scholarly journals Utilizing UAVs Technology on Microscopic Traffic Naturalistic Data Acquirement

2021 ◽  
Vol 6 (6) ◽  
pp. 89
Author(s):  
Apostolos Anagnostopoulos ◽  
Fotini Kehagia
Keyword(s):  
Traffic Flow ◽  
Field Experiments ◽  
Flow Characteristics ◽  
Cost Effective ◽  
Transport Systems ◽  
Fundamental Aspect ◽  
Traffic Data ◽  
Vehicle Trajectories ◽  
Traffic Data Collection ◽  
Vehicle Trajectory

Research into collecting and measuring reliable, accurate, and naturalistic microscopic traffic data is a fundamental aspect in road network planning scientific literature. The vehicle trajectory is one of the main variables in traffic flow theory that allows to extract information regarding microscopic traffic flow characteristics. Several methods and techniques have been applied regarding the acquisition of vehicle trajectory. The forthcoming applications of intelligent transport systems on vehicles and infrastructure require sufficient and innovative tools to calibrate existing models on more complex situations. Unmanned aerial vehicles (UAVs) are one of the most emerging technologies being used recently in the transportation field to monitor and analyze the traffic flow. The aim of this paper is to examine the use of UAVs as a tool for microscopic traffic data collection and analysis. A comprehensive guiding framework for accurate and cost-effective naturalistic traffic surveys and analysis using UAVs is proposed and presented in detail. Field experiments of acquiring vehicle trajectories on two multilane roundabouts were carried out following the proposed framework. Results of the experiment indicate the usefulness of the UAVs technology on various traffic analysis studies. The results of this study provide a practical guide regarding vehicle trajectory acquirement using UAVs.

scholarly journals Overview of emerging road traffic data collection methods

2021 ◽  
Author(s):  
Sandra Mihalinac ◽  
Maja Ahac ◽  
Saša Ahac ◽  
Miroslav Šimun
Keyword(s):  
Data Collection ◽  
Traffic Flow ◽  
Road Network ◽  
Road Traffic ◽  
Traffic Volume ◽  
Transport Systems ◽  
Traffic Data ◽  
Data Collection Methods ◽  
Traffic Data Collection ◽  
Collection Methods

It is a well-known fact that the data on road traffic flow characteristics is essential for sustainable road network management. First road traffic volume counts date back to the 1950s when short-term periodic road traffic counts were carried out in cities worldwide. Manual traffic counting is one of the oldest and most technologically simple methods to obtain data on road traffic volume and its composition. Today, because of the ever-growing road transport demand, it has become clear that the development of Intelligent Transport Systems (ITS) is vital to increase safety and tackle increasing emission and congestion problems. The introduction of ITS highly depends on the quality and quantity of traffic data. Under the growing requirement of long-term traffic flow information, various traffic data collection methods have evolved. They allow systematic recording of the traffic flow volume and composition but also vehicle speed, total gross weight, number of axles, axle load and travel destination. This data, which is collected continuously over longer periods, enables a detailed analysis of traffic flows, and represents the basis for decision making in planning, designing, construction and maintenance of road infrastructure. This paper gives an overview of traditional and emerging traffic data collection methods - both fixed and mobile - and the analysis of the current road traffic data collection methods used on the Croatian road network, in terms of their potential and limitations.

scholarly journals A traffic data collection and analysis method based on wireless sensor network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Huan Wang ◽  
Min Ouyang ◽  
Qingyuan Meng ◽  
Qian Kong
Keyword(s):  
Wireless Sensor Network ◽  
Data Collection ◽  
Sensor Network ◽  
Traffic Flow ◽  
Wireless Sensor ◽  
Traffic Data ◽  
Flow Data ◽  
Analysis Method ◽  
Traffic Data Collection ◽  
Data Collection And Analysis

AbstractWith the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network (WSN), which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification. The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals. Based on these steps, the trend of complexity change of mixed signal can be used to identify signal characteristics. The numerical experiments show that, based on incremental noise addition, the complexity trends of periodic data, random data, and chaotic data are different. The application of the method opens a new way for traffic flow data collection and analysis.

scholarly journals A Research of Traffic Prediction using Deep Learning Techniques

2019 ◽  
Vol 8 (9S2) ◽  
pp. 725-728
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Transport Systems ◽  
Traffic Data ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Learning Techniques ◽  
Modern Technologies

Traffic data is very important in designing a smart city. Now –a day’smany intelligent transport systems use modern technologies to predict traffic flow, to minimize accidents on road, to predict speed of a vehicle and etc. The traffic flow prediction is an appealing study field. Many techniques of data mining are employed to forecast traffic. Deep learning techniques can be used with technological progress to prevent information from real time. Deep algorithms are discussed to forecast real-world traffic data. When traffic data becomes big data, some techniques to improve the accuracy of trafficprediction are also discussed.

scholarly journals Visualization and Understanding of Traffic Flow and Congestion in India

2020 ◽  
Author(s):  
Tsutomu Tsuboi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Social Issues ◽  
Flow Characteristics ◽  
Flow Theory ◽  
Gujarat State ◽  
Traffic Condition ◽  
Traffic Flow Theory ◽  
Traffic Data Collection ◽  
Real Traffic

