Computation of Location Choice of Secondary Activities in Transportation Todels with Cooperative Agents

Abstract

Activity-based models in Transportation Science focus on the description of human trips and activities. We address the modeling of the spatial decision for so-called secondary activities: given both home and work locations, where do individuals perform activities such as shopping and leisure? The simulation of these decisions using random utility models requires a full enumeration of the possible outcomes. For large data sets, it becomes computationally unfeasible because of the combinatorial complexity. To overcome this limitation, we propose a model where agents have limited, accurate information about a small subset of the overall spatial environment. Agents are inter-connected by a social network through which they can exchange information. This approach has several advantages compared to the explicit simulation of a standard random utility model: a) it computes plausible choice sets in reasonable computing times b) it can be easily extended to integrate further empirical evidence about travel behavior and c) it provides a useful framework to study the propagation of any newly available information. The paper emphasizes the computational efficiency of the approach for real-world examples.