Real-time navigation in urban areas using mobile crowd-sourced data

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems (ITS). Mobile crowd-sourcing enabling automatic sensing tasks constitutes an excellent mean to complement existing technologies. In this paper, we exploit the high amount of data that can be collected by on-board and infrastructure-based sensors to evaluate traffic network statuses and improve the navigation of vehicles in urban areas. The objective is to design real-time route planning algorithms that determine best trajectories in a real-time manner based on the frequent data inputs. Two iterative algorithms with different complexity levels solving integer linear programming problems are developed. Unlike traditional navigation solutions, the algorithms update the vehicle trajectory after a certain period characterized by timely correlated data. Our results show that the crowd-sourcing based real-time algorithms outperform traditional navigation solutions by selecting less congested roads and avoiding blocked streets.