Summary Gamification is a new theme that has been applied in different fields and has contributed to different types of behavioural change. This paper aims to describe how gamification is adopted in the context of transportation. Methods We performed a systematic mapping of the scientific literature of Web of Science and retrieved 211 studies. After the inclusion and exclusion criteria were applied, 66 studies were selected. After the full texts were read, 30 studies remained to be analysed. Findings The results show that the most commonly used gamification elements are goals/challenges and points. Gamification provides support for outcomes such as changing travel behaviour, improving driving behaviour and encouraging bicycle commuting. The use of gamification has changed the behavior of travelers, promoted sustainable travel modes, encouraged safe driving, reduced carbon dioxide emissions and reduced energy consumption. Although gamification has achieved many positive results related to transportation, there are still many difficulties and challenges.

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Understanding the impact of smartphone‐based multimodal information (SMMI) on travellers' P&R (park‐and‐ride) choice behaviour is very limited so far. The purpose of this study is to better understand how SMMI, social network information, and individual characteristics influence travellers' mode choices. A stated preference experiment consisting of one P&R option and two auto‐driving routes was conducted to collect car commuters’ P&R choice data in Shanghai, China. The panel mixed logit model was utilized to determine the influencing factors. It was found that the panel mixed logit model, accounting for correlations among repeated observations of the same respondent and random taste, significantly outperforms the cross‐sectional multinomial logit model in terms of goodness‐of‐fit. Specifically, travellers are highly sensitive to the information offered by SMMI on travel time, parking fare, and crowding level in subway cars, and heterogeneities do exist in travellers' preferences for these factors. In terms of social network information, the positive propensity of online reviews and information about P&R play a positive role in P&R promotion. In addition, individual characteristics including gender, age, occupation, years of driving, and P&R experience all contribute to explaining the choice of P&R. Finally, the elasticity analysis reveals that commuters are more satisfied with P&R time than with car time, and the cross elasticity of P&R time demonstrates a limited substitution effect of P&R on private cars.