  Inspired by expert evaluation policy for urban perception, we proposed a
novel inverse reinforcement learning (IRL) based framework for predicting urban
safety and recovering the corresponding reward function. We also presented a
scalable state representation method to model the prediction problem as a
Markov decision process (MDP) and use reinforcement learning (RL) to solve the
problem. Additionally, we built a dataset called SmallCity based on the
crowdsourcing method to conduct the research. As far as we know, this is the
first time the IRL approach has been introduced to the urban safety perception
and planning field to help experts quantitatively analyze perceptual features.
Our results showed that IRL has promising prospects in this field. We will
later open-source the crowdsourcing data collection site and the model proposed
in this paper.
