Traffic State Classification in Condition-Responsive Traffic Control

2007 ◽  
Author(s):  
Geza Pesti ◽  
Montasir M. Abbas ◽  
Nadeem Chaudhary
Keyword(s):  
Traffic Control ◽  
Traffic State ◽  
State Classification

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.

scholarly journals Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5213 ◽  
Author(s):  
Donato Impedovo ◽  
Fabrizio Balducci ◽  
Vincenzo Dentamaro ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Machine Learning Techniques ◽  
Visual Features ◽  
Learning Approaches ◽  
Traffic State ◽  
State Classification ◽  
Learning Techniques

Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

Traffic state classification and prediction based on trajectory data

2021 ◽  
pp. 1-15
Author(s):  
Yu Yuan ◽  
Wenbo Zhang ◽  
Xun Yang ◽  
Yang Liu ◽  
Zhiyuan Liu ◽  
...  
Keyword(s):  
Trajectory Data ◽  
Traffic State ◽  
State Classification

scholarly journals Using Automated Planning for Traffic Signals Control

2016 ◽  
Vol 28 (4) ◽  
pp. 383-391 ◽  
Author(s):  
Matija Gulić ◽  
Ricardo Olivares ◽  
Daniel Borrajo
Keyword(s):  
Control Systems ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Automated Planning ◽  
Traffic Signals ◽  
Real World Data ◽  
Planning Techniques ◽  
Traffic Lights ◽  
Traffic State ◽  
Control Actions

Solving traffic congestions represents a high priority issue in many big cities. Traditional traffic control systems are mainly based on pre-programmed, reactive and local techniques. This paper presents an autonomic system that uses automated planning techniques instead. These techniques are easily configurable and modified, and can reason about the future implications of actions that change the default traffic lights behaviour. The proposed implemented system includes some autonomic properties, since it monitors the current traffic state, detects if the system is degrading its performance, sets up new sets of goals to be achieved by the planner, triggers the planner that generates plans with control actions, and executes the selected courses of actions. The obtained results in several artificial and real world data-based simulation scenarios show that the proposed system can efficiently solve traffic congestion.

scholarly journals Workload State Classification With Automation During Simulated Air Traffic Control

2007 ◽  
Vol 17 (4) ◽  
pp. 371-390 ◽  
Author(s):  
David B. Kaber ◽  
Carlene M. Perry ◽  
Noa Segall ◽  
Mohamed A. Sheik-Nainar
Keyword(s):  
Traffic Control ◽  
Air Traffic Control ◽  
Air Traffic ◽  
State Classification

scholarly journals Traffic State Recognition of Intersection Based on Image Model and PCA Hashing

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Li-li Zhang ◽  
Li Wang ◽  
Qi Zhao
Keyword(s):  
High Precision ◽  
Traffic Control ◽  
Road Traffic ◽  
Image Feature ◽  
Statistical Characteristics ◽  
Image Model ◽  
State Recognition ◽  
Traffic State ◽  
Detection Technology ◽  
Scale Detection

The premise of implementing an effective traffic control strategy is the accurate traffic state recognition. In the existing study, traffic state recognition methods were processed by using statistical characteristics and long-term scale detection of field traffic data. Hence, the dynamic characteristics and subtle changes in traffic flow were easy to overlook. At present, more and more advanced traffic detection technology provides reliable and accurate data for measuring and distinguishing the state of urban road traffic, such as the cooperative vehicle-infrastructure system, wide-area radar technology, and 5G technology. This study proposes a novel method called HTSI (High Precision Traffic State Identification Method), which is based on the advanced detection technology in traffic state recognition at the intersection: The raw data used for intersection traffic state recognition is high-precision detection data of tracking characteristics, which make the data look like a picture of the intersection at God’s perspective. To this end, we construct an image model for intersections and implement image feature extraction in a way that is different from traditional image processing. Then, the traffic state recognition problem at the intersection is translated into an image searching problem with tags. The image searching is realized by the hashing algorithm. Finally, the comprehensive experiments prove that the proposed method is more accurate and finer than other methods.

scholarly journals Quantitative Analysis of Urban Regional Traffic Status

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Qing-fang Yang ◽  
Ru-ru Xing ◽  
Li-li Zheng ◽  
Shu-xing Wang
Keyword(s):  
Traffic Flow ◽  
Network Traffic ◽  
Road Network ◽  
Urban Region ◽  
Operation Condition ◽  
Traffic Condition ◽  
Traffic State ◽  
Time Operation ◽  
State Classification ◽  
Real Time Operation

In order to monitor the real-time operation condition of urban region traffic flow, and to quickly identify regional traffic status, this paper adopts CNM (Clauset-Newman-Moore) Community Division Method of Complex Network to analyze traffic status information deeply implied from the regional road network traffic flow data, which aims to objectively develop the reasonable classification of regional traffic state with no classification criteria of determining regional traffic state. Combined with the regional road network traffic data from a certain city, the example analysis shows that this proposed method can easily provide the reasonable division of regional traffic condition and verifies the feasibility of the regional traffic state classification method. Besides, the example analysis gives the rough regional traffic status determination standard, laying theoretical basis for accurately judging the regional traffic state.

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