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An Intelligent Traffic Light algorithm using Machine Learning to aid in the flow of traffic

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  1. Introduction: The increased number of motor vehicles on our road networks is one of the major reasons of traffic congestion other than urbanization. With Lusaka showing an increase in motor vehicle registration of 60.89% in the first quarter of 2016 [1] with the increase in motor vehicle population comes an increased rate of road traffic accidents [1]

 

Hence the need arises for simulating and optimizing traffic control algorithms to solve the increasing demand of road usage in an effort to improve the flow of traffic [2] Improving the timing of the traffic signals is generally the most powerful and cost-effective way of solving the problem of traffic congestion. [3] In general traffic control signals are used to maximize safe flows and minimize delays and hold-up.

 

Optimizing timing of traffic signals is one way of reducing such road traffic accidents, this is because priority is given to the most the populated lane. One technique that has been used in traffic control signals is fuzzy logic. This control technique makes adjustments in the on and off timings of various lanes at a traffic junction depending on actual condition of traffic, unlike the traditional microcontroller based approach which keeps time slots fixed regardless of actual situation. [4]

  1. Statement of the problem: The traffic light system in Zambia operate under fixed time strategy. This strategy uses a preset cycle time to change the lights. The control (signal plan) is calculated in advance using statistical data. [1] Under fixed time operation each phase of the signal lasts for a specific duration before the next phase occurs, this pattern repeats itself regardless of the traffic, this strategy lacks human intelligence[4]. Thereby causing traffic congestion for lanes that would otherwise be given priority due to the number of vehicles.
  2. Aims of the study: The overall aim of the study is to come up with an intelligent traffic control algorithm that will use machine learning and neural network control techniques, this is because artificial neural networks have to ability to learn and mimic human reasoning.
  3. Study Objectives:
  4. Compare fixed time strategy control of traffic lights, to that of the neural network to determine the effectiveness of either approaches in traffic control.
  5. To create and simulate an algorithm using machine learning and artificial neural networks to optimize the flow of traffic at a four-way traffic light intersection.
  • To provide evidence of learning from the simulation.
  1. Research Questions:
  2. Is artificial intelligence approach more effective than fixed time strategy in traffic control systems?
  3. Can the results of comparing traditional fixed time strategy approach and neural network approach enable us to create and simulate an intelligent neural network algorithm to optimize the flow of traffic?
  • Can the traffic lights learn from traffic data?

 

  1. Significance of the study: The significance of the study is to come up with an algorithm that will use artificial intelligence (AI) techniques to aid in the reduction of traffic congestion at 4-way intersections. The benefits of employing these techniques will provide an efficient control and movement of motor vehicles, by giving priority to the most congested lanes thereby reducing traffic congestion and optimizing the flow of traffic subsequently reducing the negative effect traffic congestion has on motorist. The traffic lights will also be able to adapt to current traffic situations.
  2. Materials and Methodology:(i) Generate a date set containing traffic counts from different days of the week and different times of the day. The data will be used to compare the performance between fixed time approach and machine learning in traffic optimization, with results shown using matplotlib in python programming language. (ii) The proposed algorithm will be written and simulated using Python programming language (iii)The proposed algorithm will be expected to learn and adapt to any traffic condition and the results analyzed in Python using Matplotlib.
  3. References:

[1] Road Transport and Safety Agency, Annual accident report 2015

[2] T Naga Raju, Smart Traffic Light Control System for Emergency and Detection of Stolen     Vehicles, International Journal of Advanced Research in Science, Engineering and Technology Vol. 1, Issue 5, December 2014

[3] Shabnam Sayyed, Design of Dynamic Traffic Signal Control System,International Journal of Engineering Research &Technology Vol.3 – Issue 1 (January -2014)

 [4] Shilpa Mehta,  Intelligent System for Automated Traffic Signal Control Using Fuzzy Mamdani Model International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Volume 2, Issue 6, November – December 2013

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