Recently, Location-based services (LBSs) refer to the services that are based on the location (spatial) data, which bring conveniences to our daily life and challenges to both industry and academia. LBSs include the “spatial crowdsourcing” service, which allows requesters to post spatial tasks to specified locations then the crowd workers will move to the locations of the assigned tasks to conduct, the “location-based mobile advertising” service, which helps the vendors to push advertisements to potential customers near to the shops, and the “search nearby” service, which queries some points of interests (POI, e.g., hotels, parks, museums) near a location. Among the LBSs, online-to-offline (O2O) is a widely applied mechanism, where users join activities, plan travel routes and o...[ Read more ]

Recently, Location-based services (LBSs) refer to the services that are based on the location (spatial) data, which bring conveniences to our daily life and challenges to both industry and academia. LBSs include the “spatial crowdsourcing” service, which allows requesters to post spatial tasks to specified locations then the crowd workers will move to the locations of the assigned tasks to conduct, the “location-based mobile advertising” service, which helps the vendors to push advertisements to potential customers near to the shops, and the “search nearby” service, which queries some points of interests (POI, e.g., hotels, parks, museums) near a location. Among the LBSs, online-to-offline (O2O) is a widely applied mechanism, where users join activities, plan travel routes and order goods online, then perform the according actions offline. To support this fundamental mechanism, task assignment and scheduling are necessary and important, which match or schedule the tasks to users under the constraints (e.g., spatial-temporal constraints, capacity constraints, budget constraints).

In this thesis, we study task assignment and scheduling techniques in three practical problems in the spatial crowdsourcing area, namely the Utility-Aware Ridesharing on Road Networks problem, which matches riders to vehicles and schedules the routes for vehicles with a goal of maximizing the overall utility of riders (i.e., vehicle-related utility, rider-related utility and trajectory-related utility) subject to the constraints of the deadlines of riders and the capacities of the vehicles, the Reliable Diversity-Based Spatial Crowdsourcing problem, which assigns spatial workers to spatial tasks to maximize the completion reliability and the spatial/temporal diversities of spatial tasks subject to the constraints of the valid periods of tasks and the working areas of the workers, and the Multi-Skill Spatial Crowdsourcing problem, which assigns spatial workers to multi-skill required tasks to finish as many tasks as possible and to minimize the total travel cost of workers.