A Hybrid Algorithm for Fast Learning Individual Daily Activity Plans for Multiagent Transportation Simulation

This paper propose a hybrid learning algorithm based on the competing risk duration model and the cross entropy method for generating complete all-day activity plan in multiagent transportation simulation. We formulate agent´s activity scheduling problem as a sequential Markov decision process. By initially generating individual´s activity type and duration sequence from empirical data based on the competing risk duration model, the obtained plans can be efficiently improved by reinforcement learning technique towards near-optimal activity plan. We apply the cross entropy method to efficiently learn near-optimal activity plan. The numerical result shows that the proposed method generates consistent daily activity plans for multiagent transportation simulation.