Effective Social Road Map with Routing Shortest Path Prediction Using Multi Path Query Selection Model

Abstract

In this paper, the mining of road information for raw GPS information is successfully implemented. To be specific, we have demonstrated that the LSM will effectively estimate the real heading off the road from raw GPS information. Additionally, the WALSM is progressed to estimate the more correct heading off the road under finite GPS point samples to improve the accuracy of the LSM. In the WALSM, the impact of GPS noise through the estimation can be resisted by this weighted issue. The location-based services have a vital problem, mainly in estimating the shortest distance. We planned a model named minimum path pair query, that consists of a pair of source and estimation and a user-specified weight to balance the 2 completely different desires. The result is a combination of paths connecting the 2 sources and destinations severally, with the lowest overall price of the 2 paths and also the shortest route between them. To resolve Minimum Path pair queries, we tend to used depicted algorithms by enumerating the node pairs. A location-based pruning strategy is adopted to reduce the number of node pairs for enumeration. To further improve query efficiency, point-to-point shortest path calculation is planned based on the economic formulation. Extensive experiments are conducted to point out the effectiveness and efficiency of our strategies.