DictRoadNet: A Dictionary-Based RNN With Road Network Module for GPS Trajectory Completion

The Global Positioning System (GPS) provides precise geographic locations for our vehicles. Nevertheless, it is frequently subject to disruptions, potentially resulting in incomplete or absent trajectory data. To address this challenge, we present DictRoadNet, a framework designed for GPS trajectory completion, which uses a clustering-based dictionary module for initial trajectory generation and a road network module for refining results based on road network data. First, we introduce a dictionary that employs a clustering-based strategy for selecting key-value pairs, which can be used in GPS data processing. This dictionary can provide auxiliary general information acquired from trajectory clusters, enhancing the generation of rational trajectories with additional details. Second, we propose a Road Network Module that utilizes a directed graph to store road network information derived from historical GPS trajectories. This module refines the output by aligning it with an empirically constructed road network, ensuring that trajectory completions are plausible and closely adhere to actual road paths. We achieved enhancements across all tasks when assessed against Average and Final Displacement Error, with the highest enhancement reaching up to 9.50% compared to state-of-the-art methods.