Car Sharing and Relocation Strategies: a Case Study Comparison in the Italian Market

The sharing economy represents an economic model based on the sharing of goods and services. In particular, this paper examines car sharing model, an attractive alternative to a self-owned car which has found large interest in the recent literature in different research fields. This study aims to investigate innovative and effective relocation strategies based on the analysis of data on users’ consumptions, for the constantly growing car sharing system. For this purpose, after a literature review, the paper presents a case study focused on the car repositioning algorithm developed by one of the market leader in this sector: car2go. More in detail, the paper evaluates differences and similarities in the strategic management of this model within the Italian context, through a comparison among the cities of Rome and Milan. Empirical results and practical implications for users will be provided, by highlighting opportunities and threats concerning the different settings

CASSI: Designing a Simulation Environment for Vehicle Relocation in Carsharing

Simulations offer an efficient solution to comprehensive represent operational services and to track the impact of changing systematic factors and business constraints. Carsharing services provide users with mobility services on demand. Although research has introduced strategies to optimize efforts to set up and operate such a system, they lack reusable and flexible simulation environments. For instance, carsharing research applies simulations to better understand and solve the problem of balancing vehicle supply and demand, which operators need to solve to prevent operational inefficiencies and ensure customer satisfaction. Hence, one cannot feasibly test new balancing mechanisms directly in a real-world environment. As for now, researchers have implemented simulations from scratch, which results in high development efforts and a limited ability to compare results. In this paper, we address this gap by designing a versatile carsharing simulation tool that researchers can easily use and adapt. The tool simplifies the process of modeling a carsharing system and developing operation strategies. Furthermore, we propose various system performance measures to increase the developed solutions’ comparability

Increasing the Business Value Of Free-Floating Carsharing Fleets By Applying Machine-Learning Based Relocations

Free-floating carsharing (CS) services provide customers with a fleet of vehicles distributed within an operation area. These services gained popularity because of their positive impact on societal and personal mobility. Understanding determinants of customer demand is a key challenge for developing and applying vehicle relocation strategies to prevent the formation of undersupply areas. In this study, we merge possible features from publicly available data sources with historical demand from CS services situated in three different-sized cities. We train and test a Random Forest (RF) regressor estimating demand based on the enhanced dataset. Building on this demand prediction, we developed a relocation strategy that optimizes vehicle availability at anticipated demand points. Our strategy improved the reservation acceptance ratio in all three reference systems between 7.1 % and 15.6 %. Furthermore, the number of relocations compared to a deterministic relocation strategy could be reduced by 82.3 % and 20.6 % in two cities

Generating Rental Data for Car Sharing Relocation Simulations on the Example of Station-Based One-Way Car Sharing

Developing sophisticated car sharing simulations is a major task to improve car sharing as a sustainable means of transportation, because new \ algorithms for enhancing car sharing efficiency are formulated using them. \ \ Simulations rely on input data, which is often gathered in car sharing systems or artificially generated. Real-world data is often incomplete and biased while artificial data is mostly generated based on initial assumptions. Therefore, developing new ways for generating testing data is an important task for future research. \ \ In this paper, we propose a new approach for generating car sharing data for relocation simulations by utilizing machine learning. Based on real-world data, we could show that a combined methods approach consisting of a Gaussian Mixture Model and two classification trees can generate appropriate artificial testing data

Computing Incentives for User-Based Relocation in Carsharing

Carsharing offers an environmentally friendly alternative to private car ownership. However, carsharing providers face the challenging task of matching shifting vehicle supply with fluctuating customer demand to prevent related operational inefficiencies and ensure customer satisfaction. To date, researchers have improved existing relocation strategies and developed new concepts with the use of information technology tools. Still, current literature lacks research on optimization and implementation of user-based relocation solutions. The most urgent need currently lies in the development of algorithms to compute and implement effective incentives for user-based relocation. We address these needs by utilizing a design science research approach to develop an automated machine learning-based incentive computation solution for incentivizing user-based relocation. We use a survey of 274 participants resulting in 1370 individual data points to train an incentive computation model, which is then applied within a small-scale field test. Results suggest that the algorithm computes appropriate incentives

Toward User-Based Relocation Information Systems in Station-Based One-Way Car Sharing

