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Reduction of car use is one of the most effective ways to tackle congestion‐related problems. Using positive incentives to stimulate bicycle use is one possibility to reduce car use. Cycling is a sustainable transport mode that uses little space and is healthy. There is evidence that positive incentives may be more effective than punishing travellers for undesirable behaviour, and the emergence of mobile applications for delivering interventions has opened up new opportunities for influencing travellers. So far, few studies have focused on exploring the effectiveness of positive incentives on long‐term behavioural change. We used the SMART app to deliver positive incentives to more than 6000 travellers in the Dutch region of Twente. The app automatically tracks users and provides incentives such as challenges with rewards, feedback, and messages. This study covers the period from March 2017 to June 2018, in which more than 1000 SMART users participated in monthly challenges. We evaluated the effects of the challenges and rewards and found that the challenges did encourage cycling and reduced car use in the short term. There is also some evidence for behavioural change over a longer time period.

This study presents results from an investigation into the effect of positive incentives on cycling behaviour among 1802 commuters in the Twente region of the Netherlands. The authors used an on-line survey, which included mock-up apps with incentives to commute to work by bicycle. They tested five reward schemes, namely social rewards (such as badges), in-kind gifts, money, competition, and cooperation. They used the survey data in a multinomial logit model to estimate to what extent travellers will use the app and increase their cycling frequency and which incentives they prefer. The model results show that respondents who sometimes cycle to work are more positive about incentive schemes than respondents who never cycle and that offering an app with in-kind gifts is probably most effective. Interestingly, non-cyclists are more likely to change their behaviour for a reward if they care about travel costs, while occasional cyclists are more likely to cycle more often in response to incentives if they care about attributes that are related to the cycling itself. This also depends on attitudes towards cycling and on socio-demographic variables.

Events and incidents are relatively rare, but they often have a negative impact on traffic. Reliable travel demand predictions during events and incident detection algorithms are thus essential. The authors study link flows that were collected throughout the Dutch city of Almelo. We show that reliable, event-related demand forecasting is possible, but predictions can be improved if exact start and end times of events are known, and demand variations are monitored conscientiously. For incident detection, we adopt a method that is based on the detection of outliers. Our algorithm detects most outliers, while the fraction of detections due to noisy data is only a few percent. Although our method is less suitable for automatic incident detection, it can be used in an urban warning system that alerts managers in case of a possible incident. It also enables us to study incidents off-line. In doing so, we find that a significant fraction of traffic changes route during an incident.