A stacking model for variation prediction of public bicycle traffic flow

Fei Lin · Jiang Jian · Jin Fan · Shihua Wang

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Urban Transport and Accessibility Transportation Planning and Optimization Traffic Prediction and Management Techniques

Abstract

Public bicycle system can improve the public transport travel efficiency and reduce environmental pollution, which has been deployed in many cities all over the world. However, the bicycle usages become quite skewed and imbalanced in different stations. A system which could recommend the nearest av ailable stations for passengers whether they are looking for a dock or a bike, is of great importance. Monitoring the current number of docks or bikes at each station cannot tackle this problem because it’s too late to recommend the station for passengers to rent or return bikes after the imbalance has occurred. To address this issue, we propose a stacking model for variation prediction of public bicycle traffic flow called SMVP based on the real-world datasets. The stacking model integrates multiple base models which we trained by different combinations of features so that it could get better performance. We adopt a machine learning system called XGBoost [25] to train the models and construct the multiple complex factors which impact the public bicycle traffic flow. The traditional factors, such as temporal, spatial, historical and meteorological factors are taken into consideration. A new clustering factor which considers both the geographical positions and transition patterns of stations is also proposed in this framework and then we use the K-Medoids algorithm [12] to cluster stations into groups by constructing a new different station relation matrix which considers these two factors as the distance between different stations. The performance of SMVP is improved on the datasets of Hangzhou and New York City, especially in terms of Coefficient of Determination improved by 25.58% in Hangzhou, compared with the traditional stacking [5] and single model respectively.

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References

2009Bike-sharing: History, Impacts, Models of Provision, and Future 2010Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system 2015Traffic prediction in a bike-sharing system 2011Understanding Bike-Sharing Systems using Data Mining: Exploring Activity Patterns 2009Sensing and predicting the pulse of the city through shared bicycling 2011SHARED BICYCLES IN A CITY: A SIGNAL PROCESSING AND DATA ANALYSIS PERSPECTIVE 2009The role of smart bike-sharing systems in urban mobility 2014Model-Based Count Series Clustering for Bike Sharing System Usage Mining: A Case Study with the Vélib’ System of Paris

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