Learning to Route with Sparse Trajectory Sets---Extended Version

TL;DR

This paper introduces a trajectory-based routing method that constructs a graph from sparse vehicle trajectories, transfers routing preferences, and improves routing quality compared to existing services, demonstrating practicality through empirical studies.

Contribution

It presents a novel approach to build a routing infrastructure from sparse trajectory data and transfer preferences to enhance route planning.

Findings

Effective clustering of trajectories into regions.

Successful transfer of routing preferences to sparse data regions.

Improved routing quality over existing services.

Abstract

Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route, a comprehensive trajectory-based routing solution. Specifically, we first construct a graph-like structure from trajectories as the routing infrastructure. Second, we enable trajectory-based routing given an arbitrary (source, destination) pair. In the first step, given a road network and a collection of trajectories, we propose a trajectory-based clustering method that identifies regions in a road network. If a pair of regions are connected by trajectories, we maintain the paths used by these trajectories and learn a routing preference for travel between the regions. As trajectories are skewed and sparse, many region pairs are not connected by trajectories. We thus transfer routing preferences from region pairs with sufficient trajectories to such region pairs and then use the transferred preferences to identify paths between the regions. In the second step, we exploit the above graph-like structure to achieve a comprehensive trajectory-based routing solution. Empirical studies with two substantial trajectory data sets offer insight into the proposed solution, indicating that it is practical. A comparison with a leading routing service offers evidence that the paper's proposal is able to enhance routing quality. This is an extended version of "Learning to Route with Sparse Trajectory Sets" [1], to appear in IEEE ICDE 2018.