https://www.andrew.cmu.edu/user/vanhoeve/papers/bike_sharing.pdf

Inventory rebalancing and vehicle routing in bike sharing systems

Given the growing importance of bike-sharing systems nowadays, in this paper we suggest an alternative approach to mitigate the most crucial problem related to them: the imbalance of bicycles between zones owing to one-way trips. In particular, we focus on the emerging free-floating systems, where bikes can be delivered or picked-up almost everywhere in the network and not just at dedicated docking stations. We propose a new comprehensive dynamic bike redistribution methodology that starts from the prediction of the number and position of bikes over a system operating area and ends with a relocation Decision Support System. The relocation process is activated at constant gap times in order to carry out dynamic bike redistribution, mainly aimed at achieving a high degree of user satisfaction and keeping the vehicle repositioning costs as low as possible. An application to a test case study, together with a detailed sensitivity analysis, shows the effectiveness of the suggested novel methodology for the real-time management of the free-floating bike-sharing systems.

/pdf/a-modeling-framework-for-the-dynamic-management-of-free-1lmdalobxi.pdf

A modeling framework for the dynamic management of free-floating bike-sharing systems

The promotion of sustainable alternatives to motorized individual mobility has been seen in the past few decades as one of the cornerstones in a strategy to reduce the negative externalities related to the transportation sector. Bicycle sharing is increasingly popular as a sustainable transport system and the number of bike sharing schemes has grown significantly worldwide in recent years. One of the most important elements in implementation of these systems is the location of the stations. In fact the non-optimal locating of bike sharing compromises its success. Municipalities or public–private partnerships are mostly responsible for implementing bike-sharing schemes. The public investment in bicycle mobility (particularly bike-sharing) is complex because it is always subject to a budget. The main concern for public investment is to maximize the benefits through the design and implementation of bike-sharing systems. This work sets out a methodology to help with the decision-making of bike-sharing systems. The research work we present proposes using an optimization method to design the bike sharing system such that it maximizes the demand covered and takes the available budget as a constraint. It combines strategic decisions for locating bike-sharing stations and defining the dimension of the system (stations and number of bicycles) with operational decisions (relocating bicycles). As an outcome, the model determines the optimal location of the bicycle stations, the fleet size, the capacity of the stations and the number of bicycles in each station, considering an initial investment lower than the given budget. In addition, it balances the annual cost of the system and the revenue assuming a possible supplementary budget from the system provider to cover any loss resulting from the shortfall between its operating cost and the revenue from the subscription charges. A case study in Coimbra, Portugal, is presented and discussed.

Bike-sharing stations: A maximal covering location approach

In this paper, we study the static bike repositioning problem where the problem consists of selecting a subset of stations to visit, sequencing them, and determining the pick-up/drop-off quantities (associated with each of the visited stations) under the various operational constraints. The objective is to minimize the total penalties incurred at all the stations. We present an iterated tabu search heuristic to solve the described problem. Experimental results show that this simple heuristic can generate high quality solutions using small computing times.

/pdf/solving-a-static-repositioning-problem-in-bike-sharing-34v3it9kvg.pdf

Solving a static repositioning problem in bike-sharing systems using iterated tabu search

Bike-sharing systems are becoming increasingly prevalent in urban environments. They provide a low-cost, environmentally-friendly transportation alternative for cities. The management of these systems gives rise to many optimization problems. Chief among these problems is the issue of bicycle rebalancing. Users imbalance the system by creating demand in an asymmetric pattern. This necessitates action to put the system back in balance with the requisite levels of bicycles at each station to facilitate future use. In this paper, we tackle the problem of maintaing system balance during peak rush-hour usage as well as rebalancing overnight to prepare the system for rush-hour usage. We provide novel problem formulations that have been motivated by both a close collaboration with the New York City bike share (Citibike) and a careful analysis of system usage data. We analyze system data to discover the best placement of bikes to facilitate usage. We solve routing problems for overnight shifts as well as clustering problems for handling mid rush-hour usage. The tools developed from this research are currently in daily use at NYC Bike Share LLC, operators of Citibike.

https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9698/9314

Data analysis and optimization for (citi)bike sharing

/pdf/an-optimisation-algorithm-to-establish-the-location-of-155z7d7x73.pdf

Francisco Cruz

Papers

An Optimisation Algorithm to Establish the Location of Stations of a Mixed Fleet Biking System: An Application to the City of Lisbon