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.

Background Since December 2019, COVID-19 began to spread throughout the world for nearly two years. During the epidemic, the travel intensity of most urban residents has dropped significantly, and they can only complete inflexible travel such as "home to designated hospital" and "home to supermarket" and some special commuting trips. While ensuring basic travel of residents under major public health emergency, there is also a problem of high risk of infection caused by exposure of the population to the public transport network. For the discipline of urban transport, how to use planning methods to promote public health and reduce the potential spread of diseases has become a common problem faced by the government, academia and industry. Method Based on the mobility perspective of travel agents, the spatial analysis methods such as topological model of bus network structure, centrality model of public transport network and nuclear density analysis are used to obtain the exposure risk and spatial distribution characteristics of public transport from two aspects of bus stops and epidemic sites. Results The overall spatial exposure risk of Wuhan city presents an obvious "multi center circle" structure at the level of bus stops. The high and relatively high risk stops are mainly transport hubs, shopping malls and other sites, accounting for 35.63%. The medium and low-risk stops are mainly the villages and communities outside the core areas of each administrative region, accounting for 64.37%. On the other hand, at the scale of epidemic sites, the coverage covers 4018 bus stops in Wuhan, accounting for 36.5% of all bus stops, and 169 bus lines, accounting for 39.9% of all routes. High risk epidemic sites are mainly concentrated in the core areas within the jurisdiction of Wuhan City, and in the direction of urban outer circle diffusion, they are mainly distributed in the low and medium risk epidemic sites. According to the difference of the risk level of public transport exposure, the hierarchical public transport control measures are formulated. Discussion This paper proposes differentiated prevention and control countermeasures according to the difference of risk levels, and provides theoretical basis and decision-making reference for urban traffic management departments in emergency management and formulation of prevention and control countermeasures.

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.