Free-floating bike sharing: Solving real-life large-scale static rebalancing problems

Free-floating bike sharing (FFBS) is an innovative bike sharing model. FFBS saves on start-up cost, in comparison to station-based bike sharing (SBBS), by avoiding construction of expensive docking stations and kiosk machines. FFBS prevents bike theft and offers significant opportunities for smart management by tracking bikes in real-time with built-in GPS. However, like SBBS, the success of FFBS depends on the efficiency of its rebalancing operations to serve the maximal demand as possible.Bicycle rebalancing refers to the reestablishment of the number of bikes at sites to desired quantities by using a fleet of vehicles transporting the bicycles. Static rebalancing for SBBS is a challenging combinatorial optimization problem. FFBS takes it a step further, with an increase in the scale of the problem. This article is the first effort in a series of studies of FFBS planning and management, tackling static rebalancing with single and multiple vehicles. We present a Novel Mixed Integer Linear Program for solving the Static Complete Rebalancing Problem. The proposed formulation, can not only handle single as well as multiple vehicles, but also allows for multiple visits to a node by the same vehicle. We present a hybrid nested large neighborhood search with variable neighborhood descent algorithm, which is both effective and efficient in solving static complete rebalancing problems for large-scale bike sharing programs.Computational experiments were carried out on the 1 Commodity Pickup and Delivery Traveling Salesman Problem (1-PDTSP) instances used previously in the literature and on three new sets of instances, two (one real-life and one general) based on Share-A-Bull Bikes (SABB) FFBS program recently launched at the Tampa campus of University of South Florida and the other based on Divvy SBBS in Chicago. Computational experiments on the 1-PDTSP instances demonstrate that the proposed algorithm outperforms a tabu search algorithm and is highly competitive with exact algorithms previously reported in the literature for solving static rebalancing problems in SBSS. Computational experiments on the SABB and Divvy instances, demonstrate that the proposed algorithm is able to deal with the increase in scale of the static rebalancing problem pertaining to both FFBS and SBBS, while deriving high-quality solutions in a reasonable amount of CPU time.     

Modeling the impact of parking price policy on free-floating carsharing: Case study for Zurich,Switzerland

The research on carsharing has already shown that a non-negligible part of carsharing members give up a vehicle after joining a carsharing program, or avoid a vehicle purchase. This arguably reduces overall parking space needed. This might well be one of the most important impacts of a carsharing program on the transportation system, but also one of the least researched. The rapid diffusion of free-floating carsharing, which for its very nature might have a stronger impact on parking, makes the relationship between carsharing and parking an appealing topic for new research. This work presents a method for the investigation of this relationship using an agent-based simulation and explores the impacts of different parking prices on the demand for free-floating carsharing in the city of Zurich, Switzerland. Three levels of free-floating fleet-size in the city of Zurich coupled with three levels of parking prices were simulated. The obtained results show that free-floating vehicles are able to use parking spaces more efficiently than private vehicles. Moreover, the average parking occupancy tends to be more homogeneous with higher fleet-size of free-floating carsharing and with the increase of parking prices, thus avoiding spatial parking pressure peaks.     

Measuring the car ownership impact of free-floating car-sharing – A case study in Basel,Switzerland

Free-floating car-sharing schemes operate without fixed car-sharing stations, ahead reservations or return-trip requirements. Providing fast and convenient motorization, they attract both public transport users and (former) car-owners. Thus, their impact on individual travel behavior depends on the user type. Estimating the travel behavior impact of these systems therefore requires quantitative data. Using a two-wave survey approach (shortly after launch of the scheme plus one year later) including travel diaries, this research indicates that (due to their membership) 6% of the free-floating car-sharing customers reduce their private vehicle ownership. Moreover, the results suggest that free-floating car-sharing both complements and competes with station-based car-sharing.     

A static free-floating bike repositioning problem with multiple heterogeneous vehicles,multiple depots,and multiple visits

In this paper, a bike repositioning problem with multiple depots, multiple visits, and multiple heterogeneous vehicles for the free-floating bike-sharing system (FFBSS) is studied. Two types of nodes (i.e., easily and hardly access nodes) with different penalties are defined to represent different convenience levels of getting bikes from the FFBSS. The objective of the repositioning is to minimize the weighted sum of the inconvenience level of getting bikes from the system and the total unmet demand and the total operational time. To solve this problem, an enhanced version of chemical reaction optimization (CRO) is developed. A loading and unloading quantity adjustment procedure with the consideration of the node characteristics, including the type of node and its current state (i.e., in a balanced, surplus, or deficit state) is proposed and incorporated into this version to improve its solution quality. A concept of the nearby-node set is also proposed to narrow the search space. Numerical results are presented and indicate that compared to the traditional CRO and CPLEX, the enhanced CRO improves solution quality and has potential to tackle the repositioning problem for larger, longer repositioning duration, and more vehicle instances. The results also demonstrate the effectiveness of the proposed adjustment procedure.     

Empirical analysis of free-floating carsharing usage: The Munich and Berlin case

Carsharing has become an important addition to existing mobility services over the last years. Today, several different systems are operating in many big cities. For an efficient and economic operation of any carsharing system, the identification of customer demand is essential. This demand is investigated within the presented research by analyzing booking data of a German free-floating carsharing system.The objectives of this paper are to describe carsharing usage and to identify factors that have an influence on the demand for carsharing. Therefore, the booking data are analyzed for temporal aspects, showing recurring patterns of varying lengths. The spatial distribution of bookings is investigated using a geographic information system and indicates a relationship between city structure and areas with high demand for carsharing. The temporal and spatial facets are then combined by applying a cluster analysis to identify groups of days with similar spatial booking patterns and show asymmetries in the spatiotemporal distribution of vehicle supply and demand.Influences on demand can be either short-term or long-term. The paper shows that changes in the weather conditions are a short-term influence as users of free-floating carsharing react to those. Furthermore, the application of a linear regression analysis reveals that socio-demographic data are suitable for making long-term demand predictions since booking numbers show quite a strong correlation with socio-demography, even in a simple model.