Leveraging trajectory simplification for efficient map-matching on road network

Trajectory data is central to many applications with moving objects due to the popularity of Global Positioning System (GPS) devices. Raw trajectory data is usually of large volume, which incurs high storage and processing costs and require heavy computational cost for post process. A promising approach to tackling this issue is to map raw trajectory data to a sequence of discrelized road links (symbols) on a road network, which is called map-matching. However, existing map-matching algorithms also require heavy computational cost. In this paper, we propose a new offline trajectory simplification metric suitable for map-matching on road network. We present a polynomial-time algorithm for quality optimal closest road preserving simplification. Additionally, we conduct experimental evaluation with real-life trajectory datasets and the results demonstrate the superior performance of our methods.