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Modelling of User Behaviour for Static Rebalancing of Bike Sharing System: Transfer of Demand from Bike-Shortage Stations to Neighbouring Stations

机译:自行车共享系统静态再平衡的用户行为建模:骑自行车短缺站的需求转移到邻近站

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Bike sharing systems are becoming more and more common around the world. One of the main difficulties is to ensure the availability of bicycles in order to satisfy users. To achieve this objective, managers of these systems set up rebalancing vehicles that displace bicycles to stations that are likely to be in a situation of bike shortage. In order to determine which stations must be supplied on a priority basis and the number of bicycles to be supplied (named in this paper as rebalancing plan), the aim is generally to reduce the lost demand for each station, i.e., the gap between the demand for bicycles and the number of bicycles at a station. On the one hand, this paper proposes an algorithm that evaluates the lost demand in a more realistic way, by describing the behaviour of users faced with a bike-shortage station. It takes into account the possibility that a proportion of users who cannot find bicycles will move to a neighbouring station that is not empty. This proportion depends on the distance between stations and corresponds to the number of users willing to walk a given distance to a neighbouring station. On the other hand, this algorithm provides the value of the objective function to be minimized to a static rebalancing plan algorithm based on a Random Search metaheuristic. The quantities of bicycles to be picked up and dropped off at each station are calculated in a static rebalancing context. The calculation of lost demand based on this algorithm, which simulates user behaviour, was compared with that one obtained by the classical method on a real numerical example obtained from the open data of Parisian Vélib? (more than 1200 stations). In addition, the efficiency of the rebalancing algorithm coupled with the user behaviour simulation algorithm was evaluated on this numerical example and allowed to obtain very good results compared to the rebalancing performed by the system operator.
机译:自行车分享系统在世界各地变得越来越普遍。其中一个主要困难是确保自行车的可用性以满足用户。为实现这一目标,这些系统的管理人员建立了将自行车移动到可能处于自行车短缺情况的站的重新平衡车辆。为了确定必须在优先级提供哪些站点和提供的自行车数量(本文以重新平衡计划命名),旨在降低对每个站的需求损失,即对自行车的需求和车站的自行车数量。一方面,本文提出了一种以更现实的方式评估失去需求的算法,通过描述面临自行车短缺站的用户的行为。它考虑了无法找到自行车的用户比例的可能性将移动到不空的邻近站。这比例取决于站之间的距离,并且对应于愿意走向邻居站的给定距离的用户数量。另一方面,该算法提供了目标函数的值,该函数将最小化到基于随机搜索成群质训练的静态再平衡计划算法。在每个站点拾取并卸下的自行车的数量在静态再平衡背景下计算。基于该算法的丢失需求计算,它模拟了用户行为,与由帕累斯人Vélib的开放数据中获得的真正数值示例中的经典方法获得的那个算法进行了比较? (超过1200个站点)。另外,在该数值示例中评估了与用户行为仿真算法耦合的重新平衡算法的效率,并允许获得与系统操作员执行的重新平衡相比的非常好的结果。

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