Modeling urban taxi services with multiple user classes and vehicle modes
- K I Wong, S C Wong, H Yang, J H Wu
- Transportation Research Part B
We consider a partially observable Markov decision process (POMDP) model for improving a taxi agent cruising decision in a congested urban city. Using real-world data provided by a large taxi company in Singapore as a guide, we derive the state transition function of the POMDP. Specifically, we model the cruising behavior of the drivers as continuous-time Markov chains. We then apply dynamic programming algorithm for finding the optimal policy of the driver agent. Using a simulation , we show that this policy is significantly better than a greedy policy in congested road network.