I Open at the Close: A Deep Reinforcement Learning Evaluation of Open
  Streets Initiatives
R. Teal Witter Lucas Rosenblatt
The open streets initiative "opens" streets to pedestrians and bicyclists by
closing them to cars and trucks. The initiative, adopted by many cities across
North America, increases community space in urban environments. But could open
streets also make cities safer and less congested? We study this question by
framing the choice of which streets to open as a reinforcement learning
problem. In order to simulate the impact of opening streets, we first compare
models for predicting vehicle collisions given network and temporal data. We
find that a recurrent graph neural network, leveraging the graph structure and
the short-term temporal dependence of the data, gives the best predictive
performance. Then, with the ability to simulate collisions and traffic, we
frame a reinforcement learning problem to find which streets to open. We
compare the streets in the NYC Open Streets program to those proposed by a
Q-learning algorithm. We find that the streets proposed by the Q-learning
algorithm have reliably better outcomes, while streets in the program have
similar outcomes to randomly selected streets. We present our work as a step
toward principally choosing which streets to open for safer and less congested
cities. All our code and data are available on Github.