Mobile networks have brought the opportunity of using smartphone data to investigate the challenges of road safety and travel mode choice for sustainable urban mobility. This thesis adopts the mobile cloud computing (MCC) approach and uses smartphone data to address these two challenges.

Road safety influences the mode choice between private cars and low-carbon transportation that usually involves walking and cycling. In this regard, pedestrian collision avoidance is crucial for both conventional and autonomous vehicles. As dedicated 802.11p-based device-to-device communication is not readily available on smartphones, collision warning systems could use a designated mobile app for indirect vehicle-to-pedestrian (V2P) communication and centrally perform the collision prediction on cloud servers. Vehicular safety applications require high-frequency beaconing to achieve adequate locational and temporal precisions. In these situations, the power consumption of beaconing can create a bottleneck due to the limited capacity of smartphone batteries. In addition, cellular network capacity and cloud computation resources could be limiting factors that require their own city-scale assessments. To address these issues, this thesis investigates the practicality of smartphone-based V2P collision warning systems by means of model and prototype evaluations. We need to analyze extent and variations of battery consumption under different traffic conditions, assuming a situation-adaptive rate control is used for communication. We also need to examine the network and computation load of the system and compare them against the capacity and cost of available infrastructures.