Freeway Traffic Speed Estimation with Single-Loop Outputs

2000 ◽  
Vol 1727 (1) ◽  
pp. 120-126 ◽  
Author(s):  
Yinhai Wang ◽  
Nancy L. Nihan
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Traffic Density ◽  
Freeway Traffic ◽  
Single Loop ◽  
Length Estimation ◽  
Traffic Speed ◽  
G Value ◽  
Log Linear ◽  
Control And Management

Traffic speed is one of the most important indicators for traffic control and management. Unfortunately, speed cannot be measured directly from single inductance loops, the most commonly used detectors. To calculate space-mean speed, a constant, g, is often adopted to convert lane occupancy to traffic density. However, as illustrated by data from the present study, such a formula consistently underestimates speed whenever a significant number of trucks or other longer vehicles are present. This is because g is actually not a constant but, rather, a function of vehicle length. To calculate the value of g suitably, one needs to know the percentage of long vehicles or the mean vehicle length in real time. However, such information is not directly available from single-loop outputs. It is shown how the occupancy variance obtained from single-loop data can be used to estimate the percentage of long vehicles and how a log-linear regression model for mean vehicle length estimation based only on single-loop outputs can be developed. The estimated mean vehicle length is used to calculate the corresponding g-value in real-time to estimate speed more accurately. The speed estimations with corrected g-values are very close to the speeds observed by the speed trap in the present study.

scholarly journals The Dynamic Control of the Light Signalling Device in Real-Time

2019 ◽  
Vol 292 ◽  
pp. 03014
Author(s):  
Jan Mrazek ◽  
Lucia Duricova Mrazkova ◽  
Martin Hromada ◽  
Jana Reznickova
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Dynamic Control ◽  
Traffic Density ◽  
Light Signaling ◽  
Dynamic Traffic ◽  
Real Time Control ◽  
Real Time Traffic ◽  
Time Control ◽  
Control Dynamic

The article is focused on the issue of interval on a light signaling device. Light signaling devices operate on different systems by means of which they are controlled. The control problem is a very static setting that does not respond to real-time traffic. Important variables for dynamic real-time control are traffic density in a selected area along with average speed. These variables are interdependent and can be based on dynamic traffic control. Dynamic traffic control ensures smoother traffic through major turns. At the same time, the number of harmful CO2 emitted from the means of transport should be reduced to the air. When used in low operation, power consumption should be reduced.

scholarly journals Adaptive Traffic Management System using CNN (YOLO)

2021 ◽  
Vol 9 (VI) ◽  
pp. 3726-3732
Author(s):  
B. Sowmya
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Object Identification ◽  
Traffic Signal ◽  
Traffic Density ◽  
Loop Control ◽  
Control Framework

The huge number of vehicles on the roadways is making congestion a significant problem. The line longitudinal vehicle waiting to be processed at the crossroads increases quickly, and the traditionally used traffic signals are not able to program it properly. Manual traffic monitoring may be an onerous job since a number of cameras are deployed over the network in traffic management centers. The proactive decision-making of human operators, which would decrease the effect of events and recurring road congestion, might contribute to the easing of the strain of automation.The traffic control frameworks in India are now needed as it is an open-loop control framework, without any input or detection mechanism. Inductive loops and sensors employed in existing technology used to detect the number of passing vehicles. The way traffic lights are adapted is highly inefficient and costly in this existing technology. The aim was to build a traffic control framework by introducing a system for detection ,which gives an input to the existing system (closed loop control system) in order to adapt to the changing traffic density patterns and to provide the controller with a crucial indication for ongoing activities. By this technique, the improvement of the signals on street is extended and thus saves time by preventing traffic congestion. This study proposes an algorithm for real-time traffic signal control, depending on the traffic flow. In reality, the features of competitive traffic flow at the signposted road crossing are used by computer vision and by machine learning. This is done by the latest, real-time object identification, based on convolutional Neural Networks network called You Look Once (YOLO). Traffic signal phases are then improved by data acquired in order to allow more vehicles to pass safely over minimal wait times, particularly the line long and the time of waiting per vehicle.This adjustable traffic signal timer is used to calculate traffic density utilizing YOLO object identification using live pictures of cameras in intervals and adjusts the signal timers appropriately, therefore decreasing the road traffic congestion, ensuring speedier transit for persons, and reducing fuel consumption. The traffic conditions will improve enormously at a relatively modest cost. Inductive loops are a viable but costly approach. This method thereby cuts expenses and outcomes quickly.

scholarly journals Design and Development of Traffic Control System using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 3247-3252
Author(s):  
G. Kalyan
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Density ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

