scholarly journals Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique

2021 ◽  
Vol 11 (12) ◽  
pp. 5619
Author(s):  
Chieh-Min Liu ◽  
Jyh-Ching Juang
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Moving Objects ◽  
Traffic Flows ◽  
Camera System ◽  
Control Center ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Learning Technique ◽  
And Control

This paper proposes a neural network that fuses the data received from a camera system on a gantry to detect moving objects and calculate the relative position and velocity of the vehicles traveling on a freeway. This information is used to estimate the traffic flow. To estimate the traffic flows at both microscopic and macroscopic levels, this paper used YOLO v4 and DeepSORT for vehicle detection and tracking. The number of vehicles passing on the freeway was then calculated by drawing virtual lines and hot zones. The velocity of each vehicle was also recorded. The information can be passed to the traffic control center in order to monitor and control the traffic flows on freeways and analyze freeway conditions.

scholarly journals Macroscopic Traffic Flow Control Via State Estimation

2015 ◽  
Vol 15 (5) ◽  
pp. 5-16
Author(s):  
H. Abouaïssa ◽  
H. Majid
Keyword(s):  
Flow Control ◽  
State Estimation ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Density ◽  
Traffic State ◽  
Loop Detectors ◽  
Traffic State Estimation ◽  
Estimation And Control ◽  
And Control

Abstract The studies presented in this paper deal with traffic control in case of missing data and/or when the loop detectors are faulty. We show that the traffic state estimation plays an important role in traffic prediction and control. Two approaches are presented for the estimation of the main traffic variables (traffic density and mean speed). The state constructors obtained are then used for traffic flow control. Several numerical simulations show very promising results for both traffic state estimation and control.

The Traffic Prediction and Control Based on Rough Set Theory

2013 ◽  
Vol 756-759 ◽  
pp. 632-635 ◽  
Author(s):  
Ai Long Fan
Keyword(s):  
Traffic Flow ◽  
Rough Set ◽  
Traffic Control ◽  
Rough Set Theory ◽  
Fuzzy Theory ◽  
Research Priorities ◽  
Traffic Prediction ◽  
Prediction And Control ◽  
Traffic Guidance ◽  
And Control

Intelligent traffic control and traffic guidance systems have become the core question of ITS research, but real-time and accurate traffic prediction and control are two keys that they may achieve. On this basis, Research priorities are proposed and it introduces general ideas of short-term traffic flow forecasting and control, and uses the rough set and fuzzy theory to predict and control traffic flow. Compared with the actual, forecasting results' error is smaller, and the same time the jam of intersection is effectively alleviated.

scholarly journals Designs and Algorithms to Map Eye Tracking Data with Dynamic Multielement Moving Objects

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Ziho Kang ◽  
Saptarshi Mandal ◽  
Jerry Crutchfield ◽  
Angel Millan ◽  
Sarah N. McClung
Keyword(s):  
Eye Tracking ◽  
Traffic Control ◽  
Visual Angle ◽  
Human Performance ◽  
Moving Objects ◽  
Air Traffic ◽  
Tracking Data ◽  
Angle Error ◽  
Design Concepts ◽  
And Control

Design concepts and algorithms were developed to address the eye tracking analysis issues that arise when (1) participants interrogate dynamic multielement objects that can overlap on the display and (2) visual angle error of the eye trackers is incapable of providing exact eye fixation coordinates. These issues were addressed by (1) developing dynamic areas of interests (AOIs) in the form of either convex or rectangular shapes to represent the moving and shape-changing multielement objects, (2) introducing the concept of AOI gap tolerance (AGT) that controls the size of the AOIs to address the overlapping and visual angle error issues, and (3) finding a near optimal AGT value. The approach was tested in the context of air traffic control (ATC) operations where air traffic controller specialists (ATCSs) interrogated multiple moving aircraft on a radar display to detect and control the aircraft for the purpose of maintaining safe and expeditious air transportation. In addition, we show how eye tracking analysis results can differ based on how we define dynamic AOIs to determine eye fixations on moving objects. The results serve as a framework to more accurately analyze eye tracking data and to better support the analysis of human performance.

scholarly journals Autonomous Detection for Traffic Flow Parameters of a Vehicle-Mounted Sensing Device Based on Symmetrical Difference