This study focuses on traffic condition analysis, especially in under developing country India and makes more visible of traffic flow by using traffic flow theory in order to understand real traffic condition. India is one of rapid economic growing countries and large market with second largest population 1.3 billion in 2018. On the other hand, there are social issues such as environment air pollution and global warming by traffic CO2 emission of transportation. This kind of condition is not only in India, but in other South East Asia and Africa in future. From recent more than one-month traffic observation in a typical major city Ahmedabad in Gujarat state, which has about 8 million population and industrialized city. In terms of traffic data collection, 14 CCTV cameras are used in the city. Based on multiple traffic cameras monitoring, author found the unique traffic flow characteristics and compares traffic flow theory. In this study, it is described what is key parameters to show real traffic congestion condition and how these congestion occurs.

Effect of Field Setup on Accuracy of Traffic Data Collection Using Machine Vision Technology

2002 ◽  
Vol 1804 (1) ◽  
pp. 62-66 ◽  
Author(s):  
Sunita Nadella ◽  
Lloyd A. Herman
Keyword(s):  
Machine Vision ◽  
Focal Length ◽  
Reference Points ◽  
Specific Reference ◽  
Traffic Data ◽  
Video Traffic ◽  
Vertical Angle ◽  
Traffic Data Collection ◽  
Camera Position ◽  
Parameter Ranges

Video traffic data were collected in 24 combinations of four different camera position parameters. A machine vision processor was used to detect vehicle speeds and volumes from the videotapes. The machine vision results were then compared with the actual vehicle volumes and speeds to give the percentage errors in each case. The results of the study provide a procedure with which to establish camera position parameters with specific reference points to help machine vision users select suitable camera positions and develop appropriate measurement error expectations. The camera position parameters that were most likely to produce the least overall volume and speed errors, for the specific site and field setup with the parameter ranges used in this study, were the low height of approximately 7.6 m (25 ft), with an upstream orientation (traffic moving toward the camera), a 50-mm (midangle) focal length, and a 15° vertical angle.

Field Theory for Some Traffic Phenomena and Fundamental Diagram

2021 ◽  
pp. 036119812110064
Author(s):  
Daiheng Ni
Keyword(s):  
Field Theory ◽  
Traffic Flow ◽  
Causal Effect ◽  
Microscopic Approach ◽  
Fundamental Diagram ◽  
Longitudinal Control ◽  
Effect Analysis ◽  
Vehicle Trajectories ◽  
Vehicle Motion ◽  
Cross Reference

A fundamental diagram consists of a scatter of traffic flow data sampled at a specific location and aggregated from vehicle trajectories. These trajectories, if presented equivalently, constitute a microscopic version of the (conventional) fundamental diagram. The cross-reference between vehicle trajectories and the microscopic fundamental diagram provides details of vehicle motion dynamics which allow causal-effect analysis on some traffic phenomena and further reveal the microscopic basis of the conventional fundamental diagram. This observation inspires theoretical modeling by a microscopic approach to address traffic phenomena and the conventional fundamental diagram. Derived from the field theory of traffic flow, the longitudinal control model is capable of serving the purpose without the modifications or exceptions used by other approaches.

scholarly journals Formation of the Traffic Flow Rate under the Influence of Traffic Flow Concentration in Time at Controlled Intersections in Tyumen, Russian Federation

2021 ◽  
Vol 13 (15) ◽  
pp. 8324
Author(s):  
Viacheslav Morozov ◽  
Sergei Iarkov
Keyword(s):  
Traffic Flow ◽  
Flow Rate ◽  
Traffic Congestion ◽  
Traffic Management ◽  
System Analysis ◽  
Motor Vehicle ◽  
Experimental Studies ◽  
Transport Systems ◽  
Experiment Planning ◽  
Traffic Light

Present experience shows that it is impossible to solve the problem of traffic congestion without intelligent transport systems. Traffic management in many cities uses the data of detectors installed at controlled intersections. Further, to assess the traffic situation, the data on the traffic flow rate and its concentration are compared. Latest scientific studies propose a transition from spatial to temporal concentration. Therefore, the purpose of this work is to establish the regularities of the influence of traffic flow concentration in time on traffic flow rate at controlled city intersections. The methodological basis of this study was a systemic approach. Theoretical and experimental studies were based on the existing provisions of system analysis, traffic flow theory, experiment planning, impulses, probabilities, and mathematical statistics. Experimental data were obtained and processed using modern equipment and software: Traficam video detectors, SPECTR traffic light controller, Traficam Data Tool, SPECTR 2.0, AutoCad 2017, and STATISTICA 10. In the course of this study, the authors analyzed the dynamics of changes in the level of motorization, the structure of the motor vehicle fleet, and the dynamics of changes in the number of controlled intersections. As a result of theoretical studies, a hypothesis was put forward that the investigated process is described by a two-factor quadratic multiplicative model. Experimental studies determined the parameters of the developed model depending on the directions of traffic flow, and confirmed its adequacy according to Fisher’s criterion with a probability of at least 0.9. The results obtained can be used to control traffic flows at controlled city intersections.

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