scholarly journals A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bing Liu ◽  
Yu Tang ◽  
Yuxiong Ji ◽  
Yu Shen ◽  
Yuchuan Du
Keyword(s):  
Reinforcement Learning ◽  
Control Strategies ◽  
Video Data ◽  
Ramp Metering ◽  
Traffic Information ◽  
Video Frames ◽  
Traffic Volumes ◽  
Practice Method ◽  
Vehicle Mobility ◽  
Traffic Video

Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measurements collected by point detectors, such as traffic volumes and occupancies. Comparing with point detectors, traffic cameras—which have been increasingly deployed on road networks—could cover larger areas and provide more detailed traffic information. In this work, we propose a deep reinforcement learning (DRL) method to explore the potential of traffic video data in improving the efficiency of ramp metering. Vehicle locations are extracted from the traffic video frames and are reformed as position matrices. The proposed method takes the preprocessed video data as inputs and learns the optimal control strategies directly from the high-dimensional inputs. A series of simulation experiments based on real-world traffic data are conducted to evaluate the proposed approach. The results demonstrate that, in comparison with a state-of-the-practice method, the proposed DRL method results in (1) lower travel times in the mainline, (2) shorter vehicle queues at the on-ramp, and (3) higher traffic flows downstream of the merging area. The results suggest that the proposed method is able to extract useful information from the video data for better ramp metering controls.

A Trajectory Based Method of Automatic Counting of Cyclist in Traffic Video Data

2017 ◽  
Vol 26 (04) ◽  
pp. 1750015
Author(s):  
Farideh Foroozandeh Shahraki ◽  
Ali Pour Yazdanpanah ◽  
Emma E. Regentova ◽  
Venkatesan Muthukumar
Keyword(s):  
Intelligent Transportation System ◽  
Graph Matching ◽  
Research Area ◽  
Video Data ◽  
Traffic Information ◽  
Trajectory Data ◽  
Traffic Video ◽  
Automated Tools ◽  
Safety Countermeasures ◽  
New Research

Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. One of the important factors that influence cyclists safety is their counts. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, we develop a vision-based method for gathering cyclist count data at intersections and road segments. We implement a robust cyclist detection method based on a combination of classification features. We implement a multi-object tracking method based on the Kernelized Correlation Filters (KCF) in cooperation with the bipartite graph matching algorithm to track multiple cyclists. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The proposed method is the first cyclist counting method, that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis.

scholarly journals Ramp - Metering Algorithms Evaluated within Simplified Conditions

2017 ◽  
Vol 13 (2) ◽  
pp. 112-119 ◽  
Author(s):  
Aleš Janota ◽  
Peter Holečko ◽  
Michal Gregor ◽  
Marián Hruboš
Keyword(s):  
Control Strategy ◽  
Control Strategies ◽  
Ramp Metering ◽  
Transport Infrastructure ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Control Zone ◽  
Intelligent Transport ◽  
Traffic Volumes ◽  
Freeway Networks

Abstract Freeway networks reach their limits, since it is usually impossible to increase traffic volumes by indefinitely extending transport infrastructure through adding new traffic lanes. One of the possible solutions is to use advanced intelligent transport systems, particularly ramp metering systems. The paper shows how two particular algorithms of local and traffic-responsive control (Zone, ALINEA) can be adapted to simplified conditions corresponding to Slovak freeways. Both control strategies are modelled and simulated using PTV Vissim software, including the module VisVAP. Presented results demonstrate the properties of both control strategies, which are compared mutually as well as with the initial situation in which no control strategy is applied

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

scholarly journals Detecting Toe-Off Events Utilizing a Vision-Based Method

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 329 ◽  
Author(s):  
Yunqi Tang ◽  
Zhuorong Li ◽  
Huawei Tian ◽  
Jianwei Ding ◽  
Bingxian Lin
Keyword(s):  
Wearable Sensors ◽  
Gait Pattern ◽  
Video Data ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Public Database ◽  
Video Frames ◽  
Different Types ◽  
Events Detection ◽  
Good Detection