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 72 ◽  
Author(s):  
Jihai Huang ◽  
Jiansen Ye
Keyword(s):  
Data Acquisition ◽  
Traffic Flow ◽  
Moving Objects ◽  
Detection Method ◽  
Transmission Protocol ◽  
Flow Parameters ◽  
Upper Computer ◽  
And Control ◽  
Symmetrical Difference ◽  
Detection Effect

Based on the research and analysis of traffic detectors, it is found that the existing vehicle detection equipment is generally vulnerable to environmental interference and the detection effect cannot meet the current traffic demand. Therefore, an automatic detection method of traffic flow parameters based on symmetrical difference was put forward. This method collects the traffic flow parameters through wireless sensor nodes. Since the safety transmission protocol of VANET (Vehicular Ad Hoc Networks) can maximize the safety channel capacity, it transmits the traffic flow parameters to the data acquisition and control equipment of the upper computer through the cross layer pre-balanced safety transmission protocol of VANET in the wireless communication unit. The data acquisition and control equipment of the upper computer uses the traffic flow detection method based on the symmetrical difference to obtain the details of the moving objects in the traffic flow so as to realize the independent detection of the traffic flow parameters of the vehicle-borne sensor equipment. Experimental results show that the designed method has anti-interference abilities for the noise and light changes. Meanwhile, this method can completely extract moving objects from the traffic flow and can improve the detection effect of the moving objects in the traffic flow. Thus, the effectiveness of the proposed method can be fully verified.

scholarly journals DETERMINATION OF TRAFFIC FLOWS PARAMETERS ON THE MANAGEMENT NETWORK

2020 ◽  
Author(s):  
Nataliia Semchenko ◽  
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Management ◽  
Traffic Flows ◽  
The Road ◽  
Specific Intensity ◽  
Traffic Flow Management ◽  
On The Road ◽  
Basic Characteristics

The work is devoted to the actual problem of determining the parameters of dense traffic flows on the road cities network, which can be used when introducing automated traffic control systems. The subject of the study is to determine the parameters of traffic flows in the central part of the city. The purpose of the work is to develop methods for determining the parameters of traffic flows of the street and road network on the basis of empirical and analytical modeling to reduce the number of peripheral measuring devices in the automated traffic control system. Methodology. In the given thesis there was solved the applied scientific problem of short-term operational forecasting of the traffic flow intensity on the transport network using the empirical-analytical approach, in which the measurement of traffic flow parameters at the entrances to the area of traffic flow management is carried out by transport detectors, internal local objects are determined by modeling. The proposed model is based on the determination of intensities at approaches to stop lines of internal crossroads of the management area using recurrent sequences. Experimental researches of traffic flows on the network and on the crossings were carried out using video filming during periods of maximum load. A comparative analysis of the simulation results with the experimental data showed that the relative error on a network with an area of 50-60 hectares does not exceed 3%, which indicates the adequacy of the model and the possibility of using it for management tasks. Practical implications. Implementation of the empirical-analytical method in automated traffic management systems will make it possible to reduce the number of detectors by 43-46% depending on the area of traffic management and obtain a sufficient economic effect. The regularities of the movement of dense traffic flows of high specific intensity on short hauls, typical for the central parts of cities, have been investigated. Value/originality. According to experimental results there were obtained approximating models of parameters of the logarithmic normal probabilistic law of time intervals distribution in dense traffic flows, the specific intensity of which exceeds 600 vph; the changes in basic characteristics of the vehicles group in the traffic flow when driving through the road crossing taking into account its intensity and the distance from the group forming object are determined.

Artificial Intelligence for Evaluating the Mental Workload of Air Traffic Controllers

2020 ◽  
pp. 184-212
Author(s):  
Tetiana Shmelova ◽  
Yuliya Sikirda
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Expert System ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Mental Workload ◽  
Air Traffic ◽  
Control Center ◽  
Air Traffic Controllers ◽  
And Control

In this chapter, the authors propose the application of artificial intelligence (namely expert system and neural network) for estimating the mental workload of air traffic controllers while working at different control centers (sectors): terminal control center, approach control center, area control center. At each air traffic control center, air traffic controllers will perform the following procedures: coordination between units, aircraft transit, climbing, and descending. So with the help of the artificial intelligence (AI) and its branches expert system and neural network, it is possible to estimate the mental workload of dispatchers for a different number of aircraft, compare the workload intensity of the air traffic control sectors, and optimize the workload between sectors and control centers. The differentiating factor of an AI system from a standard software system is the characteristic ability to learn, improve, and predict. Real dispatchers, students, graduate students, and teachers of the National Aviation University took part in these researches.

