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Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems. Mobile crowdsourcing 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 fastest trajectories for both single and multiple destinations, in a real-time manner based on the frequent data inputs. We first formulate the routing problems as integer linear programs (ILPs) and then, design iterative approaches levels to iteratively solve the ILPs while considering updated traffic data. Afterwards, lower complexity sub-optimal graph-based algorithms are designed to …