D riving B eacon: Driving Behaviour Change Support System Considering Mobile Use and Geo-information (2022)

Abstract: Natural Language Generation has been proved to be effective and efficient in constructing health behaviour change support systems. We are working on DrivingBeacon, a behaviour change support system that uses telematics data from mobile phone sensors to generate weekly data-to-text feedback reports to vehicle drivers. The system makes use of a wealth of information such as mobile phone use while driving, geo-information, speeding, rush hour driving to generate the feedback. We present results from a real-world evaluation where 8 drivers in UK used DrivingBeacon for 4 weeks. Results are promising but not conclusive.

## introduction there has been a long tradition of adopting natural language generation (nlg) techniques in health care @xcite @xcite . one line of work focus on building behaviour change support systems (bcsss) to help people live healthier and more safely. these include systems for encouraging people to stop smoking @xcite , for ecological driving @xcite @xcite , and for safer driving @xcite 1 . such bcsss can generate feedback automatically based on users' current behaviours by employing nlg techniques. within the domain of safe driving, personalised feedback via postal mail has proved to be useful to improve users' driving habits @xcite . for example, drivesafe @xcite is a mobile application that utilises data from vehicle cameras 1 the united nations considers unsafe driving to be a health issue and lists the target of fewer road traffic accidents as a health goal for sustainable development. see: https:// sdgs.un.org/goals/goal3 . combined with gps and audio data from the mobile phone to identify unsafe driver behaviours. drivesafe estimate a driving score for each driver and then provides alerts when the score crosses a certain threshold. eco-driving @xcite is a study about reducing gas emissions arising from bad driving styles. minimising unnecessary acceleration and braking can improve eco-driving and fuel consumption and eventually reduce emissions. the study also highlights that despite the benefits of eco-driving, drivers also require feedback about their actions in order to promote long-term behavioural change. another study by @xcite investigated the high fatality rate amongst young novice drivers and their use of mobile phones while driving. a survey among 700 young drivers was conducted, where they compared distracted driving behaviour. they focused on participants' use of smartphones during driving and found that most of those uses are music-related activities (e.g., playing the next song or increasing the volume). @xcite built saferdrive, the first nlg based driving bcss, which generates weekly textual driver feedback from telemetric data foot_0 and the feedback is delivered through mobile phones. it was reported that the generated textual feedback is more helpful to drivers than the traditional scorebased and map-based feedback, especially to learners and young drivers @xcite . this is because textual feedback gives drivers a more concrete idea of how to change their driving behaviours. for example, for a speeding incident, saferdrive could generate feedback such as "you speeded 7 times on roads with 20 mph speed limit and 12 times on roads with 30 mph speed limit". this paper introduces drivingbeacon which is able to make use of richer information compared to saferdrive aiming at generating better feedback reports. vital additional information includes (i) the mobile phone use information of drivers during driving; and (ii) geofencing, which highlights driving incidents that take place near crowded places such as schools, mosques, superstores, etc. to assess our drivingbeacon, we conduct a real-world a/b test on 8 drivers in uk. this is not a lot of drivers, but it is more than the 6 drivers used by @xcite . concretely, we generated basic feedback (not considering rich information) and enhanced feedback (considering rich information) using drivingbeacon. we divided our 8 drivers into two groups, one of which is sent the basic feedback while the other is sent the enhanced feedback. the experiment lasted for 4 weeks, during which we monitor the change in their driving behaviours. to summarise, the key contributions of our work are two-fold: drivingbeacon pulls raw data from the datahub using the damoov api and extracts related geo-information using google map api. with these data, we use a rule-based nlg system to generate feedback reports. ## feedback generation we classify the information we obtain into two sets: one contains the information that also has been used in saferdrive @xcite (henceforth basic information), including information such as location, speed, speed limit, time, etc. the other contains additional information, including mobile phone usage, geo-fencing (driving near crowded places), traffic law and penalty points. basic feedback reports include only the first set, while enhanced feedback reports include both sets. we also list the driving behaviours that driv-ingbeacon captures in ## evaluation in order to evaluate our system in a real-world scenario, we conducted a short longitudinal study where we evaluated the system with real drivers. ## conclusion we presented drivingbeacon, a behaviour change support system which can generate en-hanced feedback reports by utilising the mobile use information and geo-information in addition to telemetric data. experimental results suggest that enhanced reports are more effective than the basic reports, although the difference is not significant. in the future, we plan to make the enhanced reports more effective by personalising feedback reports for individual drivers based on their interests, background, and driving history. we also plan to test our system in a larger-scale experiment with regard to both the number of participants and the duration of the experiment.