Just do it! Combining agent-based travel demand models with queue based-traffic flow models

Proper travel demand models aim to create an equilibrium between expected travel times in the planning phase and simulated travel times after mapping the road traffic on the road network. While agent-based travel demand models (ABM) focus on the trip generation mainly based on pre-calculated travel times, traffic flow models simulate these trips and compute travel times taking into account speed restrictions and road capacities. This leads to deviations between the simulated travel times and the initially expected ones especially during rush hour so that both models are not in equilibrium state. Due to the complexity and limited computational resources, combinations of these two models are often simplified in either one or both parts. In this work we present an iteratively combined simulation model with feedback of travel times. We couple an ABM with a queue-based traffic flow model which simulates the set of trips for each agent. The ABM used adjusts its activity generation, destination choice and mode choice according to the re-calculated travel times resulting in more realistic day plans. The traffic flow model takes the sequential character of the trips into account and propagates the delay to the subsequent trips of each modelled agent, resulting in feasible trips. We show that equilibrium of travel time between these two models can be achieved with a low number of iterations. Our approach is sensitive to new travel times in destination and mode choice and results in trips which are consistent for a whole day for each modelled agent. (C) 2018 The Authors. Published by Elsevier B.V.