scholarly journals Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos

2019 ◽  
Vol 11 (18) ◽  
pp. 2155 ◽  
Author(s):  
Jie Wang ◽  
Sandra Simeonova ◽  
Mozhdeh Shahbazi
Keyword(s):  
Object Tracking ◽  
Traffic Flow ◽  
State Of The Art ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Fine Tuning ◽  
Processing Technologies ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Tracking By Detection

Along with the advancement of light-weight sensing and processing technologies, unmanned aerial vehicles (UAVs) have recently become popular platforms for intelligent traffic monitoring and control. UAV-mounted cameras can capture traffic-flow videos from various perspectives providing a comprehensive insight into road conditions. To analyze the traffic flow from remotely captured videos, a reliable and accurate vehicle detection-and-tracking approach is required. In this paper, we propose a deep-learning framework for vehicle detection and tracking from UAV videos for monitoring traffic flow in complex road structures. This approach is designed to be invariant to significant orientation and scale variations in the videos. The detection procedure is performed by fine-tuning a state-of-the-art object detector, You Only Look Once (YOLOv3), using several custom-labeled traffic datasets. Vehicle tracking is conducted following a tracking-by-detection paradigm, where deep appearance features are used for vehicle re-identification, and Kalman filtering is used for motion estimation. The proposed methodology is tested on a variety of real videos collected by UAVs under various conditions, e.g., in late afternoons with long vehicle shadows, in dawn with vehicles lights being on, over roundabouts and interchange roads where vehicle directions change considerably, and from various viewpoints where vehicles’ appearance undergo substantial perspective distortions. The proposed tracking-by-detection approach performs efficiently at 11 frames per second on color videos of 2720p resolution. Experiments demonstrated that high detection accuracy could be achieved with an average F1-score of 92.1%. Besides, the tracking technique performs accurately, with an average multiple-object tracking accuracy (MOTA) of 81.3%. The proposed approach also addressed the shortcomings of the state-of-the-art in multi-object tracking regarding frequent identity switching, resulting in a total of only one identity switch over every 305 tracked vehicles.

The Study of Traffic Rules Based on Cellular Automata Traffic Wave Model

2014 ◽  
Vol 721 ◽  
pp. 47-51
Author(s):  
Wen Bin Liu ◽  
Yue Qiang Yang
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Traffic Control ◽  
Wave Model ◽  
Traffic Flows ◽  
Vehicle Performance ◽  
Vehicle Density ◽  
Vehicle Velocity ◽  
Traffic Wave ◽  
Traffic Rules

The traffic control problem is a hot issue in recent years. We investigate the effectiveness of three traffic rules in assuring safety and improve traffic flow. We utilize a cellular automata method of traffic flow to investigate and simulate the vehicle performance and use a linear weighting approach to weigh safety and traffic flow comprehensively. We establish overtaking models of a two-lane freeway on the basis of the stochastic traffic cellular automaton (STCA) model. Using the cellular automata method, we simulate the relationships between the traffic flows, the average vehicle velocity, and the vehicle density. We propose two new traffic rules, which in the premise of ensuring safety and improve traffic flow.

scholarly journals ANALISIS DISTRIBUSI PROBABILITAS DAN KENDALI OPTIMAL PERSIMPANGAN = PROBABILITY DISTRIBUTION ANALYSIS AND OPTIMAL CONTROL INTERSECTION

2015 ◽  
Vol 9 (1) ◽  
pp. 21-26
Author(s):  
Mulyadi Sinung Harjono ◽  
Wimpie A.N Aspar ◽  
Abdul Halim ◽  
Kalamullah Ramli
Keyword(s):  
Optimal Control ◽  
Control System ◽  
Probability Distribution ◽  
Traffic Flow ◽  
Traffic Control ◽  
Road Network ◽  
Traffic Flows ◽  
Hybrid Petri Nets ◽  
Traffic Conditions ◽  
Traffic Control System