Detecting gait events from video data accurately would be a challenging problem. However, most detection methods for gait events are currently based on wearable sensors, which need high cooperation from users and power consumption restriction. This study presents a novel algorithm for achieving accurate detection of toe-off events using a single 2D vision camera without the cooperation of participants. First, a set of novel feature, namely consecutive silhouettes difference maps (CSD-maps), is proposed to represent gait pattern. A CSD-map can encode several consecutive pedestrian silhouettes extracted from video frames into a map. And different number of consecutive pedestrian silhouettes will result in different types of CSD-maps, which can provide significant features for toe-off events detection. Convolutional neural network is then employed to reduce feature dimensions and classify toe-off events. Experiments on a public database demonstrate that the proposed method achieves good detection accuracy.

scholarly journals Expert Level Control of Ramp Metering Based on Multi-Task Deep Reinforcement Learning

2018 ◽  
Vol 19 (4) ◽  
pp. 1198-1207 ◽  
Author(s):  
Francois Belletti ◽  
Daniel Haziza ◽  
Gabriel Gomes ◽  
Alexandre M. Bayen
Keyword(s):  
Reinforcement Learning ◽  
Ramp Metering ◽  
Level Control

Bayes Performance of Batch Data Mining Based on Functional Dependencies

2019 ◽  
Vol 33 (03) ◽  
pp. 1959011
Author(s):  
Haixu Xi ◽  
Feiyue Ye ◽  
Sheng He ◽  
Yijun Liu ◽  
Hongfen Jiang
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Video Processing ◽  
Mining Area ◽  
Batch Processing ◽  
Video Data ◽  
Batch Processes ◽  
Functional Dependencies ◽  
Workflow System ◽  
Traffic Video

Batch processes and phenomena in traffic video data processing, such as traffic video image processing and intelligent transportation, are commonly used. The application of batch processing can increase the efficiency of resource conservation. However, owing to limited research on traffic video data processing conditions, batch processing activities in this area remain minimally examined. By employing database functional dependency mining, we developed in this study a workflow system. Meanwhile, the Bayesian network is a focus area of data mining. It provides an intuitive means for users to comply with causality expression approaches. Moreover, graph theory is also used in data mining area. In this study, the proposed approach depends on relational database functions to remove redundant attributes, reduce interference, and select a property order. The restoration of selective hidden naive Bayesian (SHNB) affects this property order when it is used only once. With consideration of the hidden naive Bayes (HNB) influence, rather than using one pair of HNB, it is introduced twice. We additionally designed and implemented mining dependencies from a batch traffic video processing log for data execution algorithms.

scholarly journals Classification of Action Based Video using Heterogeneous Feature Extraction and SVM

2019 ◽  
Vol 8 (11) ◽  
pp. 1887-1892
Keyword(s):  
Optical Flow ◽  
Video Sequence ◽  
Human Action ◽  
Video Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Video Frames ◽  
Integral Role ◽  
Heterogeneous Feature

Action recognition (AR) plays a fundamental role in computer vision and video analysis. We are witnessing an astronomical increase of video data on the web and it is difficult to recognize the action in video due to different view point of camera. For AR in video sequence, it depends upon appearance in frame and optical flow in frames of video. In video spatial and temporal components of video frames features play integral role for better classification of action in videos. In the proposed system, RGB frames and optical flow frames are used for AR with the help of Convolutional Neural Network (CNN) pre-trained model Alex-Net extract features from fc7 layer. Support vector machine (SVM) classifier is used for the classification of AR in videos. For classification purpose, HMDB51 dataset have been used which includes 51 Classes of human action. The dataset is divided into 51 action categories. Using SVM classifier, extracted features are used for classification and achieved best result 95.6% accuracy as compared to other techniques of the state-of- art.v

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