Traffic congestion is now a big issue. Although it seems to penetrate throughout the world, urban towns are the ones which are most effected. And it is expanding in nature that it is necessary to understand the density of roads in real time to better regulate signals and efficient management of transport. Various traffic congestions, such as limited capacity, unrestricted demand, huge Red Light waits might occur. While insufficient capacity and unlimited demand are somehow interconnected, their delay in lighting is difficult to encode and not traffic dependant. The necessity to simulate and optimise traffic controls therefore arises in order to better meet this growing demand. The traffic management of information, ramp metering, and updates in real-time has been frequently used in recent years for image processing and monitoring systems. An image processing can also be used for the traffic density estimation. This research describes the approach for the computation of real-time traffic density by image processing for using live picture feed from cameras. It focuses also on the algorithm for the transmission of traffic signals on the road according to the density of vehicles and therefore aims to reduce road congestion, which reduces the number of accidents.

scholarly journals Real-time Queue Length Estimation Applying Shockwave Theory at Urban Signalized Intersections

2021 ◽  
Author(s):  
Márton Tamás Horváth ◽  
Tamás Tettamanti
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Queue Length ◽  
Traffic Management ◽  
Urban Traffic ◽  
Length Estimation ◽  
Queue Lengths ◽  
General Goal ◽  
Store And Forward ◽  
Urban Traffic Control

Signal control is a basic need for urban traffic control; however, it is a very rough intervention in the free flow of traffic, which often results in queues in front of signal heads. The general goal is to reduce the delays caused, and to plan efficient traffic management on the network. For this, the exact knowledge of queue lengths on links is one of crucial importance. This article presents a link-based methodology for real-time queue length estimation in urban signalized road networks. The model uses a Kalman Filter-based recursive method and estimates the length of the queue in every cycle. The input of the filter, i.e. the dynamics of queue length is described by the traffic shockwave theory and the store and forward model. The method requires one loop-detector per link placed at the appropriate position, for which the article also provides suggestions.

Smart Vehicles for Traffic Management and Systems Using Cloud Computing

2018 ◽  
pp. 178-199
Author(s):  
Ashish Thomas ◽  
Gaurav Singal ◽  
Riti Kushwaha
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Traffic Management ◽  
Ad Hoc ◽  
Heavy Traffic ◽  
Lane Changes ◽  
Survey Report ◽  
Stationary Objects ◽  
Control And Management ◽  
Smart Traffic

A vehicular ad hoc network (VANET) is the network of mobile devices as well as stationary objects that can communicate with each other. This technology comprises of both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) data transmission links. At present, vehicular mobility framework has a lot of limitations, which includes lack of real-time operations, frequent disconnects due to dynamic-restricted topology, tracking vehicle that break rules, lane-changes, exceed speed-limit, etc. These characteristics call for a new type of high class of protocol. This chapter presents a survey report on “smart traffic control” system that incorporates traffic-related parameters to further augment the control and management of vehicular movements on the roadways. This can support efficient management of traffic in the city, and cops can communicate with each other, get real-time, accurate, status update of the traffic, track the vehicular movements, etc. In addition, jam control mechanism can be placed on heavy traffic days to optimize the routes. This system introduces artificial intelligence (AI) that can optimize deployed of cops and find alternate routes for the driver to reach the destination address without much fuel consumption.

scholarly journals ADAPTIVE TRAFFIC LIGHT CYCLE TIME CONTROLLER USING MICROCONTROLLERS AND CROWDSOURCE DATA OF GOOGLE APIs FOR DEVELOPING COUNTRIES

2018 ◽  
Vol IV-4/W7 ◽  
pp. 83-90 ◽  
Author(s):  
S. Mishra ◽  
D. Bhattacharya ◽  
A. Gupta ◽  
V. R. Singh
Keyword(s):  
Control System ◽  
Embedded System ◽  
Real Time ◽  
Cycle Time ◽  
Traffic Control ◽  
Open Loop ◽  
Traffic Density ◽  
Light Cycle ◽  
Traffic Light ◽  
Loop Control

<p><strong>Abstract.</strong> Controlling of traffic signals optimally helps in avoiding traffic jams as vehicle volume density changes on temporally short and spatially small scales. Nowadays, due to embedded system development with the rising standards of computational technology, condense electronics boards as well as software packages, system can be developed for controlling cycle time in real time. At present, the traffic control systems in India lack intelligence and act as an open-loop control system, with no feedback or sensing network, due to the high costs involved. This paper aims to improve the traffic control system by integrating different technologies to provide intelligent feedback to the existing network with congestion status adapting to the changing traffic density patterns. The system presented in this paper aims to sense real-time traffic congestion around the traffic light using Google API crowdsource data and hence avoids infrastructure cost of sensors. Subsequently, it manipulates the signal timing by triggering and conveying information to the timer control system. Generic information processing and communication hardware system designed in this paper has been tested and found to be functional for a pilot run in real time. Both simulation and hardware trials show the transmission of required information with an average time delay of 1.2 seconds that is comparatively very small considering cycle time.</p>

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