Serious gaming to co-create a value-based mobility mix in a multi-objective arena

Abstract

Introduction

The world is facing multiple challenges, e.g. in the realm of health, urbanization, climate change and social inclusion. Mobility can be part of the problem yet has the potential to be part of the solution. Serious games can support decision-making in a complex environment. This paper describes a serious game that aims to stimulate individual learning of (future) professionals in transport and urban planning, and facilitate discussions on goals and arguments concerning different (new) mobility options.

Methods

This paper presents the first version of the serious game: ‘the Optimal Mobility Mix’. This card game was developed and played in five sessions in the Netherlands, India and the USA with over 100 participants. Feedback was collected informally in a debrief, through a survey (N = 25) and through observation.

Results

During the game, participants formed small groups and collaborated to select a mobility mix using playing cards that aligned with the five societal goals (effective mobility, efficient city, economy, environment & health and equity), of the 5E framework (Van Oort et al., 2017). The survey indicated that the game was highly interactive, fun and relevant. Although the game's ‘reflection of reality’ scored relatively low, it still remained positive.

Conclusions

Overall, the game successfully facilitated discussion on societal goals and transportation modes and appears positive on its learning objectives. The effect of the game in policy and decision making practice will be tested in future research. Striking a balance between capturing the complexity of reality and creating an accessible and flexible model for professionals to interact with – in the form of a game, is an ongoing challenge. Future developments aim to involve key players and broader planning-related policies through a codesign process with game design iterations. To support this process, observations and recordings can be intensified to improve feedback processing.