Car sharing is an important mobility service to approach urban and suburban mobility problems. It is a sustainable means of transport because it lowers the emissions of each car sharing customer by substituting privately owned vehicles by joint used ones. Nevertheless, car sharing still has to fully unfold its potential and has to overcome certain obstacles. For instance, it is necessary to substitute operator-based relocation by user-based relocation, which is more sustainable and cost-efficient. In this paper, we propose a framework and algorithms for implementing user-based relocation in the context of station-based one-way car sharing. Through a simulated car sharing system with 34.418 rental data we were able to demonstrate, that user-based relocation has the potential to increase the number of accepted rentals. Implementing the proposed system would increase service quality, providers’ profits and the positive environmental impact of car sharing

Electric Revolution and Free Floating Car Sharing: A Data Driven Methodology for System Design

Nowadays, the increase in traffic congestions, land consumption, and pollution emission due to private car ownership makes the rise of shared mobility possible. One of the most spread implementations of shared mobility is Free Floating Car Sharing (FFCS). It is a car rental model where the users can pick and release the car everywhere within an operative area. The customers can reserve (and return) the vehicle using a web-based application. With just a simple tap, the users can unlock and lock the smart vehicle. Usually, the provider bills the users only for the time spend driving, with time-minute based fares. All the other costs, like petrol, insurance, and maintenance, are in charge of the provider. This service’s flexibility fills the urban mobility gap between public transport’s relative cheapness and the comfort and capillarity of private car ownership. Indeed, FFCS allows people to travel and commute faster than the standard public bus but avoiding all the fixed and variable costs related to private car ownership. Given the recent electric cars market increase and all the benefits those vehicles carry, replacing FFCS fleet with electric-powered cars may still improve urban centers’ quality of life. The setup and management of an electric FFCS require ingenuity to minimize the users’ discomfort due to car plugging procedures. In my thesis, I present a methodology to address, in different cases of studies, all the challenges related to the conversion of combustion engine cars to electric vehicles in FFCS. In particular, my research’s main driver is to propose a methodology to build a profitable and technically sustainable system setup, able to guarantee a flexible and appealing mobility service to an increasing customer audience. In the first part of my thesis, I describe the software I developed to scrape from the web real combustion engine FFCS, from two providers: car2go and Enjoy. The car2go data collection lasted from December 2016 to January 2018, collecting more than 27 million users’ bookings spread in 23 cities. The Enjoy data collection phase started in May 2017 and lasted until June 2019, recording about 6 million bookings in 6 cities. Then, I characterize both datasets in Turin, one of the cities in which both FFCS providers work. I detect the outliers, filter them out from the dataset, and extract geotemporal users’ travel patterns. After that, I compare the car2go customer’s pattern with the one-way and two-way car-sharing system. The results show how users prefer more flexible services like FFCS or one-way car sharing. Once the data are consolidated, I develop: A methodology to place a charging station in a city by looking at users’ patterns. System policies to manage the fleet when the vehicle state of charge may not guarantee a trip. Via an event-based trace-driven simulator able to replicate the recorded trips in an electrified scenario evaluating each configuration’s feasibility. Via accurate simulation in Berlin, Milan, Turin, and Vancouver, I study different electric FFCS setup. By placing the charging station in the most frequented areas, by offering an incentive to the users to plug the car when the battery state of charge is below a safety threshold, and balancing the spread of poles, it is possible to obtain a sustainable system covering with charging station only the 8-10 % of zones. To reduce the number of charging stations to have a sustainable electric FFCS, I compare several optimization algorithms. The results show how a Genetic Algorithm can find a better solution to shrink the minimum amount of resources to sustain the same mobility demand. After that, I move my attention to the users’ rentals’ demand predictability. The main goal is to understand how different open-data sources could impact the recorded FFCS users’ rental. Initially, I compare several time-series forecasts to predict the users’ demand in the short and medium-term. Random Forest regression produces better accuracy and results in terms of interpretability. Then I correlate the socio-economics features characterizing each city neighborhood to FFCS demand, and again, the Random Forest regression outperforms other algorithms. Finally, I question the system scalability figuring out several scenarios having increasing demand. I use a model to synthesize users’ demand by looking only at the geospatial users’ rentals. By varying the electric FFCS setup and simulating the new scenario, I point out how a linear increase in the demand intensity requires a fleet sublinear increase. Finally, I project those considerations in euros, proofing how electric FFCS has room for economic growth