, 1994.Broadband integrated services digital network (B-ISDN) based on the asynchronous transmission mode (ATM) is becoming reality to provide high speed, multi bit rate multimedia communications. Multimedia communication network has to support voice, video and data traffics that have different traffic characteristics, delay sensitive or loss sensitive features have to be accounted for designing high speed multimedia information networks. In this paper, we formulate the network design problem by considering the multimedia communication requirements. A high speed multimedia information network design alogrithm is developed using a stochastic optimization method to find good solutions which meet the Quality of Service (QoS) requirement of each traffic class with minimum cost.

scholarly journals HTPC: heterogeneous traffic-aware partition coding for random packet spraying in data center networks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jiawei Huang ◽  
Shiqi Wang ◽  
Shuping Li ◽  
Shaojun Zou ◽  
Jinbin Hu ◽  
...  
Keyword(s):  
Data Center ◽  
Large Scale ◽  
Network Performance ◽  
Rooted Tree ◽  
Heterogeneous Traffic ◽  
Data Center Networks ◽  
Packet Reordering ◽  
Traffic Characteristics ◽  
Network Utilization ◽  
The Impact

AbstractModern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

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