DC10. Using AI for the identification, monitoring and utilisation of a personalised self-learning safe route network for home-school trips

Hosts: UH & ABEONA

Objectives:

• Development of a personalised self-learning safe route network for home-school trips using several data sources, including subjective (user perception, expert opinions) and objective (traffic accidents, near collisions, user stress levels) data
• Monitoring the usage of the personalised safe route network through the detection of routes that do not belong to the identified personalised safe route network
• Development of a gamified system to stimulate end users to use the personalised (previously learned) safe route network

Expected results:

• A router to be adopted in the context of home-school trips of children, to contribute towards improved parental safety perception and towards the development of an automatically generated personalised safe route network in cities and school environments
• A monitoring tool for the usage of the personalised safe route network to stimulate its use, leading to more travel along the safe routes
• New knowledge on the cognitive mechanisms involved to assess the effect of using familiar routes on traffic safety, e.g., better detection of potential hazards and quicker reaction time under unforeseen circumstances
• Assessment of the potential of a gamified system to stimulate end users to use the personalised safe route network

Planned secondment(s):

• TIRF, Purpose: to validate the developed tools in a concrete case; TIRF delivers ‘safe routes to schools programmes’ through software tools, education and hands-on support in schools across Canada