  Owing to the expeditious growth in the information and communication
technologies, smart cities have raised the expectations in terms of efficient
functioning and management. One key aspect of residents' daily comfort is
assured through affording reliable traffic management and route planning.
Comprehensively, the majority of the present trip planning applications and
service providers are enabling their trip planning recommendations relying on
shortest paths and/or fastest routes. However, such suggestions may discount
drivers' preferences with respect to safe and less disturbing trips. Road
anomalies such as cracks, potholes, and manholes induce risky driving scenarios
and can lead to vehicles damages and costly repairs. Accordingly, in this
paper, we propose a crowdsensing based dynamic route planning system.
Leveraging both the vehicle motion sensors and the inertial sensors within the
smart devices, road surface types and anomalies have been detected and
categorized. In addition, the monitored events are geo-referenced utilizing GPS
receivers on both vehicles and smart devices. Consequently, road segments
assessments are conducted using fuzzy system models based on aspects such as
the number of anomalies and their severity levels in each road segment.
Afterward, another fuzzy model is adopted to recommend the best trip routes
based on the road segments quality in each potential route. Extensive road
experiments are held to build and show the potential of the proposed system.