Abstract Research traffic dynamics modeling requires the enumeration of traffic flow data on many road network nodes. Information enumeration traffic flows are applied to the analysis model of the road network control or control the intersection area, either as a standalone junction (isolated) and the coordinative intersection. Classification of types and traffic conditions used for this control is determined by ITS transportation management policy or government. Estimation and prediction of traffic conditions in real terms are based on information obtained by traffic counting. Counting of traffic flows is aimed to determine the probability distribution function (pdf) traffic flow for the intersection of two segments, namely Jalan Kyai Haji Wahid Hasyim - Jalan Mohammad Husni Thamrin and Jalan Kebun Sirih - Jalan Mohammad Husni Thamrin, Jakarta, Indonesia. Signalized intersection is composed of roads with 12 lanes and 4 lanes with traffic signs and fixtures actuated traffic control system using historical data. Based on the evolution of the combined token, it was obtained fundamental equation for the evolution of the token. Based on the modeling, the needs of departure vehicle for red light violators and breakthrough yellow light, it would require further development to SimHPN. Modeling and simulation of hybrid Petri nets on this research are aimed to perform optimal control system for traffic flow, the number of vehicles at intersections, in order to obtain optimal current flow in the study area. Abstrak Penelitian pemodelan dinamika lalu-lintas memerlukan data pencacahan arus lalu-lintas (traffic counting) pada banyak simpul jaringan jalan. Informasi pencacahan arus lalu-lintas tersebut dipergunakan untuk analisa model pengendalian jaringan jalan ataupun pengendalian area persimpangan, baik sebagai persimpangan mandiri (isolated) maupun persimpangan koordinatif. Penggolongan jenis dan kondisi lalu-lintas yang dipergunakan untuk pengendalian ini ditentukan oleh kebijakan manajemen transportasi ITS ataupun pemerintah. Estimasi dan prediksi kondisi lalu-lintas secara riil diperoleh berdasarkan informasi hasil pencacahan arus lalu-lintas. Pencacahan arus lalulintas bertujuan untuk menentukan fungsi distribusi probabilitas (pdf) arus lalulintas untuk dua ruas persimpangan, yaitu Jalan Kyai Haji Wahid Hasyim - Jalan Mohammad Husni Thamrin dan Jalan Kebon Sirih - Jalan Mohammad Husni Thamrin, Jakarta Indonesia. Persimpangan bersinyal ini tersusun atas ruas jalan dengan 12 lajur dan 4 lajur dengan rambu lalu-lintas dan perlengkapan actuated traffic control system menggunakan data historical. Berdasarkan evolusi token gabungan diperoleh persamaan fundamental untuk evolusi token. Berdasarkan kebutuhan pemodelan keberangkatan kendaraan untuk pelanggar lampu merah dan penerobos lampu kuning, maka diperlukan pengembangan lebih lanjut terhadap SimHPN. Pemodelan dan simulasi dengan hybrid Petri nets pada penelitian ditujukan untuk melakukan sistem kendali optimal terhadap arus lalulintas, jumlah kendaraan di persimpangan, sehingga diperoleh aliran arus optimal pada area penelitian.

scholarly journals OAK-TREE: One-of-a-Kind Traffic Research and Education Experiment

1997 ◽  
Vol 1603 (1) ◽  
pp. 106-111 ◽  
Author(s):  
Carlos Sun ◽  
Wilfred Recker ◽  
Stephen Ritchie ◽  
Brian Gallagher ◽  
Eric Shen ◽  
...  
Keyword(s):  
Traffic Control ◽  
Professional Training ◽  
Academic Community ◽  
University Of California ◽  
Control Center ◽  
Oak Tree ◽  
Research And Education ◽  
The University ◽  
And Control ◽  
The City

The creation and progress of OAK-TREE (One-of-a-Kind Traffic Research and Education Experiment) are chronicled. OAK-TREE is a traffic educational laboratory experiment that was developed and conducted at the University of California at Irvine (UCI) during the spring quarter of 1996. This project involved a cooperative effort between the academic community and public-sector transportation operating agencies in developing a comprehensive field and laboratory educational experience for undergraduate students in transportation engineering. The agencies involved in this effort were the Department of Civil and Environmental Engineering at the University of California at Irvine, the Advanced Traffic Surveillance and Control Center of the city of Los Angeles, the Transportation Management Center of the city of Anaheim, and the Irvine Traffic Research and Control Center of the city of Irvine. These agencies were instrumental in creating an innovative laboratory experience for academic training in the use of state-of-the-practice resources and methods for traffic engineering. The results were the development of a state-of-the-art traffic-control educational laboratory at UCI and the genesis of a unique traffic-control course that fulfilled the requirements of both fundamental academic education and rigorous professional training.

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