Location choice modeling for shopping and leisure activities with MATSim

Abstract

This paper presents validation results for the activity-based multi-agent transport simulation MATSim (http://www.matsim.org), where the main focus lies on the location choice module for shopping and leisure activities. Validation results are produced by simulating a 10% sample of the Swiss motorized individual traffic. For Switzerland detailed information about home, working and education locations together with the associated trip matrices are provided by the census. Naturally this level of detail is not available for shopping and leisure trips. In MATSim so far—to create feasible activity chains—location choice for these activities was done in a preprocessing step based on a simple nearest neighbor search, which clearly leads to a systematic underestimation of the traffic volume. In this paper a two-fold shopping and leisure location choice model is presented that produces substantially better results. First and foremost, the to-date exclusively time-based utility function for shopping activities is extended to take into account further determinants of shopping location choice, such as the store size and the stores density in a given neighborhood. In activity-based models, shopping location choice is influenced by leisure location choice, which means that a meaningful shopping location choice model requires a sound leisure location choice model. The long-term goal of MATSim is to model leisure location choice by utility maximization and by including models of social interaction. But these models are far from being productive in agent-based transportation models in general. Hence, we introduce hollow space-time prisms that are derived from empirical data. This approach is—to our knowledge—a novel extension of Hagerstrand’s time geography that by construction produces statistically correct leisure location choice and improves the simulation results in general. Furthermore, the potential of MATSim to also serve as a hypothesis testing tool—besides being a planning tool—is highlighted in this paper. It is shown that MATSim provides the possibility to test models, generated by utility maximizing approaches such as e. g., discrete choice models, in large scenarios, whereby use can be made of data (e. g., count data) that is potentially qualitatively distinct from the data that were used for estimating and validating the models in question in earlier stages.