A Nearby Vehicle Search Algorithm Based on HBase Spatial Index

Aiming to solve the nearby vehicle query problem in huge traffic data in the intelligent transportation field, we propose a nearby vehicle search algorithm based on HBase spatial index. Our algorithm builds an HBase spatial index model. It first takes column-oriented data as storage medium of huge traffic data. To build spatial index, it maps two dimensional traffic data of spatial location information into one-dimensional Geohash encoding by dichotomy. Then it saves the mapping relationship between Geohash encoding and traffic data in HBase. Based on this model, when performing nearby vehicle search, we transform query conditions into Geohash encoding by mapping rules, and match it with leftmost prefix to get query results fast. Experiments show that our algorithm has better performance than traditional HBase query methods based on the rowkey. Our method has practical value in large-scale data nearby query field.