Routing with Massive Trajectory Data
Christian S. Jensen, Bin Yang, Chenjuan Guo, Jilin Hu, Kristian Torp · 2024
The unprecedented availability of new types of data coupled with the invention of new technologies combine to enable entirely new or higher-resolution services that in turn enable more rational and data-driven processes. We consider the overall process of vehicular transportation and, more specifically, the process of deciding which route to follow when having to reach a destination. Early solutions modeled a road work as a graph, used sparse in-road sensor data to assign weights to graph edges, and then applied improved versions of Dijkstra's algorithm to find routes with the lowest sums of edge weights. Since then, massive vehicle trajectory data has become available. When coupled with new technologies, this data enables entirely new and higher-resolution routing services that in turn enable better routing. For more than a decade, the authors have engaged in research aimed at exploiting trajectory data to enable better routing. The resulting technologies were developed outside a DBMS. Here, we cover aspects of this research. Further, we challenge the community to develop DBMS support for these and other aspects of routing.