Pick-Up Point Recommendation Using Users’ Historical Ride-Hailing Orders

AbstractThe ride-hailing app must provide users with appropriate pick-up points when they submit their travel demands and their locations are recognized, efficiently reducing users’ operation complexity and optimizing the software performance. Most apps currently try to search for locations near users’ current GPS locations as the Points of Interest (POIs), which is an efficient method of locating, but seriously ignores personal preferences. In this paper, we deeply analyze the historical ride-hailing orders of users on Didi Chuxing platform (http://www.didiglobal.com). We explore the given dataset, get the general regularity of users’ commuting, and propose a Pick-Up Points Recommendation Model (PPRM) based on the clustering algorithm. We cluster users’ historical orders using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) according to orders’ spatial information. In this way, the candidate outputs closest to the user’s current environment/feature can be found in a specific category. The linear addition of the candidate outputs severs as the final pick-up point provided. Therefore, our model can offer recommendations of the best pick-up points. In addition, experimental results based on real-world datasets indicate that our model can efficiently and accurately provide users with optimal points.KeywordsPick-up point recommendationTravel pattern miningCluster analysisRide-hailing systemData analysis