Personalized route recommendation through historical travel behavior analysis

• Published in: GeoInformatica Vol. 26; no. 3; pp. 505 - 540
• Main Authors: de Oliveira e Silva, Rodrigo Augusto, Cui, Ge, Rahimi, Seyyed Mohammadreza, Wang, Xin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-07-2022; Springer; Springer Nature B.V
• Subjects: Algorithms; Analysis; Behavior; Computation; Computer Science; Customization; Data Structures and Information Theory; Distance; Geographic information systems; Geographical information systems; Geographical Information Systems/Cartography; Geospatial data; Global positioning systems; GPS; Histograms; Information Storage and Retrieval; Information systems; Multimedia Information Systems; Navigation; Positioning systems; Probability theory; Random walk; Recommender systems; Remote sensing; Roads; Scientific visualization; Shortest-path problems; Trajectories; Travel; GPS trajectories; Personalized travel route recommendation; Random walk with restart; Temporal dependency
• DOI: 10.1007/s10707-021-00453-y
• EISSN: 1573-7624
• ISSN: 1384-6175

Popular navigation applications and services optimize routes based on either distance or time, disregarding drivers’ preferences when suggesting routes. Various unknown circumstances may affect users’ travel behaviors between two locations on the road network, hence it is complicated to provide satisfactory personalized route recommendations. In this paper, it is believed that users’ travel behaviors are implicitly reflected and can be learned from their historical Global Positioning System (GPS) trajectories. The Behavior-based Route Recommendation (BR
2
) method is proposed to compute personalized routes based exclusively on users’ travel preferences. The concepts of appearance and transition behaviors are defined to describe users’ travel behaviors. The behaviors are extracted from users’ past travels and the missing behaviors, of unvisited locations, are estimated with the Optimized Random Walk with Restart technique. Furthermore, the temporal dependency of travel behaviors is considered by constructing a time difference interval histogram. A behavior graph is generated to allow the maximum probability route computation with the shortest path algorithm, resulting in the most likely route to be taken by a user. An extension is proposed, named BR
2
+, to better consider the temporal dependency and incorporate distance in the recommendation process. Experiments conducted on two real GPS trajectory data sets demonstrate the efficiency and effectiveness of the proposed method. In addition, a web-based geographic information system (GIS) called MPR is implemented to demonstrate differences in route recommendation when time, distance, or users’ preferences are considered, besides providing insight about users’ movement through data visualization of their spatial and temporal coverage.