Adaptive Traffic Signal Control System

1. Intro and Problem Definition

Traffic congestion is becoming a serious problem with a large number of cars on the roads. Vehicles queue length waiting to be processed at the intersection is rising sharply with the increase of the traffic flow, and the traditional traffic lights cannot efficiently schedule it.

2. Solution

The answer is to build a self adaptive traffic light control system. Disproportionate and diverse traffic in different lanes leads to inefficient utilization of same time slot for each of them characterized by slower speeds, longer trip times, and increased vehicular queuing.To create a system which enable the traffic management system to take time allocation decisions for a particular lane according to the traffic density on other different lanes with the help of cameras, image processing modules.

3. The Tech Side

Lets get our hands dirty with understanding why and how we can resolve this issue. To implement such a system, we need two main parts: eyes to watch the real-time road condition and a brain to process it. A traffic signal system at its core has two major tasks: move as many users through the intersection as possible doing this with as little conflict between these users as possible.

Yolo Neural Layers
  1. Scan and determine traffic density.
  2. Input this data to the Time Allocation module.
  3. The output will be the time slots for each lane, accordingly.
Flow of work
  1. The system determines further the number of cars in the lane and hence computes its relative density with respect to other lanes.
  2. Time allotment module takes input (as traffic density) from this system and determines an optimized and efficient time slot.
  3. This value is then triggered by the microprocessor to the respective Traffic Lights.

4. Result

As a result, we get

5. Conclusion and Extensibility

and hence we conclude that the goal of this work is to improve intelligent transport systems by developing a Self-adaptive algorithm to control road traffic based on deep Learning. This new system facilitates the movement of cars in intersections, resulting in reducing congestion, less CO2 emissions, etc. The richness that video data provides highlights the importance of advancing the state-of-the-art in object detection, classification and tracking for real-time applications. YOLO provides extremely fast inference speed with slight compromise in accuracy, especially at lower resolutions and with smaller objects. While real-time inference is possible, applications that utilize edge devices still require improvements in either the architecture’s design or edge device’s hardware. Finally, we have proposed a new algorithm taking this real-time data from YOLO and optimizing phases in order to reduce vehicle waiting time.

Dev| Founder of DevScript & NeoVantium| Intern@Tesselate Imaging| Beta Microsoft Student Learn Ambassador| DSC RCOEM Core