Although at the moment an excess of 500 public bicycle schemes of variable sizes operate in almost 50 countries worldwide, the impact of their use on travel behaviour and modal change have neither been studied extensively nor have been understood thoroughly as yet. This work negotiates the initial stages of an international research scheme that means to look into the attitudes and system user e...

Due to varying user demands in bicycle sharing systems, operators need to actively shift bikes between stations by a fleet of vehicles. We address the problem of finding efficient vehicle tours by an extended version of an iterated greedy construction heuristic following the concept of the PILOT method and GRASP and applying a variable neighborhood descend (VND) as local improvement. Computatio...

This study uses a large-scale mobile phone dataset to estimate potential demand of bicycle trips in a city. By identifying two important anchor points (night-time anchor point and day-time anchor point) from individual cellphone trajectories, this study proposes an anchor-point based trajectory segmentation method to partition cellphone trajectories into trip chain segments. By selecting trip c...

Bike sharing system requires prediction of bike usage based on usage history to re-distribute bikes between stations. In this study, original data was collected around Washington D.C. area in 2011 and 2012. Original data is processed by several feature engineering approaches based on analysis and understanding of the data. Ridge linear regression, support vector regression (εSVR), random forest...

Bicycle-sharing is experiencing rapidly as a low-carbon transport mode of travel, with the advantages low cost and sustainability. operators use electronic fence parking points to manage bicycle-sharing, but it too time-consuming impractical them individually. Therefore, necessary cluster implement regionalized management. This study uses Mean-shift clustering algorithm on Xiamen Island, constr...

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We conducted individual and ecologic analyses of prospectively collected data from 839 injured bicyclists who collided with motorized vehicles and presented to Bellevue Hospital, an urban Level-1 trauma center in New York City, from December 2008 to August 2014. Variables included demographics, scene information, rider behaviors, bicycle route availability, and whether the collision occurred be...