Abstract
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 Vélibʼ (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.
Highlights
In order to show the relevance of the proposed method, we have carried out three different experiments in the Velib’ system at Paris. ese three experiments have different objectives. e first one aims at showing and analysing the gap in the evaluation of the loss of demand between the proposed method and the method more frequently used in the literature. e second application aims to show the interest of using this method to define the static rebalancing plan of the Paris Velibsystem, by applying the coupling defined in Section 4. e estimated lost demand after implementing this plan will be compared to the estimated lost demand after implementing the rebalancing solution carried out by JCDecaux
We will apply the method in a prospective framework, in which we will evaluate the static rebalancing plan by considering a progressive increase in demand. is final test stage is intended to show that the method can be useful for studying a system that is in an evolutionary phase in terms of demand growth
We proposed a modelling of the user behaviour of a bike-sharing system (BSS). is behaviour concerns users who do not find an available bicycle in the desired station and can move to the nearest nonempty station to pick up a bicycle
Summary
Most medium and large cities have installed a bike-sharing system (BSS) since the first appearance of this type of system in Amsterdam in 1965. ese systems are part of sustainable development in urban areas. ey have experienced a strong growth in the last 15 years [1], with four generations of systems following one another [2]. e study of these systems has given rise to numerous works in the different scientific communities [3] which the main themes are the following: factors & barrier, system optimization, behaviour & impact, safety & health, and sharing economy [4]. sometimes managed by private operators, these systems constitute a public transport service [5]. eir objectives are to reduce congestion, gas emissions, and noise and to offer a flexible and cheap means of transport while having a beneficial effect on the health of users [6]. ey allow users to make relatively short journeys, of the order of 2.5 km [7] while integrating with other modes of transport in the intermodality context [8]. (i) e BSSs with dockings, in this case, the stations have dockings (third generation of BSS) to which the bicycles are hooked up. Is paper focuses on systems containing stations with dockings, which are the most widespread category, in Europe [13] and worldwide. Is unavailability of bicycles and dockings degrades the quality of service of these systems. For this reason, some operators deploy fleets of rebalancing vehicles that pick up bicycles from full stations and bring them to empty stations [16]. Other operators implement pricing incentive policies to encourage users to participate in rebalancing [19]
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