scholarly journals Uma Nova Metodologia para formação de Grupos em VANETs

2020 ◽  
Author(s):  
André Vieira ◽  
Claudio Farias ◽  
Wilson Melo Jr.
Keyword(s):  
Traffic Data ◽  
Distributed Protocol ◽  
Smart Vehicles ◽  
Real Traffic

This work proposes a new methodology to create groups in intervehicular networks as a basis for complex applications involving smart vehicles. We proposed a distributed protocol whose purpose is to build a higher number of groups in less time, keeping the vehicles grouped for most of their journey. We implement our proposal in two versions. The first one considers only the interaction among vehicles. The second includes a group merge functionality. We validate both versions of our protocol using simulation with real traffic data. We evaluate the amount of created groups and their persistence and size.

Development of Multiregime Speed–Density Relationships by Cluster Analysis

2005 ◽  
Vol 1934 (1) ◽  
pp. 64-71 ◽  
Author(s):  
Lu Sun ◽  
Jie Zhou
Keyword(s):  
Cluster Analysis ◽  
Traffic Flow ◽  
Ad Hoc ◽  
Data Sets ◽  
Traffic Data ◽  
Microscopic Traffic Simulation ◽  
Car Following ◽  
Data Segmentation ◽  
Essential Components ◽  
Real Traffic

Empirical speed–density relationships are important not only because of the central role that they play in macroscopic traffic flow theory but also because of their connection to car-following models, which are essential components of microscopic traffic simulation. Multiregime traffic speed– density relationships are more plausible than single-regime models for representing traffic flow over the entire range of density. However, a major difficulty associated with multiregime models is that the breakpoints of regimes are determined in an ad hoc and subjective manner. This paper proposes the use of cluster analysis as a natural tool for the segmentation of speed–density data. After data segmentation, regression analysis can be used to fit each data subset individually. Numerical examples with three real traffic data sets are presented to illustrate such an approach. Using cluster analysis, modelers have the flexibility to specify the number of regimes. It is shown that the K-means algorithm (where K represents the number of clusters) with original (nonstandardized) data works well for this purpose and can be conveniently used in practice.

scholarly journals Capturing the spatio-temporal behavior of real traffic data

2002 ◽  
Vol 49 (1-4) ◽  
pp. 147-163 ◽  
Author(s):  
Mengzhi Wang ◽  
Anastassia Ailamaki ◽  
Christos Faloutsos
Keyword(s):  
Temporal Behavior ◽  
Traffic Data ◽  
Spatio Temporal ◽  
Real Traffic

Enforcing Effective Rates for Packet-Level QoS Control in IP Networks: Theory and Validation Based on Real Traffic Data

2004 ◽  
Vol 27 (1) ◽  
pp. 9-31 ◽  
Author(s):  
John Soldatos ◽  
Evangelos Vayias ◽  
Panagiotis Stathopoulos ◽  
Nikolas Mitrou
Keyword(s):  
Ip Networks ◽  
Traffic Data ◽  
Qos Control ◽  
Real Traffic

scholarly journals Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment

2021 ◽  
Vol 11 (21) ◽  
pp. 9914
Author(s):  
Aleksandra Romanowska ◽  
Kazimierz Jamroz
Keyword(s):  
Traffic Flow ◽  
Deterministic Model ◽  
Empirical Relationship ◽  
Flow Speed ◽  
Traffic Data ◽  
Flow Models ◽  
Flow Parameters ◽  
Fundamental Relationship ◽  
Traffic Flow Models ◽  
Real Traffic

The fundamental relationship of traffic flow and bivariate relations between speed and flow, speed and density, and flow and density are of great importance in transportation engineering. Fundamental relationship models may be applied to assess and forecast traffic conditions at uninterrupted traffic flow facilities. The objective of the article was to analyze and compare existing models of the fundamental relationship. To that end, we proposed a universal and quantitative method for assessing models of the fundamental relationship based on real traffic data from a Polish expressway. The proposed methodology seeks to address the problem of finding the best deterministic model to describe the empirical relationship between fundamental traffic flow parameters: average speed, flow, and density based on simple and transparent criteria. Both single and multi-regime models were considered: a total of 17 models. For the given data, the results helped to identify the best performing models that meet the boundary conditions and ensure simplicity, empirical accuracy, and good estimation of traffic flow parameters.

