Adaptive Traffic Control System

Project Title

Adaptive Traffic Control System

Project Area of Specialization Internet of ThingsProject Summary

It is an intelligent traffic signal control system based on OpenCV processing technology, which provides appropriate time to adjust the traffic flow in each direction of the intersection, so that the lane with high vehicle density has a longer opening time than other lanes, and maintains the total intersection time ratio, so that the lane with low density also has a free path without waiting for more time. First of all, accurate vehicle quantity data should be obtained to reasonably allocate the time on
each road. The system includes: digital camera, OpenCV software, image processing unit, wireless transceiver, microcontroller, etc.

This project is focused on solving a public problem of traffic congestion at signal points. Already deployed systems use fixed waiting time for traffic which might cause huge congestion at certain lanes. Our adaptive traffic management system tends to solve this problem by detecting number of vehicles on each lane and then setting signal times accordingly. Image processing and machine learning algorithms are being used to develop system that would provide real time data and also predict upcoming traffic for future times by looking at the previous datasets.  First of all, accurate vehicle quantity data should be obtained to reasonably allocate the time on each road. The system includes: digital camera, OpenCV software, image processing unit, wireless transceiver, microcontroller, etc. In an emergency, the system will consider the best. GSM and GPS transmitters are placed on the ambulance to send out emergency warning messages, and GSM and GPS signal receiver engines are installed at every intersection.

Project Objectives

• Cars crossing the signal can be tracked at the real time.
• Number of vehicles detected can provide good traffic controlling solutions.
• Average waiting time for vehicles will be reduced.
• Congested lanes would be provided more time to clear traffic.
• Reduced traffic eventually leads to better climate conditions.
• Managed traffic would make smooth passing for vehicles eventually leading to low accident rates.
• GSM and GNSS modules are used to track emergency vehicles such as ambulances, fire engines, police vehicles and VIP vehicles.

Project Implementation Method

Adaptive Traffic Control System is an intelligent traffic signal control system based on OpenCV processing technology, which provides appropriate time to adjust the traffic flow in each direction of the intersection, so that the lane with high vehicle density has a longer opening time than other lanes, and maintains the total
intersection time ratio, so that the lane with low density also has a free path without waiting for more time. First of all, accurate vehicle quantity data should be obtained to reasonably allocate the time on each road. The system includes: digital camera, OpenCV software, image processing unit, wireless transceiver, microcontroller, etc. OpenCV system as image processing software; Python is the language used; Rasberry PI processes images and uses 8051 microcontroller to convert digital
language into binary language; Color threshold and blob detection technology are adopted; Kalman filter algorithm is used.

The system can detect fire trucks, ambulances, emergency vehicles, etc. and take the required actions, at the same time, We use Python language programming to implement the operation. After the camera captures the image, the Raspberry Pi is used to process the image, and the time is allocated to each traffic light according to
the traffic density. In an emergency, the system will consider the best. GSM and GPS transmitters are placed on the ambulance to send out emergency warning messages, and GSM and GPS signal receiver engines are installed at every intersection. Each intersection is assigned a unique code, and all codes for each route have a unique identifier. After identifying the vehicle, it will continue to track it until the vehicle leaves the frame and count it. Therefore, the center of the detected vehicle must be found and connected to the vehicle in a rectangle. When we get the center of the detected vehicle, we will use the Kalman filter algorithm to track the vehicle, obtain the accurate number of vehicles in each lane, and calculate the time spent on different routes at the intersection. This database saves the traffic information and replaces the route time allocated to each intersection on the web server.

This is a real-time signal management system with easy construction, strong operability and low cost. Volume prediction is a key component. Connecting the two factors with prediction can reflect the accuracy and resolution.

The system uses a single digital camera that evaluates the road density at intersections.This system gives emergency vehicles the highest priority and continuously monitors them after obtaining data from the emergency vehicles, in order to further handle emergency situations, the system provides better performance than other systems.

Benefits of the Project

• Reducing travel time and stop frequency,
• Reducing the number of rear-end collisions,
• Increasing customer satisfaction,
• Improving Air Quality by Reducing Emissions
• Reducing the costs of congestion (like fuel and lost time), and reducing vehicle emissions.
• They move traffic along faster and with fewer stops.
• Adaptive traffic signals increase safety by reducing stops (and thus the opportunity for rear-end collisions).
• They tame the chaos that often occurs in unforeseen circumstances (like traffic accidents or special events).
• They increase customer satisfaction and reduce complaints.
• Adaptive signal systems save drivers money and reduce vehicle emissions.
• This system gives emergency vehicles the highest priority and continuously monitors them after obtaining data from the emergency vehicles, in order to further handle emergency situations, the system provides better performance than other systems.
• Adaptive signal control technologies are also kinder to the environment. Using ASCT can reduce emissions of hydrocarbons and carbon monoxide due to improved traffic flow.

Technical Details of Final Deliverable

Hardware elements of a control system:

Components and subsystems include: detectors, local controllers, changeable message signs, CCTV, operator displays, central computers and field masters.

Software : 

Software used in traffic control systems. This includes real-time control software, optimization software and simulation software.

Real-Time Control software developed for local controllers allows the controller to function as a signal switching unit by:

• Receiving detector inputs,
• Processing status data,
• Computing timing, and
• Driving signal lamp load switches.

Manufacturers of standard NEMA controller units provide such software (or firmware) as a part of the device. By contrast, both manufacturers and users have developed software for the Model 170, Model 2070 and advanced transportation controllers.

Many conventional traffic systems feature the UTCS First Generation (1-GC) signature matching algorithm for real-time traffic-responsive control. Unlike earlier UTCS, these contemporary systems usually store signal timing plans at the intersection and select a plan based on detector data patterns. An alternative strategy selects the cycle, split and offset individually based on detector data for each of these parameters. Conventional systems often feature the ability to update timing plan databases from signal timing programs with a minimum of manual operation.

Traffic adjusted systems are being installed in increasing numbers. 

Hardware in the Loop

Recent research has resulted in the development of systems that enable traffic controller equipment to be tested under simulated traffic conditions. Figure 2-2 provides an example of the implementation of this concept. A microscopic simulation program such as CORSIM is interfaced to a physical traffic controller by a controller interface device (CID). A software link in the form of a dynamic link library (d11) transfers information between the computer on which the simulation is running to the CID. A network of traffic controllers may be interfaced to the simulation in this way.

Improved Transit Priority Systems

The increased use by transit vehicles of advanced equipment such as on board processors, terminals for drivers, GPS equipment, passenger counters and door position monitors in conjunction with computer aided dispatch systems enables the development of signal priority strategies for transit vehicles.

Traffic Signal Timing

One of the primary purposes of a traffic signal system TMC is to manage the timing of traffic signals in urban networks and on arterial streets. Special software allows an operator at a workstation in the TMC to communicate directly with field equipment and modify traffic signal programs in real time.

Final Deliverable of the Project Hardware SystemCore Industry TransportationOther IndustriesCore Technology Internet of Things (IoT)Other Technologies Artificial Intelligence(AI)Sustainable Development Goals Sustainable Cities and CommunitiesRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	80000
Raspberry Pi	Equipment	1	18000	18000
Camera	Equipment	4	1750	7000
Screen Jetson Nano	Equipment	1	10000	10000
Jetson Nano	Equipment	1	35000	35000
Others	Miscellaneous	1	10000	10000