Major navigation companies have resorted to crowdsourcing for obtaining traffic data to improve their services. In fact, a new generation of navigation systems are emerging for the sole purpose of connecting drivers on the road (Waze.com) so that they benefit from each other. The focus of these efforts is on real-time traffic estimation. While obtaining real-time data is desirable and has great benefits, it is still limited, given the potential of the detailed individual data from drivers. For example, commuters in a city have valuable knowledge about their city as a result of their daily commute over many years. Using drivers as mere traffic sensors and aggregating their data desposes off any human knowledge that exists in their choices. In this work, we demonstrate a social navigation system that can help drivers avoid road incidents using expert advice in real-time without explicitly consulting drivers but through an automated system built on top of a location-based social network. In our system, we do not aggregate data from individual users and we instead outsource the task of finding a route that best avoids incidents by looking up drivers' old choices.
['Abdullah AlDwyish', 'Egemen Tanin', 'Shanika Karunasekera']
Location-based social networking for obtaining personalised driving advice