Finding Frequent Route of Taxi Trip Events Based on MapReduce and MongoDB

Efficient Frequent Sequence Mining on Taxi Trip Records Using Road Network Shortcuts

Huge amounts of geo-referenced spatial location data and moving object trajectory data are being generated at ever increasing rates. Patterns discovered from these data are valuable in understanding human mobility and facilitating traffic mitigation. In this study, we propose a new approach to mining frequent patterns from large-scale GPS trajectory data after mapping GPS traces to road network...

Pick-Up Tree Based Route Recommendation from Taxi Trajectories

Recommending suitable routes to taxi drivers for picking up passengers is helpful to raise their incomes and reduce the gasoline consumption. In this paper, a pick-up tree based route recommender system is proposed to minimize the traveling distance without carrying passengers for a given taxis set. Firstly, we apply clustering approach to the GPS trajectory data of a large number of taxis that...

(Blue) Taxi Destination and Trip Time Prediction from Partial Trajectories

Real-time estimation of destination and travel time for taxis is of great importance for existing electronic dispatch systems. We present an approach based on trip matching and ensemble learning, in which we leverage the patterns observed in a dataset of roughly 1.7 million taxi journeys to predict the corresponding final destination and travel time for ongoing taxi trips, as a solution for the...

Demand Pattern Analysis of Taxi Trip Data for Anomalies

Route Prediction from Trip Observations

This paper develops and tests algorithms for predicting the end-to-end route of a vehicle based on GPS observations of the vehicle’s past trips. We show that a large portion of a typical driver’s trips are repeated. Our algorithms exploit this fact for prediction by matching the first part of a driver’s current trip with one of the set of previously observed trips. Rather than predicting upcomi...