Which algorithm or method used for modeling the behavior of vehicles at an intersection

Question

My first post here – hoping you can help me, I want to design the intersection Traffic light simulation , I have data about number of vehicles every traffic light phase, and I already counting the length of the cycle lights on, BUT I didn't know which algorithm can be used for modelling the traffic behavior I am looking for an algorithm for such cases :

A. Number of vehicles data : (example)

• North Path : 40 - 55 vehicles
• South Path : 70 - 85 vehicles
• East Path : 50 - 65 vehicles
• West Path : 50 - 70 vehicles

I want to modelling the number of vehicles that appear in simulation on every path based on value of the random interval value

B. Direction of movement : (example)

• North to South : 16 - 20 vehicles
• North to West : 10 - 15 vehicles
• North to East : 14 - 20 vehicles
• and so on

Behaviors of every direction of vehicle movement use AI algorithm based on value of the random interval value

C. Behaviors :

• The vehicle begins to slow down before stop if there is a vehicle in front of it or the red light is on
• the vehicle will move to smaller queues than the queue in front of it
• Have space between vehicles
• using acceleration to the initial movement, and speed will be constant after that

Answer 1

If you're not sure where to start, you're probably going to need to start with some slightly simpler problems that you can combine together to get you to a final solution.

Perhaps the best place to start would be by trying to predict how many cars arrive at a light in one cycle? In such a case, you're going to need to know something about probability distributions and for low numbers that need to greater than or equal to 0, poisson distributions specifically.

If you're trying to predict which choice may be made by the driver of a car sitting at any given set of lights, based on measurements, the easiest approach may be to generate a discrete probability distribution (if drivers choices are uncorrelated) or Markov Chains (if they are correlated).

If you're trying to work out how single lights change colour (Green -> Amber, Amber -> Red, Red -> Green), you could model that as a State machine.

If you're trying to manage multiple sets of lights, particularly when those transitions can be driven by asynchronous events, your best bet is something like a discrete event simulation. If you're looking for a more formal description of the system as a whole you may need to move to Petri nets

If you care about the positions of the individual cars you may be able to model their positions (and transitions between positions) on a graph, where vertices are associated with a location. If you need to incorporate detailed information about geometry, dynamics, kinematics you are going to need detailed models of the cars and their interactions with the road surface (and potentially each other). This would be important if you wanted to model crashes within an intersections.