Street Sections Design Based on Real Traffic Data: Case Study of Málaga, Spain

2019 ◽  
Vol 145 (4) ◽  
pp. 04019010
Author(s):  
Alberto E. García-Moreno ◽  
Carlos Rosa-Jiménez ◽  
Carlos Prados-Gómez ◽  
María José Márquez-Ballesteros ◽  
Pedro Lázaro ◽  
...  
Keyword(s):  
Traffic Data ◽  
Real Traffic

scholarly journals Real traffic-data based evaluation of vehicular traffic environment and state-of-the-art with future issues in location-centric data dissemination for VANETs

2017 ◽  
Vol 3 (3) ◽  
pp. 195-210 ◽  
Author(s):  
Abdul Hafidz Abdul Hanan ◽  
Mohd. Yazid Idris ◽  
Omprakash Kaiwartya ◽  
Mukesh Prasad ◽  
Rajiv Ratn Shah
Keyword(s):  
Data Dissemination ◽  
State Of The Art ◽  
Vehicular Traffic ◽  
Traffic Data ◽  
Traffic Environment ◽  
Real Traffic

scholarly journals Interaction-Aware Behavior Planning for Autonomous Vehicles Validated With Real Traffic Data

2020 ◽  
Author(s):  
Jinning Li ◽  
Liting Sun ◽  
Wei Zhan ◽  
Masayoshi Tomizuka
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Change Scenario ◽  
Planning Problem ◽  
Traffic Data ◽  
Monte Carlo Tree Search ◽  
Lane Changes ◽  
Markov Decision ◽  
Partially Observable ◽  
Real Traffic

Abstract Autonomous vehicles (AVs) need to interact with other traffic participants who can be either cooperative or aggressive, attentive or inattentive. Such different characteristics can lead to quite different interactive behaviors. Hence, to achieve safe and efficient autonomous driving, AVs need to be aware of such uncertainties when they plan their own behaviors. In this paper, we formulate such a behavior planning problem as a partially observable Markov Decision Process (POMDP) where the cooperativeness of other traffic participants is treated as an unobservable state. Under different cooperativeness levels, we learn the human behavior models from real traffic data via the principle of maximum likelihood. Based on that, the POMDP problem is solved by Monte-Carlo Tree Search. We verify the proposed algorithm in both simulations and real traffic data on a lane change scenario, and the results show that the proposed algorithm can successfully finish the lane changes without collisions.

scholarly journals A Novel Vehicle Detection Framework Based on Parallel Vision

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Ying Zhuo ◽  
Lan Yan ◽  
Wenbo Zheng ◽  
Yutian Zhang ◽  
Chao Gou
Keyword(s):  
Visual Information ◽  
Large Scale ◽  
Vehicle Detection ◽  
Real Data ◽  
Autonomous Driving ◽  
Research Topic ◽  
Traffic Data ◽  
Detection Model ◽  
Real Traffic ◽  
Virtual Data

Autonomous driving has become a prevalent research topic in recent years, arousing the attention of many academic universities and commercial companies. As human drivers rely on visual information to discern road conditions and make driving decisions, autonomous driving calls for vision systems such as vehicle detection models. These vision models require a large amount of labeled data while collecting and annotating the real traffic data are time-consuming and costly. Therefore, we present a novel vehicle detection framework based on the parallel vision to tackle the above issue, using the specially designed virtual data to help train the vehicle detection model. We also propose a method to construct large-scale artificial scenes and generate the virtual data for the vision-based autonomous driving schemes. Experimental results verify the effectiveness of our proposed framework, demonstrating that the combination of virtual and real data has better performance for training the vehicle detection model than the only use of real data.

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