Abstract：Worker route planning is an important spatial crowdsourcing research topic. Previous research on route planning has mainly focused on offline situation. However, online situation are more relevant to actual needs. Therefore, this study analyzes an online route planning problem extracted from a typical spatial crowdsourcing platform involving crowdsourcing logistics and shared bus, which is called the online route planning problem with fixed endpoints in spatial crowdsourcing. The first step is to analyze the route planning in the Euclidean space using a particle swarm optimization method. The algorithm plans the route by tracking the optimal solution online. A local particle swarm optimization method for online route planning is also used based on the k nearest neighbours. The route planning algorithm is then applied to a road network using weighted shortest path boundary indexing. The effectiveness and efficiency of this algorithm are verified through extensive experiments on real datasets with the local particle swarm optimization method for online route planning showing better performance than the compared method.
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