traffic
An abstract and minimilist traffic simulation, using OpenStreetMaps and OSMnx, can simulate traffic on any road network from OSM. By using a Keras three-layer, linear learning architecture, with TensorFlow backend, an optimization algorithm can be implemented for one car in the system. This car will learn its pre-configured route to shortest-time by trying an alternate route which avoids apparent obstacles (such as traffic lights or heavy traffic). The learning algorithm uses Q-learning with epsilon-greedy techniques, and a dynamic reward scheme based on a car's time-to-complete.
Usage:
After customizing desired parameters, run the artist scratch file with python artist.py to render .mp4 movies of a traffic simulation.
Or after selecting a learning agent from the available cars, run python learn.py to optimize that car's route to shortest-time.
Simple Example of Optimization
The above figure is a simple example of how a learning agent reroutes around a single traffic light.
In the original route (left) the learning agent is in State 1. State 1 implies three criteria: (1) there are obstacle(s) in the route, (2) the detour around the most significant obstacle is short, (3) the detour contains no obstacle(s). (See the Projects tab of this repository to learn about all 10 possible states in the Markov decision chain for this project.)
The alternate route (right) is traveled in lieu of the original if the learning agent takes Action 1 (Action 0 is to do nothing).
The reward-scheme for this learning algorithm is based on whether the time to travel from A to B is the shortest of the two action possibilities. Since the alternate route above does not experience a traffic light, taking Action 1 (i.e. rerouting) will be preferred the more the learning agent performs the simulation.
Sure enough, the average reward per game improves while training for 90-games (above).
Car 17 Route and Alternate Route
There are two traffic lights (shown as blue dots) in the initial route for this car. Currently by default, the rerouting algorithm chooses to reroute around the light with the longest switch-time. In this case, it was the second light in the route which was avoided by the rerouting algorithm. Since traffic light switch-times are randomly initialized each time a map is drawn, this will not always be the case.
A note on the requirements for this code base
Installing
tensorflow 1.9.0 py36_0 (latest 12/29/2018)
conflicts with any python 3.7 (py37) environment because tf uses python >=3.6,<3.7.0a0. To handle this, I've created another environment with all the python 3.6 (py36) versions of the required packages for this code base. To install this py36 environement, see requirements_py36.txt in the requirements folder.
Once TensorFlow is upgraded to py37, the py36 requirements will be depricated.