The development of new more efficient freight transportation services requires in depth understanding of the engaged stakeholders behavior. Simulation Games (SG) have been used to study decisions and raise awareness over complex transport problems. This paper investigates the possibility of applying a simulation game as an innovative data collection tool for choices related to freight transport. Games sessions were organised with Dutch logistics managers and data on their choices for synchromodal services were collected. Model results revealed reliability, costs and reduced work load as key factors making shippers to opt for more synchromodal services. Keywords: synchromodality, simulation games, behavioural data collection, service choice, multinomial choice model.

Agent-based freight models are used to simulate a plethora of agents, agent attributes, and operational environments. The population in these models typically comprises business establishments or firms. Due to lack of data, attributes are often limited to readily available traits such as firm size and industry category. This research addresses the data gap issue by building a data development engine (DDE). The DDE extracts key company data from big, online, text-based information systems and builds a dataset of companies to use in model estimation. The primary, initial focus of the DDE is to develop data regarding strategies that are adopted by companies and used to guide company decisions. Earlier work has shown that including firm strategies in an agent-based freight model is important for estimating freight agent behavior and, ultimately, impacts on energy use and vehicle-miles traveled. Factor Analysis and Structural Equation models are used to identify firm emphasis areas and a latent variable that weighs the relative importance of two emphasis areas (service vs. product innovations). The resulting strategic data is used to inform a model of private fleet ownership.

Truck surveys are carried out continuously in all EU and EEA countries according to Eurostat statistics regulations. These surveys are known to have a high reporting burden, and a major challenge is that a large share of trucks is incorrectly reported to be out of order during the reporting week, resulting in low quality data. This paper highlights how smart data capture can help reduce reporting burden and increase data quality. It further provides perspective on requirements to other data sources with potential relevance for smart and improved transport statistics, as well as their current limitations.